A Machine Learning based CIDS Model for Intrusion Detection to Ensure Security within Cloud Network

Abstract

Cloud computing has evolved rapidly in recent years, and security concerns have gained prominence. Cloud computing offers the incredible capacity to provide inexpensive, easy-to-manage, flexible, adaptive, and powerful assets on the move via the Internet. Through effective and shared utilization, cloud computing maximizes the potential of hardware resources. Many issues in cloud computing raise are concerned regarding data and service availability. Numerous security services are needed to enhance cloud security for both users and service providers. One of the most essential research to establish security within cloud to avoid load imbalance situation through robust Cloud Intrusion Detection Systems (CIDS). Denial of service, scanning, malware code injection, virus, worm, and password cracking is prevalent cloud security issues. These attacks might jeopardize the company's reputation and inflict financial damage if not identified in time. Protecting the cloud from these kinds of threats, early detection and true prediction of such threats is the key goal of our proposal through this paper. It has been observed from earlier research proposals that dimensionality reduction is applied in conjunction with Data Mining (DM) and ML approaches found more perfomant in order to create such a robust model to ensure cloud network, authors suggested an CIDS by selecting appropriate features utilizing pertinent feature reduction approaches, then feeding this subset of data through the ML tool. 
 The simulation of the suggested model through ‘Python’ using ‘Scikit-Learn’ tool. Outcome of simulation experimental has been measured using various performance evaluation metric such as Precision, Recall, F-Score, Detection Ratio, RoC-curve etc. using KDDcup99 dataset as a benchmark. Simulation results from our suggested methodology were found more effective and comparable to several other existing methodologies.
 It has observed that ML based proposed model found capable enough to protect the cloud-based information by uncovering suspicious user behaviour interns as an outcome, secure cloud network against the threat and also found more performant in true prediction and early intrusion detection resultant reduction of computational cost.