Detecting Binary Perturbation Using Advanced Machine Learning Techniques

Abstract

Traditional malware defences based on signature matching are increasingly ineffective against polymorphic and metamorphic variants, which easily evade exact-match detection. The research community now uses machine learning models to extract behavioural and structural patterns from both static and dynamic attributes in their work. Conventional model detection systems, including linear classifiers, fail to identify malware when attackers apply minimal binary modifications that preserve the functionality of the malware. The research presents a unified method to boost malware classification stability through the implementation of Variational Autoencoders (VAEs) for extracting latent space representations. The research combines traditional machine learning algorithms with Convolutional Neural Networks (CNNs) from deep learning models. The reduction in computation costs reaches 95%. The 32-dimensional embeddings from 2,381 static features lead to a 95.80% reduction in computation cost for BODMAS and a 94.42% reduct