Security Evaluation of Soft Computing Intrusion Detection Systems (Ids) with Neural Networks

Abstract

With the advancement of computer communication technology and the emergence of soft computing networks, many defense strategies have emerged to enhance network security and improve the effectiveness of intrusion detection systems (IDS) against cyber-attacks, including distributed denial of service (DDoS) attacks. This research aims to propose artificial neural network technology as a tool for network security assessment, which contributes to reducing human intervention and increasing the efficiency of detection processes to achieve accurate and fast results. Two data sets were used to train and test the proposed model, and the results showed that the model achieves a detection accuracy of up to 98.194%, with a mean square error (MSE) of 0.161%. These results confirm the effectiveness and efficiency of the technology in quickly detecting and responding to threats, which enhances network security and increases the ability of systems to face cyber challenges.