Intrusion Detection in Internet of Things using Convolutional Neural Networks

Abstract

Internet of Things (IoT) has become a popular paradigm to fulfil needs of the industry such as asset tracking, resource monitoring and automation. As security mechanisms are often neglected during the deployment of IoT devices, they are more easily attacked by complicated and large volume intrusion attacks using advanced techniques. Artificial Intelligence (AI) has been used by the cyber security community in the past decade to automatically identify such attacks. However, deep learning methods have yet to be extensively explored for Intrusion Detection Systems (IDS) specifically for IoT. Most recent works are based on time sequential models like LSTM and there is short of research in CNNs as they are not naturally suited for this problem. In this article, we propose a novel solution to the intrusion attacks against IoT devices using CNNs. The data is encoded as the convolutional operations to capture the patterns from the sensors data along time that are useful for attacks detection by CNNs. The proposed method is integrated with two classical CNNs: ResNet and EfficientNet, where the detection performance is evaluated. The experimental results show significant improvement in both true positive rate and false positive rate compared to the baseline using LSTM.

A New Approach to Improve CNN Performance in Anomaly Detection for Iot Networks Based on the Algorithm Adaboost

“…CNN can learn multiple levels of features from a large amount of unlabeled data. Therefore, the application perspective of CNN in network intrusion detection is vast (Li, Y., Xu, Y., et al, 2020). (Li, Y., Xu, Y., et al, 2020).…”

“…Therefore, the application perspective of CNN in network intrusion detection is vast (Li, Y., Xu, Y., et al, 2020). (Li, Y., Xu, Y., et al, 2020).…”

“…Layers are fully connected, and there are no connections between nodes in the same layer. Therefore, traditional neural networks have many problems that need to be solved (Li, Y., Xu, Y., et al, 2020).…”

“…More than 95 works on the subject were surveyed, spanning different themes related to security issues in IoT environments (da Costa, K. A., Papa, J. P., et al, 2019). Li et al (2020). "Robust detection for network intrusion of industrial IoT based on multi-CNN fusion."…”

“…This work contributes to the data security of industrial IoT (Li, Y., Xu, Y., et al, 2020). Kodyš and et al (2021). "Intrusion Detection in Internet of Things using Convolutional Neural Networks."…”