A learning-based hybrid framework for detection and defence of DDoS attacks Online publication date: Sat, 18-Mar-2017
by T. Subbulakshmi
International Journal of Internet Protocol Technology (IJIPT), Vol. 10, No. 1, 2017
Abstract: Distributed denial of service (DDoS) attacks are those which deplete the valuable resource available for the legitimate user and reduces the business value of any web service provided. This sort of cyber-attacks has to be detected and respective actions have to be taken on them. An integrated detection and defensive mechanism is proposed in this paper to generate and detect DDoS attacks using machine learning algorithms such as back propagation neural network (BPNN), self-organising map (SOM) and enhanced support vector machine (ESVM) and to identify the real IP address of the spoofed attack source using the entropy-based defensive mechanism. The detection and defence mechanism are found to be effective in identifying the attack source with 99% accuracy using ESVM and response time of less than two seconds using the entropy-based tracing scheme. The real source of attacks is filtered using the IP tables to defend the DDoS attacks.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Internet Protocol Technology (IJIPT):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook