New Improved Training for Deep Neural Networks Based on Intrusion Detection System

All Network intrusion detection is designed for detecting, preventing, and repelling network security breaches and it has become an urgent issue. Maintaining a safe and secure network requires an efficient and flexible solution called an intrusion detection system. This paper reports an advanced intrusion detection method created with a deep learning approach. Evolutionary operators can reduce the probability of stagnation in local solutions due to high local optima avoidance and have thus superseded conventional training algorithms, such as back propagation (BP). Combining a deep neural network (DNN) and an evolutionary algorithm (EA) may solve problems or outperform DNN in solving existing problems. We develop a hybrid training method that combines simulated annealing (SA) and BP to improve the performance of DNN (SABP-DNN). The NSL-KDD dataset is used to verify the accuracy and efficiency of the proposed method. The proposed method is also compared with the original DNN based on PB (PB-DNN) and DNN based on SA (SA-DNN). We confirm that the proposed method presents a strong potential to become an alternative solution to IDS through experiments and comparisons with existing methods.