A Survey of DDOS Attacks Using Machine Learning Techniques

Maliheh Alsadat Arshi, M. Ayeesha Nasreen, Karanam Madhavi

2020E3S Web of Conferences38 citationsDOI Open Access PDF

Abstract

The DDoS attacks are the most destructive attacks that interrupt the safe operation of essential services delivered by the internet community’s different organizations. DDOS stands for Distributed Denial Of Service attacks. These attacks are becoming more complex and expected to expand in number day after day, rendering detecting and combating these threats challenging. Hence, an advanced intrusion detection system (IDS) is required to identify and recognize an- anomalous internet traffic behaviour. Within this article the process is supported on the latest dataset containing the current form of DDoS attacks including (HTTP flood, SIDDoS). This study combines well-known grouping methods such as Naïve Bayes, Multilayer Perceptron (MLP), and SVM, Decision trees.

Topics & Concepts

Denial-of-service attackComputer scienceComputer securityFirewall (physics)The InternetIntrusion detection systemSupport vector machineNaive Bayes classifierApplication layer DDoS attackTrinooMultilayer perceptronArtificial intelligenceMachine learningData miningWorld Wide WebArtificial neural networkEntropy (arrow of time)Charged black holePhysicsExtremal black holeQuantum mechanicsNetwork Security and Intrusion DetectionInternet Traffic Analysis and Secure E-votingAdvanced Malware Detection Techniques