Feature Selection using the concept of Peafowl Mating in IDS
Partha Ghosh Joy Sharma Nilesh Pandey
Cloud computing has high applicability as an Internet based service that
relies on sharing computing resources. Cloud computing provides services that
are Infrastructure based, Platform based and Software based. The popularity of
this technology is due to its superb performance, high level of computing
ability, low cost of services, scalability, availability and flexibility. The
obtainability and openness of data in cloud environment make it vulnerable to
the world of cyber-attacks. To detect the attacks Intrusion Detection System is
used, that can identify the attacks and ensure information security. Such a
coherent and proficient Intrusion Detection System is proposed in this paper to
achieve higher certainty levels regarding safety in cloud environment. In this
paper, the mating behavior of peafowl is incorporated into an optimization
algorithm which in turn is used as a feature selection algorithm. The algorithm
is used to reduce the huge size of cloud data so that the IDS can work
efficiently on the cloud to detect intrusions. The proposed model has been
experimented with NSL-KDD dataset as well as Kyoto dataset and have proved to
be a better as well as an efficient IDS.