Leveraging Graph-Based Representations to Enhance Machine Learning Performance in IIoT Network Security and Attack Detection

Abstract

In the dynamic and ever-evolving realm of network security, the ability to accurately identify and classify portscan attacks both inside and outside networks is of paramount importance. This study delves into the underexplored potential of fusing graph theory with machine learning models to elevate their anomaly detection capabilities in the context of industrial Internet of things (IIoT) network data analysis. We employed a comprehensive experimental approach, encompassing data preprocessing, visualization, feature analysis, and machine learning model comparison, to assess the efficacy of graph theory representation in improving classification accuracy. More specifically, we converted network traffic data into a graph-based representation, where nodes represent devices and edges represent communication instances. We then incorporated these graph features into our machine learning models. Our findings reveal that incorporating graph theory into the analysis of network data results in a modest-yet-meaningful improvement in the performance of the tested machine learning models, including logistic regression, support vector machines, and K-means clustering. These results underscore the significance of graph theory representation in bolstering the discriminative capabilities of machine learning algorithms when applied to network data.

Blockchain-Assisted Machine Learning with Hybrid Metaheuristics-Empowered Cyber Attack Detection and Classification Model

“…The widely different techniques applied for detecting attacks extensively are classified into three main kinds, such as misuse-based, hybrid-based, and anomaly-based identification [12]. In the detection technique of misuse-based cyber attacks, they can be examined with pre-recorded attack signatures, and the technique is utilized significantly for detecting the identified attacks.…”