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Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. [1] A survey from May 2020 exposes the fact that practitioners report a dire need for better protecting machine learning systems in industrial applications.
The OWASP Top 10 - 2017 results from recent research based on comprehensive data compiled from over 40 partner organizations. This data revealed approximately 2.3 million vulnerabilities across over 50,000 applications. [4] According to the OWASP Top 10 - 2021, the ten most critical web application security risks include: [5] Broken access control
The Open Web Application Security Project [7] (OWASP) is an online community that produces freely available articles, methodologies, documentation, tools, and technologies in the fields of IoT, system software and web application security. [8] [9] [10] The OWASP provides free and open resources. It is led by a non-profit called The OWASP ...
Traditional inspection technologies are unable to keep up with the recent outbreaks of widespread attacks. [2] Unlike inspection methods such as deep packet inspection (DPI), where only the data part (and possibly also the header) of a packet are inspected, deep content inspection (DCI)-based systems are exhaustive, such that network traffic packets are reassembled into their constituting ...
The growth of web threats is a result of the popularity of the Web – a relatively unprotected, widely and consistently used medium that is crucial to business productivity, online banking, and e-commerce as well as the everyday lives of people worldwide. The appeal of Web 2.0 applications and websites increases the vulnerability of the Web.
STRIDE is a model for identifying computer security threats [1] developed by Praerit Garg and Loren Kohnfelder at Microsoft. [2] It provides a mnemonic for security threats in six categories.
Beyond vulnerability discovery, code property graphs find applications in code clone detection, [8] [9] attack-surface detection, [10] exploit generation, [11] measuring code testability, [12] and backporting of security patches. [13]
Systems using artificial neural networks have been used to great effect. Another method is to define what normal usage of the system comprises using a strict mathematical model, and flag any deviation from this as an attack. This is known as strict anomaly detection. [3]