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.
News – Lehigh Valley Health Network to pay $65 million in landmark ransomware settlement – TEISS
Lehigh Valley Health Network will pay a landmark $65 million in a settlement in a ransomware case. This will be the largest sum ever paid
