A Statistical Approach to Classify and Identify DDoS Attacks using UCLA Dataset
Journal Title: International Journal of Advanced Research in Computer Engineering & Technology(IJARCET) - Year 2013, Vol 2, Issue 5
Abstract
Nowadays, Internet is the most well-known and popular thing that is widely used by human beings. It is also an essential part of human life and provides the best and fast communication medium. As many people widely used Internet, i.e., so many user of Internet is increasing, network security attacks are also increasing. Among these security attacks, DDoS (Distributed Denial of Service) is the most serious attack for network security. This attack is not direct attack because it does not enter directly to the system and does not damage it. In this paper, the two proposed algorithms can be classified and identified what types of DDoS attacks by using UCLA data set. At first, packet classification algorithm classifies normal and attack from incoming packets. To get more accurate result, K-NN classifier estimates normal or attacks from results of packet classification algorithm. Finally, the proposed algorithm classifies and identifies types of DDoS attacks.
Authors and Affiliations
Thwe Thwe Oo , Thandar Phyu
Keywords
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