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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.
To be even more effective and efficient, however, threat hunting can be partially automated, or machine-assisted, as well. In this case, the analyst uses software that leverages machine learning and user and entity behavior analytics (UEBA) to inform the analyst of potential risks. The analyst then investigates these potential risks, tracking ...
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] Other techniques used to detect anomalies include data mining methods, grammar based methods, and Artificial Immune System. [2]
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 ...
Communicates threat surfaces, attack vectors and malicious activities directed to both information technology and operational technology platforms. Serve as fact-based repository for evidence of both successful and unsuccessful cyber attacks. Provide indicators for computer emergency response teams and incident response groups.
SEC541: Cloud Security Attacker Techniques, Monitoring, and Threat Detection; SEC522: Application Security: Securing Web Apps, APIs, and Microservices; FOR585: Smartphone Forensic Analysis In-Depth; SEC595: Applied Data Science and AI/Machine Learning for Cybersecurity Professionals; SEC501: Advanced Security Essentials - Enterprise Defender
Many AI platforms use Wikipedia data, [272] mainly for training machine learning applications. There is research and development of various artificial intelligence applications for Wikipedia such as for identifying outdated sentences, [273] detecting covert vandalism [274] or recommending articles and tasks to new editors.
Web application security is a branch of information security that deals specifically with the security of websites, web applications, and web services. At a high level, web application security draws on the principles of application security but applies them specifically to the internet and web systems.