Effective analysis of malware detection in cloud computing

Abstract Cloud services are relied upon to be always on and have a significant nature; acordingly, security and versatility are progressively imperative perspectives. Accessing the cloud environment is performed via internet services in which information stored on cloud environment are easier to both internal and external Malwares. To detect intruders, a variety of malware detection systems were focused on machine learning techniques, which are not delivering major results based on detection rate. To overcome this drawback, we propose novel consolidated Weighted Fuzzy K-means clustering algorithm with Auto Associative Neural Network(WFCM-AANN). Our proposed classifier successfully identifies the malwares and it is obvious that the proposed framework can identify the anomalies with high detection precision thereby outperforming existing classifiers.