Affiliations: [a]
Department of Computer Science, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India
| [b]
Department of Information Technology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, IndiaIndia
Correspondence:
[*]
Corresponding author. S. Nithinsha, PHD Scholar, Department of Computer Science, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India. E-mail: nithinshas9746@gmail.com.
Abstract: The objective of the research work is to propose an intrusion detection system in a cloud environment using K-Means clustering-based outlier detection. In the open access and dispersed cloud architecture, the main problem is security and confidentiality because these are easily susceptible to intruders. Intrusion Detection System (IDS) is a commonly used method to identify the various attacks on the cloud which is easy to access from a remote area. The existing process can’t provide the data to transmit securely. This work describes and notifies the modernly established IDS and alarm management methods by giving probable responses to notice and inhibit the intrusions in the cloud computing environment and to overcome the security and privacy issue. Proposed K-means Clustering based Outlier Detection (KmCOD) is used to detect the intruders and efficiently secure the data from malicious activity, where it is formulated respectively to increase the trustworthiness of the system by using applying intrusion detection techniques to virtual machines thus keeping the system safe and free from intrusion also provides system reliability. The parametric measures such as the detection rate, trace preprocessing, and correctly identified and incorrectly identified malicious activity are chosen. The performance analysis shows the accuracy of outlier detection as 81%, detection rate achieves 76%, packet arrival rate reaches 79%, pre-processing trace achieves 74%, and malicious activity rate of 21%.
Keywords: Cloud, intrusion detection, data security, clustering algorithm, outlier detection, data privacy, anomaly
