dorsal/arxiv
View SchemaAI-Powered Algorithms for the Prevention and Detection of Computer Malware Infections
| Authors | Rakesh Keshava, Sathish Kuppan Pandurangan, M. Sakthivanitha, Sankaranainar Parmsivan, Goutham Sunkara, R. Maruthi |
|---|---|
| Categories | |
| ArXiv ID | 2601.06219vv1 |
| URL | https://arxiv.org/abs/2601.06219 |
| DOI | 10.1109/ICESC65114.2025.11212519 |
| License | http://creativecommons.org/licenses/by/4.0/ |
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.
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"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.",
"arxiv_id": "2601.06219",
"authors": [
"Rakesh Keshava",
"Sathish Kuppan Pandurangan",
"M. Sakthivanitha",
"Sankaranainar Parmsivan",
"Goutham Sunkara",
"R. Maruthi"
],
"categories": [
"cs.CR",
"cs.AI"
],
"doi": "10.1109/ICESC65114.2025.11212519",
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "AI-Powered Algorithms for the Prevention and Detection of Computer Malware Infections",
"url": "https://arxiv.org/abs/2601.06219",
"version": "v1"
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"variant": "snapshot-2026-01-17",
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