Lightweight intrusion detection system using multiscale attention 1D CNN for large scale internet of things

The Internet of Things (IoT) and its applications are increasing rapidly over the years. Due to the wide variety of IoT applications, cyber attackers are exploring strong attacking methods and patterns to damage the IoT networks in real-time applications even if the IoT network is secure. To protect the IoT networks, it is essential to design and develop a real-time intrusion detection system that can detect the attacking patterns and methods and prevent them immediately. To achieve this goal, we have proposed an intrusion detection system using multiscale attention 1D convolutional neural networks for efficient detection of all major attacks. Our proposed mechanism integrates multi-scale convolutional kernels with a dual attention mechanism for computationally efficient intrusion detection. This mechanism has extracted spatial features to discriminate against the normal and malicious IoT traffic patterns. The experiment evaluation of the proposed work has tested two datasets, UNSW-NB15 and UM-NIDS 24, to evaluate its inference efficiency and intrusion detection capability. The proposed IDS has demonstrated the best performance in comparison to state-of-the-art models and achieved an accuracy of 91.03% on the UM-NIDS and an accuracy of 99.37% on the UNSW-NB15. Based on the experimental results and analysis, we can conclude that the proposed IDS with MA-1D-CNN is a lightweight, feature-efficient, and high-precision model for the real-time attack detection in large-scale IoT networks.