Energy-Efficient Bio-Inspired Hybrid Deep Learning Model for Network Intrusion Detection Based on Intelligent Decision Making
Keywords:
Network Security, Bio-Inspired Algorithms, Hybrid Deep Learning, Energy Efficiency, Intelligent Decision Making, Resource Optimization, Computational Efficiency, Anomaly Detection, Deep LearningAbstract
This study presents an energy-efficient, hybrid deep learning model for network intrusion detection that takes its cues from biology. As a result, intelligent decision-making is included into the improvement of security infrastructures. The model's high accuracy is shown by the fact that it performs well across a variety of metrics, including TPR, precision, and F-Measure, thanks to the use of deep learning and techniques inspired by biology. Despite the fact that there is some unpredictability in FPR and FNR, the data demonstrate that the model is capable of providing a long-term and intelligent response to the ever-changing cybersecurity threats
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