An efficient optimized fuzzy inference system based intrusion detection in cloud environment

Abstract

Security incidents namely, Denial of service (DoS), scanning, virus, malware code injection, worm and password cracking are becoming common in a cloud environment that affects the company and may produce an economic loss if not detected in time. These problems are handled by presenting an intrusion detection system (IDS) in the cloud. But, the existing cloud IDSs affect from low detection accuracy, high false detection rate and execution time. To tackle these issues, in this paper, gravitational search algorithm based fuzzy Inference system (GSA-FIS) is developed as intrusion detection. In this approach, fuzzy parameters are optimized using GSA. The proposed consist of two modules namely; Possibilistic Fuzzy C-Means (PFCM) algorithm based clustering, training based on GSA-FIS and testing process. Initially, the incoming data are pre-processed and clustered with the help of PFCM. PFCM is detecting the noise of fuzzy c-means clustering (FCM), conquer the coincident cluster problem of Possibilistic Fuzzy C-Means (PCM) and eradicate the row sum constraints of fuzzy Possibilistic c-means clustering (FPCM). After the clustering process, the clustered data are given to the optimized fuzzy Inference system (OFIS). Here, normal and abnormal data are identified by the Fuzzy score, while the training is done by the GSA through optimizing the entire fuzzy system. In this approach, four types of abnormal data are detected namely, probe, Remote to Local (R2L), User to Root (U2R), and DoS. Simulation results show that the performance of the proposed GSA-FIS based IDS outperforms that of the different scheme in terms of precision, recall and F-measure.