Energy-Efficient Bio-Inspired Hybrid Deep Learning Model for Network Intrusion Detection Based on Intelligent Decision Making - Hegde, Sandeep Kumar; William, P.; Basvant, Mule Shrishail; Deepak, A.; Badhoutiya, Arti; Rao, A L N; Srivastava, Amit; Shrivastava, Anurag (International Journal of Intelligent Systems and Applications in Engineering, 2024)
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