A deep learning based HTTP slow DoS classification approach using flow data
N. Muraleedharan, B. Janet
Abstract
The popularity of the Internet introduces many network-enabled services that can be accessed by the user. But the adversaries are trying to deny these critical services to the user through Denial of Service (DoS) attacks. Presently, dealing with DoS attack which targets the application layer using slow traffic rate is one of the key challenges faced by the service providers. In this paper, a deep classification model using flow data is proposed to detect slow DoS attack on HTTP. The classifier is evaluated using CICIDS2017 dataset. The results obtained show that the classifier can obtain 99.61% accuracy.
Topics & Concepts
Denial-of-service attackComputer sciencePopularityClassifier (UML)The InternetDeep learningData miningArtificial intelligenceComputer networkMachine learningComputer securityWorld Wide WebPsychologySocial psychologyNetwork Security and Intrusion DetectionInternet Traffic Analysis and Secure E-votingAdvanced Malware Detection Techniques