Defeating Machine Learning - Systemic Deficiencies for Detecting Malware

Explore the systemic deficiencies in machine learning for malware detection in this 45-minute BSidesLV conference talk. Delve into the evolution of the security landscape over the past 30 years, examining sandbox evasion techniques and current security postures. Gain insights into supervised machine learning, its potential vulnerabilities, and common model problems. Discover machine learning security solutions and learn about obfuscation techniques, balancing replacement and addition, and efficacy results from the speakers' lab experiments. Understand the benefits and challenges of vulnerability testing, continuous learning, and crowd-sourcing in improving malware detection systems. Examine feature vectors and their role in enhancing machine learning models for cybersecurity applications.