Poster Abstract: A Semi-Supervised Approach for Network Intrusion Detection Using Generative Adversarial Networks
Hyejeong Jeong, Jieun Yu, Wonjun Lee
Abstract
Network intrusion detection is a crucial task since malicious traffic occurs every second these days. Various research has been studied in this field and shows high performance. However, most of them are conducted in a supervised manner that needs a range of labeled data but it is hard to obtain. This paper proposes a semi-supervised Generative Adversarial Networks (GAN) model for network intrusion detection that requires only 10 labeled data per each flow type. Our model is evaluated using the publicly available CICIDS-2017 dataset and outperforms other malware traffic classification models.
Topics & Concepts
Computer scienceIntrusion detection systemMalwareTask (project management)Field (mathematics)Adversarial systemMachine learningArtificial intelligenceData miningGenerative grammarGenerative adversarial networkNetwork securityDeep learningComputer networkComputer securityEngineeringSystems engineeringMathematicsPure mathematicsNetwork Security and Intrusion DetectionAdvanced Malware Detection TechniquesInternet Traffic Analysis and Secure E-voting