The paper presents a methodology for evading malware detection models using active opcode insertion and reinforcement learning based MalAOI. They develop a function to classify a malware sample as benign software by modifying it to generate a new sample, while retaining functionality and introducing minimal additional load. A reinforcement learning environment enables automatic selection of suitable insertion positions in malware samples and corresponding benign code sequences to generate adversarial malware avoiding detection.
System Safety and Security Handbook [Book]
Hey there, Bay Area folks! Let’s hash out the cool, intricate world of system safety and security, especially in arenas we care about like healthcare
