AI-Driven Malware Mutation: Evaluating the Re-silience of Next-Generation Antivirus (NGAV) Against AI-Powered Evasion Techniques

The integration of AI into modern attacking methods has completely defeated traditional endpoint security systems according to the study results. NGAV establishes essential progress through its behavioral heuristics and deep learning capabilities which enable it to surpass outdated security systems [1] [15] yet it still faces security risks from Adversarial Machine Learning (AML) attacks [4]. The sophisticated mutation engines use RL and GANs to maintain destructive payloads while they hide the features which ML classifiers depend on [14]. The findings show that static algorithmic defense systems exhibit extreme weakness because their average evasion rates exceed 40 percent against commercial systems and reach a maximum of 98 percent in academic settings [6]. The research demonstrates that organizations can enhance their NGAV protection through additional security measures. Organizations can achieve substantial reductions in generative malware success rates by implementing continuous adversarial retraining pipelines and HCAMDF multi-layered ensemble architectures and mathematical security regularization methods [28] [24].

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