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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]
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.
Threat Dragon follows the values and principles of the threat modeling manifesto. It can be used to record possible threats and decide on their mitigations, as well as giving a visual indication of the threat model components and threat surfaces. Threat Dragon runs either as a web application or as a desktop application.
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 ...
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
Attack tree – another approach to security threat modeling, stemming from dependency analysis; Cyber security and countermeasure; DREAD – a classification system for security threats; OWASP – an organization devoted to improving web application security through education
These attacks typically involve similar statistical techniques as power-analysis attacks. A deep-learning-based side-channel attack, [11] [12] [13] using the power and EM information across multiple devices has been demonstrated with the potential to break the secret key of a different but identical device in as low as a single trace.
Anomaly detection is applicable in a very large number and variety of domains, and is an important subarea of unsupervised machine learning. As such it has applications in cyber-security, intrusion detection, fraud detection, fault detection, system health monitoring, event detection in sensor networks, detecting ecosystem disturbances, defect ...