Malware Analysis and Attribution in Human Threat Intelligence

Increasing numbers of people using online resources without precautions is one of the main reasons hazardous computer apps evolve quickly. These days, Trojan horseback, malware, and worm authors do it for profit rather than fame. So, malware problem Computer security now prioritizes detection. Zero-day malware exploits undiscovered holes without detection. Deep learning models, which can analyze data in several dimensions and uncover features, are an effective threat detection and classification alternative. This chapter examines deep learning-based malware recognition and categorization for zero-day vulnerabilities and attack authentication. While studying model architectures, data sources, evaluation criteria, and real-world applications, we discuss asymmetrical evasion, comprehensibility, and statistical imbalance. The article also explores how graph-based models, self-encoder RNN, and CNN understand unfamiliar virus behaviors.