Tiki-Taka
Chaoyun Zhang, Xavier Costa‐Pérez, Paul Patras
Abstract
Neural networks are increasingly important in the development of Network Intrusion Detection Systems (NIDS), as they have the potential to achieve high detection accuracy while requiring limited feature engineering. Deep learning-based detectors can be however vulnerable to adversarial examples, by which attackers that may be oblivious to the precise mechanics of the targeted NIDS add subtle perturbations to malicious traffic features, with the aim of evading detection and disrupting critical systems in a cost-effective manner. Defending against such adversarial attacks is therefore of high importance, but requires to address daunting challenges.
Topics & Concepts
Adversarial systemComputer scienceIntrusion detection systemDeep neural networksFeature (linguistics)Deep learningComputer securityArtificial intelligenceFeature engineeringPhilosophyLinguisticsNetwork Security and Intrusion DetectionAdversarial Robustness in Machine LearningAdvanced Malware Detection Techniques