Novel Approach for Intrusion Detection Using Simulated Annealing Algorithm Combined with Hopfield Neural Network

Abstract

With the continued increase in Internet usage, the risk of encountering online threats remains high. This study proposes a new approach for intrusion detection to produce better outcomes than similar approaches with high accuracy rates. The proposed approach uses Simulated Annealing algorithms [1] combined with Hopfield Neural network [2] for supervised learning to improve performance by increasing the correctness of true detection and reducing the error rates as a result of false detection. The proposed approach is evaluated on an intrusion detection data set called KDD99[3]. Experimental tests demonstrate the potential of the proposed approach to rapidly detect high precision and efficiency intrusion behaviors. The proposed approach offers a 99.16% accuracy rate and a 0.3% false-positive rate.