Case Study: Neural Network Malware Detection Verification for Feature and Image Datasets

arXiv:2404.05703v1 Announce Type: new
Abstract: Malware, or software designed with harmful intent, is an ever-evolving threat that can have drastic effects on both individuals and institutions. Neural network malware classification systems are key tools for combating these threats but are vulnerable to adversarial machine learning attacks. These attacks perturb input data to cause misclassification, bypassing protective systems. Existing defenses often rely on enhancing the training process, thereby increasing the model's robustness to these perturbations, which is quantified using verification. While …

adversarial arxiv attacks can case classification cs.cr datasets detection feature image institutions intent key machine machine learning malware malware classification malware detection network neural network software study systems threat threats tools verification vulnerable