AI-Powered Algorithms for the Prevention and Detection of Computer Malware Infections

TL;DR

This paper introduces a hybrid AI-based malware detection framework that combines static, dynamic, and contextual analysis to improve accuracy and timeliness in identifying malware infections, outperforming existing methods.

Contribution

The study presents HCAMDF, a novel multi-layer AI framework integrating static and behavioral analysis with ensemble risk scoring for proactive malware detection.

Findings

Achieved 97.3% accuracy in malware detection.

Reduced false positive rate to 1.5%.

Demonstrated superior performance over existing ML and DL methods.

Abstract

The rise in frequency and complexity of malware attacks are viewed as a major threat to modern digital infrastructure, which means that traditional signature-based detection methods are becoming less effective. As cyber threats continue to evolve, there is a growing need for intelligent systems to accurately and proactively identify and prevent malware infections. This study presents a new hybrid context-aware malware detection framework(HCAMDF) based on artificial intelligence (AI), which combines static file analysis, dynamic behavioural analysis, and contextual metadata to provide more accurate and timely detection. HCADMF has a multi-layer architecture, which consists of lightweight static classifiers such as Long Short Term Memory (LSTM) for real-time behavioral analysis, and an ensemble risk scoring through the integration of multiple layers of prediction. Experimental evaluations of the new/methodology with benchmark datasets, EMBER and CIC-MalMem2022, showed that the new approach provides superior performances with an accuracy of 97.3%, only a 1.5% false positive rate and minimal detection delay compared to several existing machine learning(ML) and deep learning(DL) established methods in the same fields. The results show strong evidence that hybrid AI can detect both existing and novel malware variants, and lay the foundation on intelligent security systems that can enable real-time detection and adapt to a rapidly evolving threat landscape.