Adversarial machine learning


Machine learning algorithms are developed for stationary environments. However, intelligent and adaptive adversaries can carefully craft input data to always bypass AI-based cybersecurity systems. Therefore, direct utilization of machine learning algorithms would provide limited benefit in the cyber security domain. In adversarial machine learning, we try to first identify potential vulnerabilities of machine learning algorithms during learning and classification and build attacks that correspond to detected vulnerabilities (anti-forensics). Afterward, we are building countermeasures to improve the security of machine learning algorithms (anti-anti-forensics).

Attacking Machine Learning with Adversarial Examples
  Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make
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