Abstract
Recent advancements in computer technology have precipitated a shift towards virtual environments, accelerated by the COVID-19 pandemic. Cybercriminals have capitalized on this trend, transitioning their activities to exploit vulnerabilities in cyberspace. Malicious software (malware) has emerged as a preferred tool for launching cyber-attacks, continually evolving with sophisticated obfuscation and packing techniques to evade detection. Traditional machine learning (ML) algorithms, once effective in identifying malware, are now struggling to keep pace with these advancements. In response, deep learning (DL) algorithms offer a promising solution, leveraging their ability to discern intricate patterns and correlations within data. This study proposes a novel hybrid deep-learning-based architecture, integrating two pre-trained network models to enhance classification accuracy. Through extensive evaluation on datasets including Malimg, Microsoft BIG 2015, and Malevis, the proposed method demonstrates significant improvements in accuracy, outperforming existing ML-based malware detection methods in the literature. Specifically, the proposed method achieves an impressive accuracy of 97.78% on the Malimg dataset, underscoring its effectiveness in combating sophisticated malware variants. Keywords — Malware, malware classification, malware detection, malware variants, deep neural networks, transfer learning, deep learning.
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