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

Atef Obeidat(1*)
(1) Al-Balqa Applied University
(*) Corresponding Author

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.

Department of Information Technology,

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Institute of Computing, International Journal of Communication Networks and Information Security (IJCNIS)               ISSN: 2073-607X (Online)