An Intelligent Intrusion Detection Framework for Cyber Security in Hybrid Network Environments
Abstract
In the era of contemporary data traffic routing, the concept of Intrusion Detection Systems (IDS) is substantially utilized. However, the efficacy of IDS is often decreased because of the reality that high-concentration traffic postfixes, sophisticated cyber criminals, and more and more stringent demands are tending to decrease in resource-limited environments. The paper presents the enhanced intrusion detection system based on deep learning architecture, which can be flexible, adaptive and as well maintain the high detection capability with confidence under changing or to-be changed network settings. The objectives of this and aforementioned also address the issue of avoiding strong overtting behavior by models during the transfer learning and even rich feature representation through the first-stage operation: moving to address and ideally preventing attacks rather than supporting other attacks. The work is tailored to the deployment of the light-weight and adaptive IDS design which is supposed to be large enough to work in real time on low-powered devices such as IoTs and edge devices that are nondominated in energy and computationally less demanding. Real-time adaptability of the model will be examined through operational deployment simulations. It is also expected that such simulations would take into account latency, throughput, and energy consumption of the IDS model. On the one hand, In a stage nested within the very last period of this research, the IDS model has been merged with Explainable AI technologies; now LIME and SHAP are also preserved to improve the interpretability of the model decisions and the level of decision-making. What kind of feature attributions are made with the intrusion data? How is Interpretability of the model evaluated in terms of fidelity, comprehensibility, and expert belief? Therefore, all the above-mentioned events will be a perfect example of how the technologically ingrained tasks, particularly in the technical discipline of security studies, can be wrapped into the very cognitive resource of human beings.