AI-Driven Approaches to Combat Malware Threats

Abstract

Malicious software, or malware, poses a constant threat to computer systems and networks, necessitating innovative and robust methods of detection. One innovative strategy that has gained popularity is Machine Learning (ML). Address these challenges, using its capability to assay vast datasets and descry complex patterns reflective of vicious conditioning. This paper provides a detailed review of the state- of- the- art ML ways for malware discovery and introduces a new frame aimed at perfecting delicacy and functional effectiveness. The proposed frame uses supervised, unsupervised, and semi-supervised machine learning styles, integrating different features uprooted from malware samples. These features include stationary characteristics, similar as train size, train type, and API call alongside dynamic traits like system calls, network business, and gest al patterns. By combining these features and employing multiple ML models, the frame ensures robust discovery across a wide diapason of malware types, including contagions, worms, Trojans, and ransomware.
Additionally, the framework addresses key challenges in traditional detection methods, such as scalability and adaptability to new and evolving malware variants. By leveraging the strengths of different ML techniques, it provides a unified approach to combat malware threats in complex and dynamic cybersecurity environments. This framework represents a significant advancement in malware detection, offering enhanced protection and resilience against emerging threats.
Overall, the research underscores the transformative potential of ML in the realm of cyber security, paving the way for smarter, more adaptive defines mechanisms to secure digital systems and networks effectively.