article · Egyptian Informatics Journal
The growing reliance on connected Internet of Things devices within critical infrastructure creates major cybersecurity risks, particularly Distributed Denial of Service attacks directed at energy hubs. To counter these threats, multiple supervised machine learning algorithms were assessed for their ability to predict incoming attacks. Testing was carried out using the benchmark CICDDOS2019 and KDD-CUP datasets across several classifiers, including Decision Tree, Gradient Boosting, Support Vector Machine, K-Nearest Neighbors, and Random Forest, alongside hybrid combinations. Gradient Boosting emerged as the most effective algorithm, achieving superior accuracy and predictive capabilities, especially on the CICDDOS2019 dataset. Hybrid pairings of Gradient Boosting with either Support Vector Machine or Decision Tree also demonstrated strong detection capabilities, although their precision and recall varied. The findings offer guidance on choosing and tailoring machine learning models to strengthen the resilience of energy infrastructure against evolving digital threats.
Energy hubs rely heavily on connected devices to coordinate power systems, leaving essential infrastructure vulnerable to disruptive cyberattacks. Identifying reliable algorithmic tools to detect digital assaults allows network operators to recognise threats early, protecting vital services from shutdowns caused by malicious network traffic.
This research could support developers of cybersecurity software and operators of energy hub infrastructure seeking automated intrusion detection tools. Because the evaluation relied strictly on benchmark datasets such as CICDDOS2019 and KDD-CUP rather than active physical deployments, the technology sits at an applied testing stage and requires live operational trials before direct commercial implementation.
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With the growing integration of IoT devices in critical infrastructure, cybersecurity threats such as Distributed Denial of Service (DDoS) attacks on Energy Hubs (EH) have become a significant concern. This study aims to address these challenges by evaluating the effectiveness of various supervised machine learning (ML) algorithms in predicting DDoS attacks targeting EH systems through IoT devices. Using the CICDDOS2019 and KDD-CUP datasets, a comprehensive analysis was conducted on several classifiers, including Decision Tree (DT), Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest. The results highlight Gradient Boosting as the most effective model, particularly for the CICDDOS2019 dataset, demonstrating superior accuracy and predictive capability. Additionally, hybrid models combining Gradient Boosting with SVM or DT showed strong performance, though with varying precision and recall. This study provides valuable insights into the selection and tailoring of ML models for specific security challenges, emphasizing the need for ongoing research to enhance the resilience of EH systems and IoT devices against evolving DDoS threats.
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DOI: 10.1016/j.eij.2024.100540
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