An Effective FOG Computing Based Distributed Forecasting of Cyber-Attacks in Internet of Things

Abstract

Existing cloud based security procedures are insufficient to manage the ever-increasing assaults in IoT due to the volume of data generated and the processing latency. IoT applications are vulnerable to cyberattacks, and some of these assaults might have catastrophic results if not stopped or mitigated quickly enough. As a result, IoT calls for self-protect security systems that can automatically interpret attacks in IoT traffic and efficiently handle the attack situation by activating the proper response quickly. Fog computing satisfies this need because it can embed the intelligent self-protection mechanism in the distributed fog nodes, allowing them to swiftly deal with the assault scenario and safeguard the IoT application with little in the way of human interaction. At the fog nodes, the forecasting method employs distributed Gaussian process regression. The cyber-attack may be predicted more quickly and with less mistake for both low- and high-rate attacks thanks to the local forecasting about the IoT traffic characteristics at fog node. One of the fundamental necessities of an IoT security mechanism is the ability to forecast attacks in a timely manner with a high degree of accuracy, and the simulation results highlight this fact.