Abstract:
The rapid proliferation of malware presents a significant threat to computer systems and data security. The ability to detect and accurately classify malware is crucial for mitigating cyber threats and preventing potential damages. However, traditional methods for malware classification and analysis have shown some limitations in keeping pace with the with the ever-changing landscape of malware. In this thesis, we propose a novel approach that harnesses the power of machine and deep learning techniques for efficient malware classification and offers real-time and automated data-driven solution, enabling proactive measures to efficiently prevent and mitigate cyber threats.
