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Real-time Adaptive Security
Real-time Adaptive Security is the network security model necessary to accommodate the emergence of multiple perimeters and moving parts on the network, and increasingly advanced threats targeting enterprises. Adaptive security can watch a network for malicious traffic and behavioral anomalies, ferret out end point vulnerabilities, identify real-time changes to systems, automatically enforce end point protections and access rules, block malicious traffic, follow a compliance dashboard while providing audit data, and more. Among the key features of an adaptive security infrastructure are security platforms that share and correlate information rather than point solutions, so the heuristics system could communicate its suspicions to the firewall. Other features include finer-grained controls, automation (in addition to human intervention), on-demand security services, security as a service, and integration of security and management data. Rather than adding security to custom applicati ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Network Security
Network security consists of the policies, policies, processes and practices adopted to prevent, detect and monitor unauthorized access, Abuse, misuse, modification, or denial of a computer network and network-accessible resources. Network security involves the authorization of access to data in a network, which is controlled by the network administrator. Users choose or are assigned an ID and password or other authenticating information that allows them access to information and programs within their authority. Network security covers a variety of computer networks, both public and private, that are used in everyday jobs: conducting transactions and communications among businesses, Government agency, government agencies and individuals. Networks can be private, such as within a company, and others which might be open to public access. Network security is involved in organizations, enterprises, and other types of institutions. It does as its title explains: it secures the network, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Security As A Service
Security as a service (SECaaS) is a business model in which a service provider integrates their security services into a corporate infrastructure on a subscription basis more cost-effectively than most individuals or corporations can provide on their own when the total cost of ownership is considered. SECaaS is inspired by the "software as a service" model as applied to information security type services and does not require on-premises hardware, avoiding substantial capital outlays. These security services often include authentication, anti-virus, anti-malware/spyware, intrusion detection, Penetration testing and security event management, among others. Outsourced security licensing and delivery is boasting a multibillion-dollar market. SECaaS provides users with Internet security services providing protection from online threats and attacks such as DDoS that are constantly searching for access points to compromise websites. As the demand and use of cloud computing skyrockets, user ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F.,Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning IEEE Transactions on Vehicular Technology, 2020. A subset of machine learning is closely related to computational statistics, which focuses on making predicti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Multi-factor Authentication
Multi-factor authentication (MFA; encompassing two-factor authentication, or 2FA, along with similar terms) is an electronic authentication method in which a user is granted access to a website or application only after successfully presenting two or more pieces of evidence (or factors) to an authentication mechanism: knowledge (something only the user knows), possession (something only the user has), and inherence (something only the user is). MFA protects user data—which may include personal identification or financial assets—from being accessed by an unauthorized third party that may have been able to discover, for example, a single password. A ''third-party authenticator'' (TPA) app enables two-factor authentication, usually by showing a randomly generated and frequently changing code to use for authentication. Factors Authentication takes place when someone tries to log into a computer resource (such as a network, device, or application). The resource requires the u ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Artificial Intelligence
Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by animals and humans. Example tasks in which this is done include speech recognition, computer vision, translation between (natural) languages, as well as other mappings of inputs. The ''Oxford English Dictionary'' of Oxford University Press defines artificial intelligence as: the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. AI applications include advanced web search engines (e.g., Google), recommendation systems (used by YouTube, Amazon and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Tesla), automated decision-making and competing at the highest level in strategic game systems (such as chess and Go). ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Risk Score
Risk score (or risk scoring) is the name given to a general practice in applied statistics, bio-statistics, econometrics and other related disciplines, of creating an easily calculated number (the score) that reflects the level of risk in the presence of some risk factors (e.g. risk of mortality or disease in the presence of symptoms or genetic profile, risk financial loss considering credit and financial history, etc.). Risk scores are designed to be: * Simple to calculate: In many cases all you need to calculate a score is a pen and a piece of paper (although some scores use rely on more sophisticated or less transparent calculations that require a computer program). * Easily interpreted: The result of the calculation is a single number, and higher score usually means higher risk. Furthermore, many scoring methods enforce some form of monotonicity along the measured risk factors to allow a straight forward interpretation of the score (e.g. risk of mortality only increases wit ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Intrusion-detection System
An intrusion detection system (IDS; also intrusion prevention system or IPS) is a device or software application that monitors a network or systems for malicious activity or policy violations. Any intrusion activity or violation is typically reported either to an administrator or collected centrally using a security information and event management (SIEM) system. A SIEM system combines outputs from multiple sources and uses alarm filtering techniques to distinguish malicious activity from false alarms. IDS types range in scope from single computers to large networks. The most common classifications are network intrusion detection systems (NIDS) and host-based intrusion detection systems (HIDS). A system that monitors important operating system files is an example of an HIDS, while a system that analyzes incoming network traffic is an example of an NIDS. It is also possible to classify IDS by detection approach. The most well-known variants are signature-based detection (recogni ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Network Intrusion Detection System
An intrusion detection system (IDS; also intrusion prevention system or IPS) is a device or software application that monitors a network or systems for malicious activity or policy violations. Any intrusion activity or violation is typically reported either to an administrator or collected centrally using a security information and event management (SIEM) system. A SIEM system combines outputs from multiple sources and uses alarm filtering techniques to distinguish malicious activity from false alarms. IDS types range in scope from single computers to large networks. The most common classifications are network intrusion detection systems (NIDS) and host-based intrusion detection systems (HIDS). A system that monitors important operating system files is an example of an HIDS, while a system that analyzes incoming network traffic is an example of an NIDS. It is also possible to classify IDS by detection approach. The most well-known variants are signature-based detection (recogni ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sourcefire
Sourcefire, Inc was a technology company that developed network security hardware and software. The company's Firepower network security appliances were based on Snort, an open-source intrusion detection system (IDS). Sourcefire was acquired by Cisco for $2.7 billion in July 2013. Background Sourcefire was founded in 2001 by Martin Roesch, the creator of Snort. The company created a commercial version of the Snort software, the Sourcefire 3D System, which evolved into the company's Firepower line of network security products. The company's headquarters was in Columbia, Maryland in the United States, with offices abroad. Financial The company's initial growth was funded through four separate rounds of financing raising a total of $56.5 million from venture investors such as Sierra Ventures, New Enterprise Associates, Sequoia Capital, Core Capital Partners, Inflection Point Ventures, Meritech Capital Partners, and Cross Creek Capital, L.P. In 2005, Check Point Software attemp ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
IBM Internet Security Systems
IBM Internet Security Systems, formerly Internet Security Systems, and often known simply as ISS or ISSX (after its former NASDAQ ticker symbol) is a security software and managed security services provider founded in 1994. The company was acquired by IBM in 2006. It provides software and services for computers, servers, networks, and remote locations that involve preemptive security against threats before they affect a business. History In 1992, while attending the Georgia Institute of Technology, Christopher Klaus developed the first version of Internet Security Scanner. In 1994, Klaus founded Internet Security Systems (ISS) to further develop and market Internet Security Scanner, which later became Internet Scanner. Although the larger shareholder, Klaus took the role of chief technology officer (CTO), while Tom Noonan was recruited as chief executive officer in 1995. In 1996 David Strohm from Greylock Ventures and Bob Davoli, from Sigma Partners, led the first round of ven ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F.,Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning IEEE Transactions on Vehicular Technology, 2020. A subset of machine learning is closely related to computational statistics, which focuses on making predicti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Risk-based Authentication
In Authentication, risk-based authentication is a non-static authentication system which takes into account the profile (IP address, User-Agent HTTP header, time of access, and so on) of the agent requesting access to the system to determine the risk profile associated with that transaction. The risk profile is then used to determine the complexity of the challenge. Higher risk profiles leads to stronger challenges, whereas a static username/password may suffice for lower-risk profiles. Risk-based implementation allows the application to challenge the user for additional credentials only when the risk level is appropriate. The point is that user validation accuracy is improved without inconveniencing a user and risk-based authentication is used by major companies. Criticism * The system that computes the risk profile has to be diligently maintained and updated as new threats emerge. Improper configuration may lead to unauthorized access. * The user's connection profile (e.g. IP Ge ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
