Lightweight Deep Learning Approach for Intelligent Intrusion Detection in IoT Networks

Intrusion detection system (IDS) is designed to analyze and monitor the network traffic to identify unauthorized access or attacks in an Internet of Things (IoT). IDS assists in protecting IoT devices and networks by recognizing malicious activities and preventing potential breaches. However, IDS struggled with accurately identifying cyber threats in IoT environments due to the presence of redundant features that lead to overfitting and reduced generalization. Feature selection is essential to enhance model robustness, remove misleading information, and ensure that only the most appropriate features contribute to the detection process that enhances overall performance and security. This research proposes a feature selection–based Di-Strategy Black-Winged Kite Algorithm (DS-BWKA) to detect the intrusion system in an IoT environment. In BWKA, tent mapping and dynamic cosine learning factor (DCLF) are incorporated as a di-strategy to initialize population and enhance global search ability for feature selection that enhances model performance. The proposed DS-BWKA–LSTM framework effectively addressed the class imbalance by selecting highly discriminative features and learning temporal patterns that enhanced the detection of minority attack classes. Long short-term memory (LSTM) is employed to identify IDS and capture temporal dependencies in network traffic, allowing for better detection of sequential attack patterns. The proposed DS-BWKA achieves a high accuracy of 99.56%, 99.67%, and 99.94% for CSE-CIC-IDS2018, ToN-IoT, and IoT23 datasets compared with existing methods like Long short-term memory autoencoders (LSTM-AE) and secured automatic two-level intrusion detection system (SATIDS), respectively.