ADAPTIVE INTRUSION DETECTION SYSTEMS USING MACHINE LEARNING IN CLOUD ENVIRONMENTS - Ravindrakumar (ShodhKosh: Journal of Visual and Performing Arts, 2023)
There are considerable hurdles that must be overcome in order to maintain adequate security against cyber-attacks in cloud settings because of their dynamic and dispersed nature. A proactive method to identifying and mitigating new threats in real time is provided by adaptive intrusion detection systems (IDS) that make use of machine learning (ML). The design and operation of adaptive intrusion detection systems (IDS) that are adapted for cloud platforms are investigated in this study. Particular attention is paid to the utilisation of supervised, unsupervised, and reinforcement learning methods. It investigates the benefits of adaptive intrusion detection systems (IDS) in terms of identifying abnormal behaviours, minimising the number of false positives, and adapting to shifting threat environments. Several important factors, including the selection of features, the quality of the dataset, and the optimisation of the algorithm, are covered. In addition to this, we investigate the possibility of integrating adaptive intrusion detection systems with cloud-native technologies such as serverless computing and containers. Performance measurements and comparative evaluations demonstrate that machine learning-based intrusion detection systems are more effective than older techniques. Additionally, the study addresses issues such as scalability, data privacy, and adversarial assaults, and it proposes viable methods to improve dependability. The adaptive intrusion detection system (IDS) is an essential component of cloud security methods since it enables continuous monitoring and reaction to complex kinds of attacks.