Anomaly Detection System in Cloud Computing Network

Abstract

Security in cloud computing poses higher challenges due to modern day distributed computing architectures. Cloud computing is a quickly developing IT show for the trade and conveyance of various administrations through the Internet. Be that as it may, there are a plenty of security worries in cloud computing which still should be handled (e.g., confidentiality, auditability and Privileged User Access). To recognize and avoid such issues, the Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) are powerful instruments against assaults, for example, SQL Injection. Monitoring such a highly complex network environment needs a system capable of handling passive monitoring efficiently with less human intervene. Intrusion detection is a versatile security paradigm which can avert most of the computer and network related attacks if efficiently employed. In this paper, we propose a modern security monitoring framework capable of detecting security attacks through data intelligence. The results of experiments prove that the average time taken to predict attacks by K-means algorithm is better than Naive Bayes, MLP, and SVM algorithms.