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.
Don’t Skip This Critical FBI Warning About Free Online File Converters
The FBI Denver Field Office has issued a warning against the use of free online file conversion tools and YouTube video downloaders, which can load
