Integrating Salp Swarm Optimization with LSTM for Intrusion Detection

Over time, intrusion detection systems have grown essential in ensuring network security by identifying malicious activities within network traffic and alerting security teams. Machine learning techniques have been employed to develop these systems. However, these approaches often face challenges related to low accuracy and high false alarm rates. Deep learning models like Long Short-Term Memory (LSTM) are utilized to address these limitations. Despite their potential, LSTM models require numerous iterations to achieve optimal performance. This study introduces an enhanced version of the LSTM algorithm, termed ILSTM, which integrates the Salp Swarm Optimizer (SSO) to boost accuracy. The ILSTM framework was applied to construct an advanced intrusion detection system capable of binary and multi-class classifications. The approach comprises two phases: The first involves training a standard LSTM model to initialize its weights. In contrast, the second employs the SSO hybrid optimization algorithm to fine-tune these weights, enhancing overall performance. The effectiveness of the ILSTM algorithm and the intrusion detection system was assessed using two publicly available datasets, NSL-KDD and LITNET-2020, across nine performance metrics. Results demonstrated that the ILSTM significantly outperformed the conventional LSTM and other comparable deep learning models in accuracy and precision. Specifically, the ILSTM achieved an accuracy of 93.09% and a precision of 96.86%, compared to 82.74% accuracy and 76.49% precision for the standard LSTM. Moreover, the ILSTM exhibited superior performance on both datasets and was statistically validated to be more robust than LSTM. Furthermore, the ILSTM excelled in multiclass intrusion classification tasks, effectively identifying intrusion types.