AI Image Generators

Explore the best AI Image Generators — independent reviews, comparisons, pricing and step-by-step how-to guides, curated by Aizhi.

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  Competition in artificial intelligence

  Competition in artificial intelligence refers to the rivalry among companies, research institutions, and governments to develop and deploy the most capable artificial intelligence (AI) systems. The competition spans multiple domains, including large language models (LLMs), autonomous vehicles, robotics, computer vision systems, natural language processing (NLP), and AI-optimized hardware. == Background == Competition in AI is driven by potential economic, strategic, and scientific advantages. Breakthroughs in AI can enhance productivity, enable new products and services, and provide geopolitical leverage. The field has experienced rapid progress since the mid-2010s, particularly in machine learning and artificial neural networks, leading to intense rivalry among leading actors. == Corporate competition == Major technology companies are among the most visible competitors in AI. In the United States, firms such as OpenAI, Google DeepMind, Meta Platforms, Microsoft, Anthropic, and Nvidia compete in building advanced LLMs, generative AI platforms, and AI-optimized graphics processing units (GPUs). In China, companies such as Baidu, Alibaba Group, Tencent, and startups such DeepSeek have become leaders in AI deployment, often with state backing. The "[war for talent]" in AI research has become a defining feature of corporate competition. Leading firms often recruit top AI researchers from rivals, sometimes offering multi-million-dollar compensation packages. == National competition == Governments see leadership in AI as a strategic priority. The United States has funded AI research for military, economic, and societal applications, while China has set a target to lead the world in AI by 2030 through its "New Generation Artificial Intelligence Development Plan". Other nations, including the UK, India, Israel, Russia, South Korea, and members of the European Union, have launched national AI strategies. In February 2026 Anthropic said Chinese companies - DeepSeek, Moonshot AI, and MiniMax - were conducting "distillation attacks" in an attempt to copy their model's capabilities, and warned that business wars were closely tied to geopolitical ones: "foreign labs that illicitly distill American models can remove safeguards, feeding model capabilities into their own military, intelligence, and surveillance systems." == Sectors of competition == === Large language models and chatbots competition === Competition to produce the most capable generative text models, with benchmarks such as MMLU and ARC used to evaluate performance has been on scale since the emergence of AI. These systems leverage deep learning, especially transformer architectures, to understand and generate human-like language. Companies and research groups globally compete to develop chatbots that are more capable, reliable, and context-aware. Among the most well-known chatbots is ChatGPT, developed by OpenAI. Since its public release in 2022, ChatGPT has rapidly gained widespread attention for its ability to engage in coherent and versatile conversations, assist with creative writing, and solve complex problems. In response, technology firms introduced competing chatbots aiming to challenge or surpass ChatGPT's capabilities. Notably, DeepSeek, a Chinese AI company, launched an advanced chatbot integrated with their R1 language model, emphasizing strong natural language understanding and multilingual support. Similarly, Grok, developed by xAI (company), integrates conversational AI into vehicles and digital assistants, combining natural language processing with real-time data for personalized user interaction. These chatbots not only compete in language tasks but also demonstrate strategic reasoning capabilities by playing complex games such as chess and Go. This form of competition is reminiscent of historic AI milestones set by programs such as Deep Blue and AlphaGo. The OpenAI’s ChatGPT has been tested in playing chess at various levels, while DeepSeek’s chatbot showcased its prowess in online chess tournaments in early 2024, winning several matches against human and AI opponents. Grok, leveraging Tesla's vast data infrastructure, has demonstrated real-time strategic decision-making in simulation environments that include chess-like games. The competition pushes rapid innovation, with firms racing to improve chatbot conversational depth, reduce biases, increase factual accuracy, and integrate multimodal inputs like images and videos. At the same time, the competition raises questions about AI safety, ethical use, and the societal impacts of increasingly human-like chatbots. === Autonomous vehicles === Companies such as Waymo, Tesla, and Baidu are racing to deploy safe and reliable self-driving car technology. === AI chips === Rivalry between Nvidia, AMD, Intel, and Huawei in designing processors optimized for AI workloads. === Military applications === Development of AI-enabled drones, surveillance systems, and decision-support tools, with associated ethical debates. == Events == In 2023, OpenAI released GPT-4, prompting competitors such as Google DeepMind to accelerate the release of their own models, including Gemini. In 2024, Chinese AI company DeepSeek launched the R1 model, leading OpenAI to release an open-source system, GPT-OSS, as a strategic countermeasure. In 2022, Tesla and Waymo both expanded autonomous taxi services in U.S. cities, competing for regulatory approval and public trust. The U.S. Department of Defense's Project Maven and China's AI-enabled surveillance programs have been cited as examples of military AI rivalry. In 2025, Microsoft hired several senior engineers from Google DeepMind, highlighting the ongoing "talent poaching" competition in the AI sector. == Risks and concerns == Critics warn that unrestrained competition in AI can undermine safety, ethics, and governance. Concerns include the proliferation of biased or unsafe models, escalation in autonomous weapons, and reduced cooperation on safety standards.

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  Client honeypot

  Honeypots are security devices whose value lie in being probed and compromised. Traditional honeypots are servers (or devices that expose server services) that wait passively to be attacked. Client Honeypots are active security devices in search of malicious servers that attack clients. The client honeypot poses as a client and interacts with the server to examine whether an attack has occurred. Often the focus of client honeypots is on web browsers, but any client that interacts with servers can be part of a client honeypot (for example ftp, email, ssh, etc.). There are several terms that are used to describe client honeypots. Besides client honeypot, which is the generic classification, honeyclient is the other term that is generally used and accepted. However, there is a subtlety here, as "honeyclient" is actually a homograph that could also refer to the first known open source client honeypot implementation (see below), although this should be clear from the context. == Architecture == A client honeypot is composed of three components. The first component, a queuer, is responsible for creating a list of servers for the client to visit. This list can be created, for example, through crawling. The second component is the client itself, which is able to make a requests to servers identified by the queuer. After the interaction with the server has taken place, the third component, an analysis engine, is responsible for determining whether an attack has taken place on the client honeypot. In addition to these components, client honeypots are usually equipped with some sort of containment strategy to prevent successful attacks from spreading beyond the client honeypot. This is usually achieved through the use of firewalls and virtual machine sandboxes. Analogous to traditional server honeypots, client honeypots are mainly classified by their interaction level: high or low; which denotes the level of functional interaction the server can utilize on the client honeypot. In addition to this there are also newly hybrid approaches which denotes the usage of both high and low interaction detection techniques. == High interaction == High interaction client honeypots are fully functional systems comparable to real systems with real clients. As such, no functional limitations (besides the containment strategy) exist on high interaction client honeypots. Attacks on high interaction client honeypots are detected via inspection of the state of the system after a server has been interacted with. The detection of changes to the client honeypot may indicate the occurrence of an attack against that has exploited a vulnerability of the client. An example of such a change is the presence of a new or altered file. High interaction client honeypots are very effective at detecting unknown attacks on clients. However, the tradeoff for this accuracy is a performance hit from the amount of system state that has to be monitored to make an attack assessment. Also, this detection mechanism is prone to various forms of evasion by the exploit. For example, an attack could delay the exploit from immediately triggering (time bombs) or could trigger upon a particular set of conditions or actions (logic bombs). Since no immediate, detectable state change occurred, the client honeypot is likely to incorrectly classify the server as safe even though it did successfully perform its attack on the client. Finally, if the client honeypots are running in virtual machines, then an exploit may try to detect the presence of the virtual environment and cease from triggering or behave differently. === Capture-HPC === Capture [1] is a high interaction client honeypot developed by researchers at Victoria University of Wellington, NZ. Capture differs from existing client honeypots in various ways. First, it is designed to be fast. State changes are being detected using an event based model allowing to react to state changes as they occur. Second, Capture is designed to be scalable. A central Capture server is able to control numerous clients across a network. Third, Capture is supposed to be a framework that allows to utilize different clients. The initial version of Capture supports Internet Explorer, but the current version supports all major browsers (Internet Explorer, Firefox, Opera, Safari) as well as other HTTP aware client applications, such as office applications and media players. === HoneyClient === HoneyClient [2] is a web browser based (IE/FireFox) high interaction client honeypot designed by Kathy Wang in 2004 and subsequently developed at MITRE. It was the first open source client honeypot and is a mix of Perl, C++, and Ruby. HoneyClient is state-based and detects attacks on Windows clients by monitoring files, process events, and registry entries. It has integrated the Capture-HPC real-time integrity checker to perform this detection. HoneyClient also contains a crawler, so it can be seeded with a list of initial URLs from which to start and can then continue to traverse web sites in search of client-side malware. === HoneyMonkey (dead since 2010) === HoneyMonkey [3] is a web browser based (IE) high interaction client honeypot implemented by Microsoft in 2005. It is not available for download. HoneyMonkey is state based and detects attacks on clients by monitoring files, registry, and processes. A unique characteristic of HoneyMonkey is its layered approach to interacting with servers in order to identify zero-day exploits. HoneyMonkey initially crawls the web with a vulnerable configuration. Once an attack has been identified, the server is reexamined with a fully patched configuration. If the attack is still detected, one can conclude that the attack utilizes an exploit for which no patch has been publicly released yet and therefore is quite dangerous. === SHELIA (dead since 2009) === Shelia [4] is a high interaction client honeypot developed by Joan Robert Rocaspana at Vrije Universiteit Amsterdam. It integrates with an email reader and processes each email it receives (URLs & attachments). Depending on the type of URL or attachment received, it opens a different client application (e.g. browser, office application, etc.) It monitors whether executable instructions are executed in data area of memory (which would indicate a buffer overflow exploit has been triggered). With such an approach, SHELIA is not only able to detect exploits, but is able to actually ward off exploits from triggering. === UW Spycrawler === The Spycrawler [5] developed at the University of Washington is yet another browser based (Mozilla) high interaction client honeypot developed by Moshchuk et al. in 2005. This client honeypot is not available for download. The Spycrawler is state based and detects attacks on clients by monitoring files, processes, registry, and browser crashes. Spycrawlers detection mechanism is event based. Further, it increases the passage of time of the virtual machine the Spycrawler is operating in to overcome (or rather reduce the impact of) time bombs. === Web Exploit Finder === WEF [6] is an implementation of an automatic drive-by-download – detection in a virtualized environment, developed by Thomas Müller, Benjamin Mack and Mehmet Arziman, three students from the Hochschule der Medien (HdM), Stuttgart during the summer term in 2006. WEF can be used as an active HoneyNet with a complete virtualization architecture underneath for rollbacks of compromised virtualized machines. == Low interaction == Low interaction client honeypots differ from high interaction client honeypots in that they do not utilize an entire real system, but rather use lightweight or simulated clients to interact with the server. (in the browser world, they are similar to web crawlers). Responses from servers are examined directly to assess whether an attack has taken place. This could be done, for example, by examining the response for the presence of malicious strings. Low interaction client honeypots are easier to deploy and operate than high interaction client honeypots and also perform better. However, they are likely to have a lower detection rate since attacks have to be known to the client honeypot in order for it to detect them; new attacks are likely to go unnoticed. They also suffer from the problem of evasion by exploits, which may be exacerbated due to their simplicity, thus making it easier for an exploit to detect the presence of the client honeypot. === HoneyC === HoneyC [7] is a low interaction client honeypot developed at Victoria University of Wellington by Christian Seifert in 2006. HoneyC is a platform independent open source framework written in Ruby. It currently concentrates driving a web browser simulator to interact with servers. Malicious servers are detected by statically examining the web server's response for malicious strings through the usage of Snort signatures. === Monkey-Spider (dead since 2008) === Monkey-Spider [8] is a low-interaction client honeypot i

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  UpScrolled

  UpScrolled is an Australian social media platform for microblogging and short-form online video sharing that was launched in June 2025 by Recursive Methods Pty Ltd. It was founded by Issam Hijazi. == History == UpScrolled was launched in June 2025 by Recursive Methods Pty Ltd. It was founded by Issam Hijazi, a Palestinian-Australian app developer. UpScrolled is backed by the Tech for Palestine incubator. In January 2026, UpScrolled saw increased attention and number of downloads after the acquisition of TikTok by a group of pro-Donald Trump US investors, including Larry Ellison, which led to calls to boycott TikTok and migrate to other apps. TikTok was alleged to be suppressing pro-Palestinian content, as well as news surrounding the killing of Alex Pretti in Minneapolis on the platform. UpScrolled subsequently climbed to the top 10 of Apple's App Store list of free apps. The app saw a reported 2,850% increase in downloads between 22 and 24 January 2026. As of 27 January 2026, UpScrolled "had been downloaded about 400,000 times in the US and 700,000 globally since launching in June 2025". The app became the most downloaded app in the Apple App store on 29 January 2026, following allegations that TikTok was suppressing videos and content opposed to Immigration and Customs Enforcement (ICE) under its new ownership. By 2 February 2026, UpScrolled had reached 2.5 million users. According to the Google Play Store and the Apple App Store, it has become the most downloaded social media app in the United States and Canada, with rising interest in the United Kingdom, France, Germany and Italy. On 14 February, UpScrolled was suspended from the Google Play Store; the suspension was reverted by 15 February. == Founder == Hijazi was born in Jordan. His parents and grandparents are from Safad, a northern Israeli city near the Lebanese border. He worked for IBM and Oracle prior to starting UpScrolled. Hijazi told Rest of World that he launched UpScrolled in response to Israel's genocide in Gaza which followed the October 7 attacks. He said, "I couldn't take it anymore. I lost family members in Gaza, and I didn't want to be complicit. So I was like, I'm done with this, I want to feel useful. I found this gap in the market, with a lot of people asking why there is no alternative to the Big Tech platforms for their content, which was getting censored." Hijazi also alleges that social media accounts that were posting pro-Palestinian content were getting shadow banned on larger platforms, and alleges that even his account was not exempt from being targeted by censors. Hijazi has further elaborated on the importance of social media independence to further the Palestinian cause. In January 2026, Web Summit Qatar announced that Hijazi would be an opening night speaker. Following the announcement, there was a surge in ticket sales for the summit. Hijazi lives in Sydney with his wife and daughter. He lost 60 family members during the Gaza war. == Features == UpScrolled's algorithm allows users to discover posts based on likes, comments, and shares with time decay and some randomness, all chronologically, with "no manipulation" according to the app's website. UpScrolled has an interface resembling a mix of Instagram and Twitter, allowing users to post and view text posts, photos, and videos. It also lets users send private messages to each other. The app is currently available for iOS and Android devices, with plans to upscale. UpScrolled does not include Israel as an option in its location selection menu. Cities such as Tel Aviv are included under "Occupied Territories of Palestine", and Palestine can also be set as the location. UpScrolled says that it is against censorship and shadow banning, and describes itself as "belong[ing] to the people who use it — not to hidden algorithms or outside agendas". Hijazi said, "The other platforms claim to be free speech platforms. But when it comes to anything on Palestine, that's a different story." UpScrolled states that it "does not tolerate hate speech, propaganda, or bad-faith behaviour, but it also refuses to silence voices quietly or without explanation". == User base and content == Al Jazeera reported that posts expressing pro-Palestinian sentiment or depicting the continued suffering in the Gaza Strip were "flooding" the app. Political and global issues such as the Gaza war are prominent. Content includes updates from the Gaza Freedom Flotilla, posts by doctors working in Gaza, video essays about Palantir’s influence within the military and calls for boycotts of Israel. It has been used by Gazans to crowdfund and record daily life. Celebrity users of UpScrolled include American labour activist Chris Smalls and actor Jacob Berger, both of whom were on the July 2025 Gaza Freedom Flotilla. Political figures have also joined UpScrolled, such as South African politician and Economic Freedom Fighters leader Julius Malema, and Islamic Revolutionary Guard Corps commander Esmail Qaani. One user said that most early users were attracted to the platform for the opportunity to criticize Zionism. The Jewish Telegraphic Agency (JTA) reported that UpScrolled was observed to be "flooded" with antisemitic and anti-Israel content, including Holocaust denial and accusations that Israel carried out the 9/11 attacks. In a statement, UpScrolled said, "Our content moderation hasn't been able to keep up with the massive rise of users this week. We're working with digital rights experts to grow our Trust & Safety team and are beefing up our content moderation to prevent this. We apologise to all impacted users, thank you for being part of Upscrolled." The Times reported in February 2026 that UpScrolled was hosting content that could potentially breach UK law, including antisemitic content and posts promoting Hamas, Hezbollah, Islamic State and Al-Qaeda, as well as footage of the 2019 Christchurch mosque shootings and content praising the perpetrators of the 2019 Halle synagogue shooting and 2018 Pittsburgh synagogue shooting. Antisemitic influencers Lucas Gage, Jake Shields, Stew Peters and Anastasia Maria Loupis have accounts on UpScrolled. UpScrolled’s policies prohibit threats, glorification of harm or support for terrorist or violent groups. Hijazi said harmful content was being uploaded to UpScrolled and the company had expanded its content moderation team and upgraded its technology infrastructure to deal with the issue. In May 2026, Moment magazine said that users had identified some antisemitic content, pornography and extremist videos on the platform. The magazine said there were gaps in content moderation due to the small size of the developer team. == Reception == In January 2026, the Council on American–Islamic Relations (CAIR) praised UpScrolled for "pledging to protect the free flow of ideas on its platform, including both support for and opposition to the Israeli government's human rights abuses." Guy Christensen, a pro-Palestinian social media celebrity, has encouraged his audience to download UpScrolled. Christensen characterized UpScrolled as having "no censorship, no ownership by billionaires who put their interests and biases onto you to control you". He compared the platform to others like TikTok, saying that Israel is behind censorship that wouldn't happen on UpScrolled. Jaigris Hodson, an associate professor of Interdisciplinary Studies at Royal Roads University in Canada, has argued that "Network effects mean that unless UpScrolled continues its explosive growth, people are unlikely to continue to choose it over the more established TikTok. At best, we might see a Twitter/X effect, which is where TikTok will host more pro-U.S. government content creators and those people who want to follow them, and UpScrolled will host more critical content creators and their followers."

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  Vx-underground

  vx-underground, also known as VXUG, is an educational website about malware and cybersecurity. It claims to have the largest online repository of malware. The site was launched in May, 2019 and has grown to host over 35 million pieces of malware samples. On their account on Twitter, VXUG reports on and verifies cybersecurity breaches. == Reception == Kim Crawley compared the site to VirusTotal and states that vx-underground is more susceptible to suspicion for law enforcement. == Data breach reports == In May 2024, the International Baccalaureate organizations faced allegations over supposed breaches in their IT infrastructure after an incident of examination leaks. Upon inspecting leaked data, VXUG were the first to report that the breach seemed legitimate on the morning of May 6.

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  Maximum inner-product search

  Maximum inner-product search (MIPS) is a search problem, with a corresponding class of search algorithms which attempt to maximise the inner product between a query and the data items to be retrieved. MIPS algorithms are used in a wide variety of big data applications, including recommendation algorithms and machine learning. Formally, for a database of vectors x i {\displaystyle x_{i}} defined over a set of labels S {\displaystyle S} in an inner product space with an inner product ⟨ ⋅ , ⋅ ⟩ {\displaystyle \langle \cdot ,\cdot \rangle } defined on it, MIPS search can be defined as the problem of determining a r g m a x i ∈ S ⟨ x i , q ⟩ {\displaystyle {\underset {i\in S}{\operatorname {arg\,max} }}\ \langle x_{i},q\rangle } for a given query q {\displaystyle q} . Although there is an obvious linear-time implementation, it is generally too slow to be used on practical problems. However, efficient algorithms exist to speed up MIPS search. Under the assumption of all vectors in the set having constant norm, MIPS can be viewed as equivalent to a nearest neighbor search (NNS) problem in which maximizing the inner product is equivalent to minimizing the corresponding distance metric in the NNS problem. Like other forms of NNS, MIPS algorithms may be approximate or exact. MIPS search is used as part of DeepMind's RETRO algorithm.

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  List of security-focused operating systems

  This is a list of operating systems specifically focused on security. Similar concepts include security-evaluated operating systems that have achieved certification from an auditing organization, and trusted operating systems that provide sufficient support for multilevel security and evidence of correctness to meet a particular set of requirements. == Linux == === Android-based === GrapheneOS is a security-focused, Android-based mobile OS that uses a hardened kernel, C library, custom memory allocator (hardened_malloc), and a hardened Chromium-based browser named Vanadium. It also offers privacy/security features, such as Duress PIN/Password or disabling the USB-C port at a driver/hardware level to avoid exploitation. It deploys exploit mitigations such as hardware-based memory tagging, secure app spawning, restricted dynamic code loading, and more. === Debian-based === Linux Kodachi is a security-focused operating system. Tails is aimed at preserving privacy and anonymity. KickSecure is a security-focused Linux distribution that aims to be "hardened by default". It uses network hardening, kernel hardening, Strong Linux User Account Isolation, better randomness, root access restrictions, and app-specific hardening. Whonix is an anonymity focused operating system based on KickSecure. It consists of two virtual machines, And all communications are routed through Tor. === Other Linux distributions === Alpine Linux is designed to be small, simple, and secure. It uses musl, BusyBox, and OpenRC instead of the more commonly used glibc, GNU Core Utilities, and systemd. Owl - Openwall GNU/Linux, a security-enhanced Linux distribution for servers. Secureblue, a Fedora Silverblue based distro that uses a hardened kernel, custom memory allocator (hardened_malloc), Trivalent, a security-focused, Chromium-based browser inspired by Vanadium, and many other exploit mitigations. == BSD == OpenBSD is a Unix-like operating system that emphasizes portability, standardization, correctness, proactive security, and integrated cryptography. == Xen == Qubes OS aims to provide security through isolation. Isolation is provided through the use of virtualization technology. This allows the segmentation of applications into secure virtual machines.

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  Insider threat

  An insider threat is a perceived threat to an organization that comes from people within the organization, such as employees, former employees, contractors or business associates, who have inside information concerning the organization's security practices, data and computer systems. The threat may involve fraud, the theft of confidential or commercially valuable information, the theft of intellectual property, or the sabotage of computer systems. == Overview == Insiders may have accounts giving them legitimate access to computer systems, with this access originally having been given to them to serve in the performance of their duties; these permissions could be abused to harm the organization. Insiders are often familiar with the organization's data and intellectual property as well as the methods that are in place to protect them. This makes it easier for the insider to circumvent any security controls of which they are aware. Physical proximity to data means that the insider does not need to hack into the organizational network through the outer perimeter by traversing firewalls; rather they are in the building already, often with direct access to the organization's internal network. Insider threats are harder to defend against than attacks from outsiders, since the insider already has legitimate access to the organization's information and assets. An insider may attempt to steal property or information for personal gain or to benefit another organization or country. The threat to the organization could also be through malicious software left running on its computer systems by former employees, a so-called logic bomb. == Research == Insider threat is an active area of research in academia and government. The CERT Coordination Center at Carnegie-Mellon University maintains the CERT Insider Threat Center, which includes a database of more than 850 cases of insider threats, including instances of fraud, theft and sabotage; the database is used for research and analysis. CERT's Insider Threat Team also maintains an informational blog to help organizations and businesses defend themselves against insider crime. The Threat Lab and Defense Personnel and Security Research Center (DOD PERSEREC) has also recently emerged as a national resource within the United States of America. The Threat Lab hosts an annual conference, the SBS Summit. They also maintain a website that contains resources from this conference. Complimenting these efforts, a companion podcast was created, Voices from the SBS Summit. In 2022, the Threat Lab created an interdisciplinary journal, Counter Insider Threat Research and Practice (CITRAP) which publishes research on insider threat detection. === Findings === In the 2022 Data Breach Investigations Report (DBIR), Verizon found that 82% of breaches involved the human element, noting that employees continue to play a leading role in cybersecurity incidents and breaches. According to the UK Information Commissioners Office, 90% of all breaches reported to them in 2019 were the result of mistakes made by end users. This was up from 61% and 87% over the previous two years. A 2018 whitepaper reported that 53% of companies surveyed had confirmed insider attacks against their organization in the previous 12 months, with 27% saying insider attacks have become more frequent. A report published in July 2012 on the insider threat in the U.S. financial sector gives some statistics on insider threat incidents: 80% of the malicious acts were committed at work during working hours; 81% of the perpetrators planned their actions beforehand; 33% of the perpetrators were described as "difficult" and 17% as being "disgruntled". The insider was identified in 74% of cases. Financial gain was a motive in 81% of cases, revenge in 23% of cases, and 27% of the people carrying out malicious acts were in financial difficulties at the time. The US Department of Defense Personnel Security Research Center published a report that describes approaches for detecting insider threats. Earlier it published ten case studies of insider attacks by information technology professionals. Cybersecurity experts believe that 38% of negligent insiders are victims of a phishing attack, whereby they receive an email that appears to come from a legitimate source such as a company. These emails normally contain malware in the form of hyperlinks. == Typologies and ontologies == Multiple classification systems and ontologies have been proposed to classify insider threats. Traditional models of insider threat identify three broad categories: Malicious insiders, which are people who take advantage of their access to inflict harm on an organization; Negligent insiders, which are people who make errors and disregard policies, which place their organizations at risk; and Infiltrators, who are external actors that obtain legitimate access credentials without authorization. == Criticisms == Insider threat research has been criticized. Critics have argued that insider threat is a poorly defined concept. Forensically investigating insider data theft is notoriously difficult, and requires novel techniques such as stochastic forensics. Data supporting insider threat is generally proprietary (i.e., encrypted data). Theoretical/conceptual models of insider threat are often based on loose interpretations of research in the behavioral and social sciences, using "deductive principles and intuitions of subject matter expert." Adopting sociotechnical approaches, researchers have also argued for the need to consider insider threat from the perspective of social systems. Jordan Schoenherr said that "surveillance requires an understanding of how sanctioning systems are framed, how employees will respond to surveillance, what workplace norms are deemed relevant, and what ‘deviance’ means, e.g., deviation for a justified organization norm or failure to conform to an organizational norm that conflicts with general social values." By treating all employees as potential insider threats, organizations might create conditions that lead to insider threats. == Sector-specific concerns == === Healthcare === The healthcare industry faces particularly acute insider threat risks due to the large number of workforce members who require access to sensitive patient records for legitimate clinical purposes. The U.S. Department of Health and Human Services has identified unauthorized access by insiders, including workforce snooping on patient records and theft of protected health information for identity fraud, as a persistent enforcement concern. The Health Insurance Portability and Accountability Act (HIPAA) Security Rule addresses insider threats through several administrative safeguards, including workforce security procedures requiring covered entities to implement policies for authorizing and supervising workforce members who work with electronic protected health information, as well as termination procedures to revoke access when employment ends (45 CFR 164.308(a)(3)). The rule also requires audit controls to record and examine information system activity (45 CFR 164.312(b)), enabling detection of unauthorized access by insiders. The December 2024 Notice of proposed rulemaking (NPRM) to overhaul the HIPAA Security Rule would strengthen insider threat defenses by mandating role-based access controls, requiring notification of relevant workforce members within 24 hours of any changes to access privileges, and requiring regular review of audit logs to detect anomalous access patterns.

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  List of monochrome and RGB color formats

  This list of monochrome and RGB palettes includes generic repertoires of colors (color palettes) to produce black-and-white and RGB color pictures by a computer's display hardware. RGB is the most common method to produce colors for displays; so these complete RGB color repertoires have every possible combination of R-G-B triplets within any given maximum number of levels per component. Each palette is represented by a series of color patches. When the number of colors is low, a 1-pixel-size version of the palette appears below it, for easily comparing relative palette sizes. Huge palettes are given directly in one-color-per-pixel color patches. For each unique palette, an image color test chart and sample image (truecolor original follows) rendered with that palette (without dithering) are given. The test chart shows the full 256 levels of the red, green, and blue (RGB) primary colors and cyan, magenta, and yellow complementary colors, along with a full 256-level grayscale. Gradients of RGB intermediate colors (orange, lime green, sea green, sky blue, violet, and fuchsia), and a full hue spectrum are also present. Color charts are not gamma corrected. These elements illustrate the color depth and distribution of the colors of any given palette, and the sample image indicates how the color selection of such palettes could represent real-life images. These images are not necessarily representative of how the image would be displayed on the original graphics hardware, as the hardware may have additional limitations regarding the maximum display resolution, pixel aspect ratio and color placement. Implementation of these formats is specific to each machine. Therefore, the number of colors that can be simultaneously displayed in a given text or graphic mode might be different. Also, the actual displayed colors are subject to the output format used - PAL or NTSC, composite or component video, etc. - and might be slightly different. For simulated images and specific hardware and alternate methods to produce colors other than RGB (ex: composite), see the List of 8-bit computer hardware palettes, the List of 16-bit computer hardware palettes and the List of video game console palettes. For various software arrangements and sorts of colors, including other possible full RGB arrangements within 8-bit color depth displays, see the List of software palettes. == Monochrome palettes == These palettes only have some shades of gray, from black to white (considered the darkest and lightest "grays", respectively). The general rule is that those palettes have 2n different shades of gray, where n is the number of bits needed to represent a single pixel. === Monochrome (1-bit grayscale) === Monochrome graphics displays typically have a black background with a white or light gray image, though green and amber monochrome monitors were also common. Such a palette requires only one bit per pixel. Where photo-realism was desired, these early computer systems had a heavy reliance on dithering to make up for the limits of the technology. In some systems, as Hercules and CGA graphic cards for the IBM PC, a bit value of 1 represents white pixels (light on) and a value of 0 the black ones (light off); others, like the Playdate and Atari ST and Apple Macintosh with monochrome monitors, a bit value of 0 means a white pixel (no ink) and a value of 1 means a black pixel (dot of ink), which it approximates to the printing logic. === 2-bit Grayscale === In a 2-bit color palette each pixel's value is represented by 2 bits resulting in a 4-value palette (22 = 4). 2-bit dithering: It has black, white and two intermediate levels of gray as follows: A monochrome 2-bit palette is used on: The Monochrome Display Adapter for the IBM PC NeXT Computer, NeXTcube and NeXTstation monochrome graphic displays. Original Game Boy system portable video game console. Macintosh PowerBook 150 monochrome LC displays. Amiga with A2024 monochrome monitor in high-resolution mode. The original Amazon Kindle The original WonderSwan The Tiger Electronics Game.com portable video game console The original Neo Geo Pocket. === 4-bit Grayscale === In a 4-bit color palette each pixel's value is represented by 4 bits resulting in a 16-value palette (24 = 16): 4-bit grayscale dithering does a fairly good job of reducing visible banding of the level changes: A monochrome 4-bit palette is used on: MOS Technology VDC (on the Commodore 128 with monochrome monitor) Amstrad CPC series with a GT64/GT65 Green Monitor (16 unique green shades) Amstrad CPC Plus series with the MM12 Monochrome monitor (16 shades of grey) Some Apple PowerBooks equipped with monochrome displays like the PowerBook 5300 The original VideoNow === 8-bit Grayscale === In an 8-bit color palette each pixel's value is represented by 8 bits resulting in a 256-value palette (28 = 256). This is usually the maximum number of grays in ordinary monochrome systems; each image pixel occupies a single memory byte. Most scanners can capture images in 8-bit grayscale, and image file formats like TIFF and JPEG natively support this monochrome palette size. Alpha channels employed for video overlay also use (conceptually) this palette. The gray level indicates the opacity of the blended image pixel over the background image pixel. == Dichrome palettes == === 16-bit RG palette === The RG or red–green color space is a color space that uses only two primary colors: red and green. It was used on early color processes for films. It was used as an additive format, similar to the RGB color model but without a blue channel, on processes such as Kinemacolor, Prizma, Technicolor I, Raycol, etc., producing shades of black, red, green and yellow. Alternatively, it was used as a subtractive format on Brewster Color I, Kodachrome I, Prizma II, Technicolor II, etc., producing shades of transparent, red, green and black. Until recently, its primary use was in low-cost light-emitting diode displays in which red and green tended to be far more common than the still nascent blue LED technology, but full-color LEDs with blue have become more common in recent years. ColorCode 3-D, a anaglyph stereoscopic color scheme, uses the RG color space to simulate a broad spectrum of color in one eye, while the blue portion of the spectrum transmits a black-and-white (black-and-blue) image to the other eye to give depth perception. === 16-bit RB palette === === 16-bit GB palette === == Regular RGB palettes == Here are grouped those full RGB hardware palettes that have the same number of binary levels (i.e., the same number of bits) for every red, green and blue components using the full RGB color model. Thus, the total number of colors are always the number of possible levels by component, n, raised to a power of 3: n×n×n = n3. === 3-bit RGB === 3-bit RGB dithering: Systems with a 3-bit RGB palette use 1 bit for each of the red, green and blue color components. That is, each component is either "on" or "off" with no intermediate states. This results in an 8-color palette ((21)3 = 23 = 8) that has black, white, the three RGB primary colors red, green and blue and their correspondent complementary colors cyan, magenta and yellow as follows: The color indices vary between implementations; therefore, index numbers are not given. The 3-bit RGB palette is used by: Text terminals following the ECMA-48 standard (sometimes known as the "ANSI standard", although ANSI X3.128 does not define colors) World System Teletext Level 1/1.5 Videotex Oric computers BBC Micro PC-8801 (up to the MkII) PC-9801 (with original 8086 CPU, before the VM/VX models) Sharp X1 (models before the X1 Turbo Z) Sharp MZ 700 FM-7, FM New 7, FM 77 (before the FM77AV) Sinclair QL Space Invaders Part II (arcade hardware) Macintosh SE (with a color printer or external monitor) Atari 2600 (SECAM version) Color Maximite (PIC32 based microcomputer) Arcadia 2001 PV-1000 Monkey Magic (arcade hardware) VIC-20 (high-res mode) Mouse Trap (arcade hardware) Sanyo MBC-550 series Windows 1.0 (includes dithering) === 6-bit RGB === Systems with a 6-bit RGB palette use 2 bits for each of the red, green, and blue color components. This results in a (22)3 = 43 = 64-color palette as follows: 6-bit RGB systems include the following: Enhanced Graphics Adapter (EGA) for IBM PC/AT (16 colors at once) Sega Master System video game console (32 colors at once) GIME for TRS-80 Color Computer 3 (16 colors at once) Pebble Time smartwatch which has a 6-bit (64 color) e-paper display Parallax Propeller using the reference VGA circuit === 9-bit RGB === Systems with a 9-bit RGB palette use 3 bits for each of the red, green, and blue color components. This results in a (23)3 = 83 = 512-color palette as follows: 9-bit RGB systems include the following: Atari ST (Normally 4 to 16 at once without tricks) MSX2 computers (up to 16 at once) Sega Genesis video game console, (64 colors at once) Sega Nomad TurboGrafx-16 (NEC PC-Engine) ZX Spectrum Next The NEC PC-88

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  Apprenticeship learning

  In artificial intelligence, apprenticeship learning (or learning from demonstration or imitation learning) is the process of learning by observing an expert. It can be viewed as a form of supervised learning, where the training dataset consists of task executions by a demonstration teacher. == Mapping function approach == Mapping methods try to mimic the expert by forming a direct mapping either from states to actions, or from states to reward values. For example, in 2002 researchers used such an approach to teach an AIBO robot basic soccer skills. === Inverse reinforcement learning approach === Inverse reinforcement learning (IRL) is the process of deriving a reward function from observed behavior. While ordinary "reinforcement learning" involves using rewards and punishments to learn behavior, in IRL the direction is reversed, and a robot observes a person's behavior to figure out what goal that behavior seems to be trying to achieve. The IRL problem can be defined as: Given 1) measurements of an agent's behaviour over time, in a variety of circumstances; 2) measurements of the sensory inputs to that agent; 3) a model of the physical environment (including the agent's body): Determine the reward function that the agent is optimizing. IRL researcher Stuart J. Russell proposes that IRL might be used to observe humans and attempt to codify their complex "ethical values", in an effort to create "ethical robots" that might someday know "not to cook your cat" without needing to be explicitly told. The scenario can be modeled as a "cooperative inverse reinforcement learning game", where a "person" player and a "robot" player cooperate to secure the person's implicit goals, despite these goals not being explicitly known by either the person nor the robot. In 2017, OpenAI and DeepMind applied deep learning to the cooperative inverse reinforcement learning in simple domains such as Atari games and straightforward robot tasks such as backflips. The human role was limited to answering queries from the robot as to which of two different actions were preferred. The researchers found evidence that the techniques may be economically scalable to modern systems. Apprenticeship via inverse reinforcement learning (AIRP) was developed by in 2004 Pieter Abbeel, Professor in Berkeley's EECS department, and Andrew Ng, Associate Professor in Stanford University's Computer Science Department. AIRP deals with "Markov decision process where we are not explicitly given a reward function, but where instead we can observe an expert demonstrating the task that we want to learn to perform". AIRP has been used to model reward functions of highly dynamic scenarios where there is no obvious reward function intuitively. Take the task of driving for example, there are many different objectives working simultaneously - such as maintaining safe following distance, a good speed, not changing lanes too often, etc. This task, may seem easy at first glance, but a trivial reward function may not converge to the policy wanted. One domain where AIRP has been used extensively is helicopter control. While simple trajectories can be intuitively derived, complicated tasks like aerobatics for shows has been successful. These include aerobatic maneuvers like - in-place flips, in-place rolls, loops, hurricanes and even auto-rotation landings. This work was developed by Pieter Abbeel, Adam Coates, and Andrew Ng - "Autonomous Helicopter Aerobatics through Apprenticeship Learning" === System model approach === System models try to mimic the expert by modeling world dynamics. == Plan approach == The system learns rules to associate preconditions and postconditions with each action. In one 1994 demonstration, a humanoid learns a generalized plan from only two demonstrations of a repetitive ball collection task. == Example == Learning from demonstration is often explained from a perspective that the working Robot-control-system is available and the human-demonstrator is using it. And indeed, if the software works, the Human operator takes the robot-arm, makes a move with it, and the robot will reproduce the action later. For example, he teaches the robot-arm how to put a cup under a coffeemaker and press the start-button. In the replay phase, the robot is imitating this behavior 1:1. But that is not how the system works internally; it is only what the audience can observe. In reality, Learning from demonstration is much more complex. One of the first works on learning by robot apprentices (anthropomorphic robots learning by imitation) was Adrian Stoica's PhD thesis in 1995. In 1997, robotics expert Stefan Schaal was working on the Sarcos robot-arm. The goal was simple: solve the pendulum swingup task. The robot itself can execute a movement, and as a result, the pendulum is moving. The problem is, that it is unclear what actions will result into which movement. It is an Optimal control-problem which can be described with mathematical formulas but is hard to solve. The idea from Schaal was, not to use a Brute-force solver but record the movements of a human-demonstration. The angle of the pendulum is logged over three seconds at the y-axis. This results into a diagram which produces a pattern. In computer animation, the principle is called spline animation. That means, on the x-axis the time is given, for example 0.5 seconds, 1.0 seconds, 1.5 seconds, while on the y-axis is the variable given. In most cases it's the position of an object. In the inverted pendulum it is the angle. The overall task consists of two parts: recording the angle over time and reproducing the recorded motion. The reproducing step is surprisingly simple. As an input we know, in which time step which angle the pendulum must have. Bringing the system to a state is called “Tracking control” or PID control. That means, we have a trajectory over time, and must find control actions to map the system to this trajectory. Other authors call the principle “steering behavior”, because the aim is to bring a robot to a given line.

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  Deductive language

  A deductive language is a computer programming language in which the program is a collection of predicates ('facts') and rules that connect them. Such a language is used to create knowledge based systems or expert systems which can deduce answers to problem sets by applying the rules to the facts they have been given. An example of a deductive language is Prolog, or its database-query cousin, Datalog. == History == As the name implies, deductive languages are rooted in the principles of deductive reasoning; making inferences based upon current knowledge. The first recommendation to use a clausal form of logic for representing computer programs was made by Cordell Green (1969) at Stanford Research Institute (now SRI International). This idea can also be linked back to the battle between procedural and declarative information representation in early artificial intelligence systems. Deductive languages and their use in logic programming can also be dated to the same year when Foster and Elcock introduced Absys, the first deductive/logical programming language. Shortly after, the first Prolog system was introduced in 1972 by Colmerauer through collaboration with Robert Kowalski. == Components == The components of a deductive language are a system of formal logic and a knowledge base upon which the logic is applied. === Formal Logic === Formal logic is the study of inference in regards to formal content. The distinguishing feature between formal and informal logic is that in the former case, the logical rule applied to the content is not specific to a situation. The laws hold regardless of a change in context. Although first-order logic is described in the example below to demonstrate the uses of a deductive language, no formal system is mandated and the use of a specific system is defined within the language rules or grammar. As input, a predicate takes any object(s) in the domain of interest and outputs either one of two Boolean values: true or false. For example, consider the sentences "Barack Obama is the 44th president" and "If it rains today, I will bring an umbrella". The first is a statement with an associated truth value. The second is a conditional statement relying on the value of some other statement. Either of these sentences can be broken down into predicates which can be compared and form the knowledge base of a deductive language. Moreover, variables such as 'Barack Obama' or 'president' can be quantified over. For example, take 'Barack Obama' as variable 'x'. In the sentence "There exists an 'x' such that if 'x' is the president, then 'x' is the commander in chief." This is an example of the existential quantifier in first order logic. Take 'president' to be the variable 'y'. In the sentence "For every 'y', 'y' is the leader of their nation." This is an example of the universal quantifier. === Knowledge Base === A collection of 'facts' or predicates and variables form the knowledge base of a deductive language. Depending on the language, the order of declaration of these predicates within the knowledge base may or may not influence the result of applying logical rules. Upon application of certain 'rules' or inferences, new predicates may be added to a knowledge base. As new facts are established or added, they form the basis for new inferences. As the core of early expert systems, artificial intelligence systems which can make decisions like an expert human, knowledge bases provided more information than databases. They contained structured data, with classes, subclasses, and instances. == Prolog == Prolog is an example of a deductive, declarative language that applies first- order logic to a knowledge base. To run a program in Prolog, a query is posed and based upon the inference engine and the specific facts in the knowledge base, a result is returned. The result can be anything appropriate from a new relation or predicate, to a literal such as a Boolean (true/false), depending on the engine and type system.

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  System Service Descriptor Table

  The System Service Descriptor Table (SSDT) is an internal dispatch table within Microsoft Windows. == Function == The SSDT maps syscalls to kernel function addresses. When a syscall is issued by a user space application, it contains the service index as parameter to indicate which syscall is called. The SSDT is then used to resolve the address of the corresponding function within ntoskrnl.exe. In modern Windows kernels, two SSDTs are used: One for generic routines (KeServiceDescriptorTable) and a second (KeServiceDescriptorTableShadow) for graphical routines. A parameter passed by the calling userspace application determines which SSDT shall be used. == Hooking == Modification of the SSDT allows to redirect syscalls to routines outside the kernel. These routines can be either used to hide the presence of software or to act as a backdoor to allow attackers permanent code execution with kernel privileges. For both reasons, hooking SSDT calls is often used as a technique in both Windows kernel mode rootkits and antivirus software. In 2010, many computer security products which relied on hooking SSDT calls were shown to be vulnerable to exploits using race conditions to attack the products' security checks.

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  Tensor glyph

  In scientific visualization a tensor glyph is an object that can visualize all or most of the nine degrees of freedom, such as acceleration, twist, or shear – of a 3 × 3 {\displaystyle 3\times 3} matrix. It is used for tensor field visualization, where a data-matrix is available at every point in the grid. "Glyphs, or icons, depict multiple data values by mapping them onto the shape, size, orientation, and surface appearance of a base geometric primitive." Tensor glyphs are a particular case of multivariate data glyphs. There are certain types of glyphs that are commonly used: Ellipsoid Cuboid Cylindrical Superquadrics According to Thomas Schultz and Gordon Kindlmann, specific types of tensor fields "play a central role in scientific and biomedical studies as well as in image analysis and feature-extraction methods."

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  Write or Die

  Write or Die is an online web application designed to combat writer's block by letting users of the application punish themselves if they slow down or stop typing in the application's window. How severe the punishments are depends on the mode the user chooses, which ranges from "Gentle" to "Kamikaze". It was reviewed by publications PCWorld, the Los Angeles Times and The Guardian, and it was most notably used by writers Helen Oyeyemi and David Nicholls. The creator, Jeff Printy, explained that he wrote the application because he wants "to be published and make a living as a writer."

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  Network eavesdropping

  Network eavesdropping, also known as eavesdropping attack, sniffing attack, or snooping attack, is a method that retrieves user information through the internet. This attack happens on electronic devices like computers and smartphones. This network attack typically happens under the usage of unsecured networks, such as public wifi connections or shared electronic devices. Eavesdropping attacks through the network is considered one of the most urgent threats in industries that rely on collecting and storing data. Internet users use eavesdropping via the Internet to improve information security. A typical network eavesdropper may be called a Black-hat hacker and is considered a low-level hacker as it is simple to network eavesdrop successfully. The threat of network eavesdroppers is a growing concern. Research and discussions are brought up in the public's eye, for instance, types of eavesdropping, open-source tools, and commercial tools to prevent eavesdropping. Models against network eavesdropping attempts are built and developed as privacy is increasingly valued. Sections on cases of successful network eavesdropping attempts and its laws and policies in the National Security Agency are mentioned. Some laws include the Electronic Communications Privacy Act and the Foreign Intelligence Surveillance Act. == Types of attacks == Types of network eavesdropping include intervening in the process of decryption of messages on communication systems, attempting to access documents stored in a network system, and listening on electronic devices. Types include electronic performance monitoring and control systems, keystroke logging, man-in-the-middle attacks, observing exit nodes on a network, and Skype & Type. === Electronic performance monitoring and control systems (EPMCSs) === Electronic performance monitoring and control systems are used by employees or companies and organizations to collect, store, analyze, and report actions or performances of employers when they are working. The beginning of this system is used to increase the efficiency of workers, but instances of unintentional eavesdropping can occur, for example, when employees' casual phone calls or conversations would be recorded. === Keystroke logging === Keystroke logging is a program that can oversee the writing process of the user. It can be used to analyze the user's typing activities, as keystroke logging provides detailed information on activities like typing speed, pausing, deletion of texts, and more behaviors. By monitoring the activities and sounds of the keyboard strikes, the message typed by the user can be translated. Although keystroke logging systems do not explain reasons for pauses or deletion of texts, it allows attackers to analyze text information. Keystroke logging can also be used with eye-tracking devices which monitor the movements of the user's eyes to determine patterns of the user's typing actions which can be used to explain the reasons for pauses or deletion of texts. === Man-in-the-middle attack (MitM) === A Man-in-the-middle attack is an active eavesdropping method that intrudes on the network system. It can retrieve and alter the information sent between two parties without anyone noticing. The attacker hijacks the communication systems and gains control over the transport of data, but cannot insert voice messages that sound or act like the actual users. Attackers also create independent communications through the system with the users acting as if the conversation between users is private. The "man-in-the-middle" can also be referred to as lurkers in a social context. A lurker is a person who rarely or never posts anything online, but the person stays online and observes other users' actions. Lurking can be valuable as it lets people gain knowledge from other users. However, like eavesdropping, lurking into other users' private information violates privacy and social norms. === Observing exit nodes === Distributed networks including communication networks are usually designed so that nodes can enter and exit the network freely. However, this poses a danger in which attacks can easily access the system and may cause serious consequences, for example, leakage of the user's phone number or credit card number. In many anonymous network pathways, the last node before exiting the network may contain actual information sent by users. Tor exit nodes are an example. Tor is an anonymous communication system that allows users to hide their IP addresses. It also has layers of encryption that protect information sent between users from eavesdropping attempts trying to observe the network traffic. However, Tor exit nodes are used to eavesdrop at the end of the network traffic. The last node in the network path flowing through the traffic, for instance, Tor exit nodes, can acquire original information or messages that were transmitted between different users. === Skype & Type (S&T) === Skype & Type (S&T) is a new keyboard acoustic eavesdropping attack that takes advantage of Voice-over IP (VoIP). S&T is practical and can be used in many applications in the real world, as it does not require attackers to be close to the victim and it can work with only some leaked keystrokes instead of every keystroke. With some knowledge of the victim's typing patterns, attackers can gain a 91.7% accuracy typed by the victim. Different recording devices including laptop microphones, smartphones, and headset microphones can be used for attackers to eavesdrop on the victim's style and speed of typing. It is especially dangerous when attackers know what language the victim is typing in. == Tools to prevent eavesdropping attacks == Computer programs where the source code of the system is shared with the public for free or for commercial use can be used to prevent network eavesdropping. They are often modified to cater to different network systems, and the tools are specific in what task it performs. In this case, Advanced Encryption Standard-256, Bro, Chaosreader, CommView, Firewalls, Security Agencies, Snort, Tcptrace, and Wireshark are tools that address network security and network eavesdropping. === Advanced encryption standard-256 (AES-256) === It is a cipher block chaining (CBC) mode for ciphered messages and hash-based message codes. The AES-256 contains 256 keys for identifying the actual user, and it represents the standard used for securing many layers on the internet. AES-256 is used by Zoom Phone apps that help encrypt chat messages sent by Zoom users. If this feature is used in the app, users will only see encrypted chats when they use the app, and notifications of an encrypted chat will be sent with no content involved. === Bro === Bro is a system that detects network attackers and abnormal traffic on the internet. It emerged at the University of California, Berkeley that detects invading network systems. The system does not apply to the detection of eavesdropping by default, but can be modified to an offline analyzing tool for eavesdropping attacks. Bro runs under Digital Unix, FreeBSD, IRIX, SunOS, and Solaris operating systems, with the implementation of approximately 22,000 lines of C++ and 1,900 lines of Bro. It is still in the process of development for real-world applications. === Chaosreader === Chaosreader is a simplified version of many open-source eavesdropping tools. It creates HTML pages on the content of when a network intrusion is detected. No actions are taken when an attack occurs and only information such as time, network location on which system or wall the user is trying to attack will be recorded. === CommView === CommView is specific to Windows systems which limits real-world applications because of its specific system usage. It captures network traffic and eavesdropping attempts by using packet analyzing and decoding. === Firewalls === Firewall technology filters network traffic and blocks malicious users from attacking the network system. It prevents users from intruding into private networks. Having a firewall in the entrance to a network system requires user authentications before allowing actions performed by users. There are different types of firewall technologies that can be applied to different types of networks. === Security agencies === A Secure Node Identification Agent is a mobile agent used to distinguish secure neighbor nodes and informs the Node Monitoring System (NMOA). The NMOA stays within nodes and monitors the energy exerted, and receives information about nodes including node ID, location, signal strength, hop counts, and more. It detects nodes nearby that are moving out of range by comparing signal strengths. The NMOA signals the Secure Node Identification Agent (SNIA) and updates each other on neighboring node information. The Node BlackBoard is a knowledge base that reads and updates the agents, acting as the brain of the security system. The Node Key Management agent is created when an encryption key is inserted to th

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  Israeli cybersecurity industry

  The Israeli cybersecurity industry is a rapidly growing sector within Israel's technology and innovation ecosystem. Israel is internationally recognized as a powerhouse in the cybersecurity domain, with numerous cybersecurity startups, established companies, research institutions, and government initiatives. Tel Aviv itself is being ranked 7th in annual list of best global tech ecosystems, as reported by the Jerusalem Post. == History == The roots of Israel's cybersecurity industry can be traced back to the country's strong focus on national security and intelligence. The establishment of elite military units such as Unit 8200, the Israeli Intelligence Corps unit responsible for signals intelligence and code decryption, played a significant role in the development of cybersecurity expertise in the country. Many former members of Unit 8200 have gone on to establish successful cybersecurity companies or join existing organizations, bringing their unique skill sets and experience to the private sector. == Market overview == As of 2024, Israel housed more than 450 cybersecurity startups and companies. In 2023, the value of exits by Israeli tech companies reached $7.5 billion. Israel's cybersecurity industry is characterized by a high concentration of startups develop new technologies in areas such as network security, endpoint protection, data security, cloud security, and threat intelligence. In recent years, the sector has attracted significant investment from both local and international venture capital firms, as well as major technology companies such as Microsoft, Google, and IBM. Several Israeli cybersecurity companies have gained global recognition and success, with some being acquired by major corporations or conducting successful initial public offerings (IPOs). === Key Israeli cybersecurity companies === Some key Israeli cybersecurity companies include: Check Point Software Technologies CyberArk Cato Networks Radware Wiz === Financial activity === Israel’s cybersecurity sector has seen significant financial activity. As of 2023, mergers and acquisitions in the cybersecurity sector totaled $2.8 billion. In the first quarter of 2024, the sector secured $846 million in private funding. == Background == The military experience helped much. Israel's mandatory military service, combined with the expertise developed within elite units such as Unit 8200, has fostered a strong talent pool with practical experience in cybersecurity. Israel's thriving startup ecosystem, often referred to as the "Startup Nation," has fostered an environment of innovation and collaboration that has contributed to the growth of the cybersecurity industry. Israeli cybersecurity companies often collaborate with international partners, both in the private and public sectors, to share knowledge and develop joint solutions. === Government Initiatives and Support === The government also supported well through various initiatives, such as the Israel National Cyber Directorate (INCD), which works to strengthen cybersecurity defenses and promote the development of the sector. === Academic institutions === Israeli universities and research centers are involved in cybersecurity research and education, contributing to the development of new technologies and training the next generation of cybersecurity professionals. Academic Tech transfer offices in Israel also facilitate the commercialization of cybersecurity technologies. Some academic institutions with cybersecurity laboratories include: Tel Aviv University Technion Ben-Gurion University

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