Towards Secure Edge Computing: Advanced Machine Learning Techniques for Detecting Malicious Computing Tasks
Mshari Aljumaie, Tran Viet Khoa, Chi-Hieu Nguyen, Dinh Thai Hoang, Diep Ngoc Nguyen, Eryk Dutkiewicz · 2024
In this work, we propose a novel machine learning empowered intrusion detection for Mobile Edge Computing (MEC) networks. Unlike most of the research works that focus on detecting attacks at the network layer, such as IP spoofing and Denial of Service (DoS) attacks, we aim to detect attacks/threats at the application layer, especially attacks caused by malicious codes embedded in offloaded computing tasks. This is an emerging issue in MEC networks as more and more MEC services allow MEC users to offload their computational tasks to the edge nodes to process. Yet, this is a very challenging problem in MEC, as data at the application layer is often complex and challenging to interpret, making anomaly detection difficult. Therefore, we first propose an effective solution to transfer data from the original offloading file to a new form, i.e., images, to make it more effective for the detection process. After that, a Convolutional Neural Network (CNN) and a collaborative learning process are proposed to learn information from training data (i.e., transformed images) and, at the same time, share the learned knowledge (i.e., trained models) together to improve the global accuracy in detecting attacks. Simulation results show that our approach can detect attacks with an accuracy of approximately 90%.