Proceedings of International Conference on Applied Innovation in IT
2026/03/31, Volume 14, Issue 1, pp.715-726

An Improved Framework Using LDA and LSTM with RSO for Intrusion Detection in IoT


Hussein Ali Manji Nasrawi, Hasanain Salim Kadhim, Rifaq Adil Majeed and Hussain Ali Hussain


Abstract: This paper proposes a lightweight and efficient intrusion detection framework for Internet of Things (IoT) and Wireless Sensor Network (WSN) environments by integrating Linear Discriminant Analysis (LDA), Long Short-Term Memory (LSTM) networks, and Rat Swarm Optimization (RSO). The proposed approach first applies LDA to reduce high-dimensional network traffic data into a compact and discriminative feature space, improving class separability while reducing computational complexity. Subsequently, an LSTM-based deep learning model is employed to capture temporal dependencies in network traffic for multi-class intrusion classification. To enhance model performance and stability, RSO is utilized to optimize critical LSTM hyperparameters, including learning rate, batch size, and hidden units, enabling an effective balance between exploration and exploitation during training. The framework is evaluated on two benchmark datasets, CIC-IDS2018 and TON_IoT, representing diverse and realistic IoT attack scenarios. Experimental results demonstrate that the proposed model achieves high detection performance, with accuracy exceeding 99%, along with strong precision, recall, and F1-scores, and a low false alarm rate. Furthermore, ROC-AUC and PR-AUC values confirm robust discriminative capability under imbalanced data conditions. Deployment experiments on edge devices such as Raspberry Pi 4 and NVIDIA Jetson Nano show low inference latency and moderate resource consumption, validating the suitability of the proposed framework for real-time intrusion detection in resource-constrained IoT environments.

Keywords: Wireless Sensor Networks, Intrusion Detection, Long Short-Term Memory (LSTM), Rat Swarm Optimization (RSO), Feature Selection (LDA).

DOI: Under indexing

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