Deep Learning Performance Evaluation Model for Enhancing Network Intrusion Detection Systems

Abstract

Network intrusion detection systems (NIDSs) play an important role in protecting network infrastructure from cyber threats. Traditional NIDS often rely on signature-based or rule-based methods, which can contention to detect incoming strong attacks. Deep learning techniques have emerged as a promising method for improving NIDS due to their ability to learn complex structures and features from raw network traffic data. In this paper, a performance evaluation model for enhancing network intrusion detection is proposed. Deep learning methods including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Attention Mechanisms, and Deep Neural Networks are investigated in the proposed model. Furthermore, the proposed model discusses the challenges and opportunities associated with the implementation of deep learning-based NIDS. These challenges include scalability, interpretability, and adversarial attacks. The performance of the proposed model is examined across numerous datasets that are real-world. The experimental results prove the efficiency to deep learning techniques and their capabilities with NIDS for enhancing the detection capability. Finally, the empirical results show that CNN outperforms the other competitive models for network intrusion detection in the face of cyber threats and in front of the ongoing.