Intrusion detection using multi-objective evolutionary convolutional neural network for Internet of Things in Fog computing

• Published in: Knowledge-based systems 2022-05, Vol.244, p.108505, Article 108505
• Main Authors: Chen, Yi, Lin, Qiuzhen, Wei, Wenhong, Ji, Junkai, Wong, Ka-Chun, Coello, Carlos A. Coello
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial neural networks; Cloud computing; Convolutional neural network; Evolutionary algorithms; Fog computing; Internet of Things; Intrusion detection system; Intrusion detection systems; Machine learning; Mathematical models; Multi-objective optimization; Network latency; Neural networks; Neuroevolution; Nodes; Parameter modification; Process parameters; Tuning
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2022.108505

Our world is moving fast towards the era of the Internet of Things (IoT), which connects all kinds of devices to digital services and brings significant convenience to our lives. With the rapid increase in the number of devices connected to the IoT, there may exist more network vulnerabilities, resulting in more network attacks. Under this dynamic IoT environment, an effective intrusion detection system (IDS) is urgently needed to detect attacks with low-latency and high accuracy. A number of promising IDSs have been proposed based on deep learning (DL) techniques, but they need to do parameter tuning under different environments, which is very time-consuming. To alleviate this problem, this paper proposes a multi-objective evolutionary convolutional neural network for intrusion detection system, called MECNN, which is run on the fog nodes of Fog computing on IoT. In this approach, convolutional neural network (CNN) is used as the classifier to detect intrusions and the multi-objective evolutionary algorithm based on decomposition (MOEA/D) algorithm is modified to evolve the CNN model, which greatly simplifies the parameter tuning process of DL. To be specific, a novel encoding scheme is first proposed to transform the topological architecture of CNN into a chromosome of MOEA/D and then the two conflicting objectives, i.e., detection performance and model complexity of the CNN model, are simultaneously optimized by MOEA/D, which can obtain a number of IDSs with various detection performance and model complexities. Then, the most suitable MECNN model can be deployed in different fog nodes of Fog computing, providing low-latency and high-accuracy intrusion detection for IoT. Finally, the experimental studies are conducted on two popular datasets (AWID and CIC-IDS2107), which have validated that our MECNN model can improve detection performance and robustness to better protect the IoT when compared to other state-of-the-art IDSs.