Fog computing facilitates the placement of data at the network’s edge for processing, which effectively reduces energy consumption and enhances efficiency. However, the limited resources inherent in fog computing render it vulnerable to extensive volumes of high-dimensional anomalous traffic. This study proposes a novel feature selection algorithm called filtered interaction maximum relevance minimum redundancy, which incorporates feature interaction to enable effective intrusion detection in fog computing. Through feature selection, the algorithm downscales the high-dimensional data captured in the fog nodes to reduce redundant features. The experimental results show that the parsimonious feature set obtained using the algorithm in this paper improves the classification accuracy while reducing the execution time compared to the original dataset.

Yimeng Wang, Zongpu Jia, Xiaoyan Pang, Shan Zhao. Feature Interaction Based Feature Selection Algorithm for In-trusion Detection. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 3: 1-12. https://doi.org/10.25236/AJCIS.2024.070301.

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