ALRN-RCS: Advanced Approach to Network Intrusion Detection Using Attention Long-Term Recurrent Networks and Chaotic Optimization

Abstract

Detecting intrusions within a network is essential for protecting digital environments; it encompasses the observation and analysis of network traffic to recognize and counteract unauthorized or malicious activities. Conventional methods in network intrusion detection face several challenges such as polymorphic and evasive attacks, scalability issues, and anomaly-based complexity. To address these complexities, this paper proposed a novel method named Attention Long-term Recurrent Network-based Random Chaotic Sine (ALRN-RCS) algorithm for network intrusion detection. In this study, Long Short-Term Memory (LSTM) is utilized to capture complex patterns in network traffic and identify anomalous activities. Also, the attention-based Recurrent Neural Network (RNN) is employed to focus on relevant features within network traffic and enable precise intrusion detection. In this paper, we have incorporated the Chaotic Chimp Sine Cosine optimization algorithm, employing a random update strategy, to optimize hyperparameters and improve the overall efficacy of the proposed approach and the study conducted experiments on the datasets namely the UNSW NB-15, Network Intrusion Detection dataset, and the Segmented Image-based Network Intrusion Detection (SIDD) dataset. Diverse assessment criteria, including accuracy, F1-score, recall, AUC-ROC, and precision, are employed to assess the effectiveness of the ALRN-RCS method and to draw comparisons with established methodologies. The experimental results depict the effectiveness of the ALRN-RCS method for addressing the dynamic and sophisticated nature of modern cyber threats.