A Robust Security Task Offloading in Industrial IoT-Enabled Distributed Multi-Access Edge Computing

The rapid increase in the Industrial Internet of Things (IIoT) use cases plays a significant role in Industry 4.0 development. However, IIoT systems face resource constraints problems and are vulnerable to cyberattacks due to their inability to implement existing sophisticated security systems. One way of alleviating these resource constraints is to utilize multi-access edge computing (MEC) to provide computational resources at the network edge to execute the security applications. To provide resilient security for IIoT using MEC, the offloading latency, synchronization time, and turnaround time must be optimized to provide real-time attack detection. Hence, this paper provides a novel adaptive machine learning-based security (MLS) task offloading (ASTO) mechanism to ensure that the connectivity between the MEC server and IIoT is secured and guaranteed. We explored the trade-off between the limited computing capacity and high cloud computing latency to propose an ASTO, where MEC and IIoT can collaborate to provide optimized MLS to protect the network. In the proposed system, we converted the MLS task offloading and synchronization problem into an equivalent mathematical model, which can be solved by applying Markov transition probability and clock offset estimation using maximum likelihood. Our extensive simulations show that the proposed algorithm provides robust security for the IIoT network with low latency, synchronization accuracy, and energy efficiency.