Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Can Machine Learning Enhance Intrusion Detection to Safeguard Smart City Networks from Multi-Step Cyberattacks?
Intrusion detection systems are essential for detecting network cyberattacks. As the sophistication of cyberattacks increases, it is critical that defense technologies adapt to counter them. Multi-step attacks, which need several correlated intrusion operations to reach the desired target, are a rising trend in the cybersecurity field. System administrators are responsible for recreating whole attack scenarios and developing improved intrusion detection systems since the systems at present are still designed to generate alerts for only single attacks with little to no correlation. This paper proposes a machine learning approach to identify and classify multi-step network intrusion attacks, with particular relevance to smart cities, where interconnected systems are highly vulnerable to cyber threats. Smart cities rely on these systems seamlessly functioning with one another, and any successful cyberattack could have devastating effects, including large-scale data theft. In such a context, the proposed machine learning model offers a robust solution for detecting and mitigating multi-step cyberattacks in these critical environments. Several machine learning algorithms are considered, namely Decision Tree (DT), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machine (SVM), Light Gradient-Boosting Machine (LGBM), Extreme Gradient Boosting (XGB) and Random Forest (RF). These models are trained on the Multi-Step Cyber-Attack Dataset (MSCAD), a recent dataset that is highly representative of real-world multi-step cyberattack scenarios, which increases the accuracy and efficiency of such systems. The experimental results show that the best performing model was XGB, which achieved a testing accuracy of 100% and an F1 Score of 88%. The proposed model is computationally efficient and easy to deploy, which ensures a fast, sustainable and low power-consuming intrusion detection system at the cutting edge.
Can Machine Learning Enhance Intrusion Detection to Safeguard Smart City Networks from Multi-Step Cyberattacks?
Intrusion detection systems are essential for detecting network cyberattacks. As the sophistication of cyberattacks increases, it is critical that defense technologies adapt to counter them. Multi-step attacks, which need several correlated intrusion operations to reach the desired target, are a rising trend in the cybersecurity field. System administrators are responsible for recreating whole attack scenarios and developing improved intrusion detection systems since the systems at present are still designed to generate alerts for only single attacks with little to no correlation. This paper proposes a machine learning approach to identify and classify multi-step network intrusion attacks, with particular relevance to smart cities, where interconnected systems are highly vulnerable to cyber threats. Smart cities rely on these systems seamlessly functioning with one another, and any successful cyberattack could have devastating effects, including large-scale data theft. In such a context, the proposed machine learning model offers a robust solution for detecting and mitigating multi-step cyberattacks in these critical environments. Several machine learning algorithms are considered, namely Decision Tree (DT), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machine (SVM), Light Gradient-Boosting Machine (LGBM), Extreme Gradient Boosting (XGB) and Random Forest (RF). These models are trained on the Multi-Step Cyber-Attack Dataset (MSCAD), a recent dataset that is highly representative of real-world multi-step cyberattack scenarios, which increases the accuracy and efficiency of such systems. The experimental results show that the best performing model was XGB, which achieved a testing accuracy of 100% and an F1 Score of 88%. The proposed model is computationally efficient and easy to deploy, which ensures a fast, sustainable and low power-consuming intrusion detection system at the cutting edge.
Can Machine Learning Enhance Intrusion Detection to Safeguard Smart City Networks from Multi-Step Cyberattacks?
Jowaria Khan (Autor:in) / Rana Elfakharany (Autor:in) / Hiba Saleem (Autor:in) / Mahira Pathan (Autor:in) / Emaan Shahzad (Autor:in) / Salam Dhou (Autor:in) / Fadi Aloul (Autor:in)
2025
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
Countering Cyberattacks During Information Operations
| Springer Verlag | 2020
Countering Cyberattacks During Information Operations
| TIBKAT | 2021