Detection of DDoS Attack using Optimized Hop Count Filtering Technique

Autor: M. K. Priyan, E. Vishnu Balan, G. Usha Devi, C. Gokul Nath, M. Chandrasekhar
Rok vydání: 2015
Předmět:
Zdroj: Indian Journal of Science and Technology. 8
ISSN: 0974-5645
0974-6846
DOI: 10.17485/ijst/2015/v8i1/83981
Popis: Background: The Distributed Denial of Service (DDoS) attack is one of the most recent and most vulnerable attacks that can happen to a web server to crash or stop providing survive. Many papers have been proposed to resolve this attack and have resolved to a certain extent but it is very difficult to explore and solve every loophole since Internet is a vast domain. Methods: In Denial of Services (DoS) attack, the attacker uses up all the resources available to the server so that the legitimate user does not get the actual service. The well-established network infrastructure is the backbone to carry out this attack. DoS attacks are very severe when it happens to important servers such as banking and government websites. In this paper, we have proposed a new optimized mechanism which could be more reliable than the existing models. The traffic generated by an IP packet is recorded and a window matrix is generated. This matrix consists of number of packets from each IP during a given window and the maximum packets received from an IP will be decided. Then, this result is used as an input to the Hop Count Filtering (HCF) algorithm, the packets can be distinguished as legitimate and attacker packets. Results: This paper proposes a technique to detect Distributed Denial of Service attack by using window matrix and optimized HCF filtering technique. Finally, the algorithm says that the packets are legitimate IP packets and spoofed IP packets along with their IP addresses. Based on this result, we conclude to accept or discard the packets. Applications: This paper combines the features of existing methods to track the IP address as in tracking applications and it is also used to discard the unwanted packets based on IP address.
Databáze: OpenAIRE