Intrusion Detection in Internet of Things using Convolutional Neural Networks

TL;DR

This paper introduces a novel CNN-based approach for IoT intrusion detection, encoding sensor data as images to leverage convolutional patterns, resulting in improved detection performance over traditional LSTM methods.

Contribution

The paper proposes a new CNN-based method for IoT intrusion detection by encoding sensor data as images, demonstrating superior performance over LSTM-based approaches.

Findings

Significant improvement in true positive rate.

Reduction in false positive rate.

Effective use of CNNs like ResNet and EfficientNet for IoT security.

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.