Feature Selection using the concept of Peafowl Mating in IDS
This addresses cloud security vulnerabilities for cloud service providers and users, but appears incremental as it applies a bio-inspired optimization variant to an existing problem.
The paper tackles the problem of intrusion detection in cloud computing by proposing a feature selection algorithm based on peafowl mating behavior to reduce data size for efficient IDS operation, achieving better performance on NSL-KDD and Kyoto datasets.
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.