A Novel Hybrid Intrusion Detection System (IDS) for the Detection of Internet of Things (IoT) Network Attacks

Nowadays, IoT has been widely used in different applications to improve the quality of life. However, the IoT becomes increasingly an ideal target for unauthorized attacks due to its large number of objects, openness, and distributed nature. Therefore, to maintain the security of IoT systems, there is a need for an efficient Intrusion Detection System (IDS). IDS implements detectors that continuously monitor the network traffic. There are various IDs methods proposed in the literature for IoT security. However, the existing methods had the disadvantages in terms of detection accuracy and time overhead. To enhance the IDS detection accuracy and reduces the required time, this paper proposes a hybrid IDS system where a pre-processing phase is utilized to reduce the required time and feature selection as well as the classification is done in a separate stage. The feature selection process is done by using the Enhanced Shuffled Frog Leaping (ESFL) algorithm and the selected features are classified using Light Convolutional Neural Network with Gated Recurrent Neural Network (LCNN-GRNN) algorithm. This two-stage method is compared to up-to-date methods used for intrusion detection and it over performs them in terms of accuracy and running time due to the light processing required by the proposed method.