Impact of Machine Learning on Intrusion Detection Systems for the Protection of Critical Infrastructure
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| I publikationen: | Information vol. 16, no. 7 (2025), p. 515-557 |
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| Huvudupphov: | |
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MDPI AG
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| Länkar: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2078-2489 | ||
| 024 | 7 | |a 10.3390/info16070515 |2 doi | |
| 035 | |a 3233222222 | ||
| 045 | 2 | |b d20250701 |b d20250731 | |
| 084 | |a 231474 |2 nlm | ||
| 100 | 1 | |a Kumar, Avinash | |
| 245 | 1 | |a Impact of Machine Learning on Intrusion Detection Systems for the Protection of Critical Infrastructure | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a In the realm of critical infrastructure protection, robust intrusion detection systems (IDSs) are essential for securing essential services. This paper investigates the efficacy of various machine learning algorithms for anomaly detection within critical infrastructure, using the Secure Water Treatment (SWaT) dataset, a comprehensive collection of time-series data from a water treatment testbed, to experiment upon and analyze the findings. The study evaluates supervised learning algorithms alongside unsupervised learning algorithms. The analysis reveals that supervised learning algorithms exhibit exceptional performance with high accuracy and reliability, making them well-suited for handling the diverse and complex nature of anomalies in critical infrastructure. They demonstrate significant capabilities in capturing spatial and temporal variables. Among the unsupervised approaches, valuable insights into anomaly detection are provided without the necessity for labeled data, although they face challenges with higher rates of false positives and negatives. By outlining the benefits and drawbacks of these machine learning algorithms in relation to critical infrastructure, this research advances the field of cybersecurity. It emphasizes the importance of integrating supervised and unsupervised techniques to enhance the resilience of IDSs, ensuring the timely detection and mitigation of potential threats. The findings offer practical guidance for industry professionals on selecting and deploying effective machine learning algorithms in critical infrastructure environments. | |
| 653 | |a Machine learning | ||
| 653 | |a Accuracy | ||
| 653 | |a Internet | ||
| 653 | |a Datasets | ||
| 653 | |a Infrastructure | ||
| 653 | |a Intrusion detection systems | ||
| 653 | |a Public safety | ||
| 653 | |a National security | ||
| 653 | |a Electricity distribution | ||
| 653 | |a Supervised learning | ||
| 653 | |a Unsupervised learning | ||
| 653 | |a Water treatment plants | ||
| 653 | |a Effectiveness | ||
| 653 | |a Cybersecurity | ||
| 653 | |a Algorithms | ||
| 653 | |a Malware | ||
| 653 | |a Anomalies | ||
| 653 | |a Water treatment | ||
| 653 | |a Security personnel | ||
| 653 | |a Critical infrastructure | ||
| 653 | |a Financial institutions | ||
| 700 | 1 | |a Gutierrez, Jairo A | |
| 773 | 0 | |t Information |g vol. 16, no. 7 (2025), p. 515-557 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3233222222/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3233222222/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3233222222/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |