Performance Analysis of Hybrid Cloud Intrusion DetectionModel by Using Supervised Machine Learning BasedClassification Algorithms

Cloud Computing refers to an Internet-based infrastructure that delivers shared resources, software, and ‎information to computers and other devices on an on-demand basis. However, it faces numerous security ‎challenges, including issues related to availability, data confidentiality, integrity, and access control. ‎Additionally, Cloud Computing is vulnerable to various conventional attacks. Traditional security systems ‎are inadequate to protect Cloud services from these diverse threats. In the realm of Cloud Computing, ‎Intrusion Detection refers to the identification and management of unauthorized access, harmful actions, ‎and potential security threats. Intrusion Detection Systems (IDS) serve as security mechanisms that monitor ‎network traffic and event logs to detect any anomalous activities. The Cloud Computing (CC) environment ‎necessitates the implementation of specific Intrusion Detection Systems to safeguard each machine from ‎potential attacks. Machine Learning and Deep Learning algorithms enhance the accuracy of Intrusion ‎Detection Systems over time, leading to a decrease in both false positives and false negatives. In the hybrid ‎phase, the implementation of Naïve Bayes and Decision Tree algorithms resulted in an impressive ‎accuracy rate of 99.71%. Future research should explore additional combinations of hybrid models to ‎achieve greater efficiency across all performance metrics‎.