Implementing Flash Event Discrimination in IP Traceback using Shark Smell Optimisation Algorithm

Abstract

Denial of service attack and its variants are the largest ravaging network problems. They are used to cause damage to network by disrupting its services in order to harm a business or organization. Flash event is a network phenomenon that causes surge in normal network flow due to sudden increase in number of network users, to curtail the menace of the Denial of service attack it is pertinent to expose the perpetrator and take appropriate action against it. Internet protocol traceback is a network forensic tool that is used to identify source of an Internet protocol packet. Most of presently available Internet protocol traceback tools that are based on bio-inspired algorithm employ flowbased search method for tracing source of a Denial of service attack without facility to differentiate flash event from the attack. Surge in network due to flash event can mislead such a traceback tool that uses flow-based search. This work presents a solution that uses hop-by-hop search with an incorporated discrimination policy implemented by shark smell optimization algorithm to differentiate the attack traffic from other traffics. It was tested on performance and convergence against an existing bio-inspired traceback tool that uses flowbase method and yielded outstanding results in all the tests.

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