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).