Detection And Analysis Of Ddos Attacks Using Machine Learning Techniques: A Literature Review
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 6
Abstract
Distributed Denial of Service (DDoS) attacks is a serious threat to the network security. Servers of many companies have been the victims of such novel type of attacks. In a short span of time, these attacks from the multiple bots controlled by the botmaster (cracker) can easily drain the computing and communication resources of the victim. As the attacker uses the spoofed IP address and therefore cracker leaves the botnet quickly after it executes the command, therefore detecting the attacker is extremely difficult. Thus we need an intelligent intrusion detection system (IDS) for DDoS attacks to defend the network services. To develop the system we utilized the various machine learning techniques for detection and analysis of the behaviour of DDoS packets using anomaly-based approach. In this paper, the work is carried out on the novel type of the DDoS attacks that may occur in the network and application layers such as (SIDDoS, HTTP Flood, Smurf and UDP Flood). This work incorporates various well-known classification techniques: Naïve Bayes, Multilayer Perceptron (MLP), and Support Vector Machine (SVM) and Decision trees.
Authors and Affiliations
Irfan Sofi, Amit Mahajan, Vibhakar Mansotra
Keywords
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