Attack Detection in Fog Layer for IIoT Based on Machine Learning Approach
Mareska Pratiwi Maharani, Philip Tobianto Daely, Jae Min Lee, Dong‐Seong Kim · 2020
In Industrial internet of things(IIoT), the infrastructure and the technology has been improved a lot throughout times. With those improvements, the threats and attacks are also growing rapidly to become more various and advanced attacks. One of the weakest parts in IIoT infrastructure is in the cloud layer that can cause the system failure, but it can reduce the possibility by controlling and maximizing the ability in the fog layer as its near to the edge of devices. In this paper, attack detection in fog computing framework with several machine learning algorithms to efficiently detecting malicious activities is proposed. The evaluation performed by using KDD Cup'99 dataset and compared by using Decision Tree, K-Means, and Random Forest algorithms.