Energy-Aware AI and Machine Learning Approaches for Next-Generation IDS
Keywords:
Energy-aware Intrusion Detection Systems., Machine Learning in Cybersecurity, Resource-efficient AI Models, Low-Power IDS Architecture, Intelligent Threat DetectionAbstract
Due to the increasing trend and sophistication of cyber threats, Intrusion Detection Systems (IDS) have formed an important part of the current cybersecurity repertoire. Nevertheless, the computational vocal power claim of Artificial Intelligence (AI) and Machine Learning (ML) models employed in the next-generation IDS has its energy depleting problems, especially in edge and mobile settings. This paper is an exhaustive examination of energy-aware AI and ML methods of improving IDS performance with minimal consumption of power. A new lightweight framework hybridizing ML algorithms and adaptive power management practices has been proposed in the paper to lower the energy overhead to increase detection accuracy. With the help of benchmark datasets (CICIDS2017, NSL-KDD), we measure the performance of multiple ML models (Decision tree, Support Vector machine, Random forest, and Deep Neural Network) with the help of such metrics as energy consumption, detection accuracy, and false-positive rate. To achieve this, the proposed system will be operated on a resource scarce testbed to provide a real world scenario in terms of operational constraints. Experimental results show that some ensemble models reported up to 30 percent saving in energy consumption at a minimal manner in performance when configured with energy-wise scheduling. The paper also discusses how energy-efficient network security appliances affect critical infrastructure, Internet of Things (IoT) devices and Cloud-Native infrastructures. Model behavior and energy-performance trade-offs are illustrated using visual analytics signs (bar charts, pie charts and system diagrams). The study provides a direction to the development of intelligent sustainable IDS that is applicable in next generation network settings where robustness in security is given emphasis and energy-efficient computing. The next step is the investigation of federated learning architectures and edge-based energy optimization in order to extend scalability and efficiency further.
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