Machine Learning Model for Detecting Attack in Service Supply Chain
Article content
Abstract
Supply chain attacks exploit weaknesses in third-party vendors, software updates, and service providers, mainly posing a cybersecurity problem. Traditional detection methods often lag behind these sophisticated attacks. The study employs machine learning methods to increase the detection of service supply chain attacks, including Decision Trees, Random Forest, and XGBoost algorithms. These models were assessed in accordance with accuracy, precision, recall, and the F1-score, with Random Forest topping the list with an accuracy of 96.1%, followed by Decision Trees with 95.0% accuracy and XGBoost with 94.7% accuracy. Through the use of graphs showing the ROC and Precision-Recall curves, Random Forest can best describe the balance between precision and recall. Random Forest is tremendously good for detection with less false positives; however, due to its high computational costs, it may be challenging to implement in real-time. These results shed light on the potential of machine learning technology to outperform traditional intrusion detection systems and enhance cybersecurity in service supply chains. Future research will focus on real-time implementation and hybrid models that combine classical and deep learning techniques.
Keywords
anomaly detection, cybersecurity, indicator of compromise (IoC), intrusion detection, machine learning, supply chain attack, tactics, techniques and procedures
Downloads
References
Khan, I., Moustafa, N., Pi, D., Hussain, Y., & Khan, N. (2023). DFF-SC4N: A deep federated defence framework for protecting Supply Chain 4.0 networks. IEEE Transactions on Industrial Informatics, 19, 3300–3309. https://doi.org/10.1109/TII.2021.3108811
Kim, J., et al. (2022). Securing third-party services in supply chains. Supply Chain Security Journal, 14(1), 112–123.
Masum, M., Nazim, M., Faruk, M., Shahriar, H., Valero, M., Khan, M., Uddin, G., Barzanjeh, S., Saglamyurek, E., Rahman, A., & Ahamed, S. (2022). Quantum machine learning for software supply chain attacks: How far can we go? 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), 530–538. https://doi.org/10.48550/arXiv.2204.02784
Ismail, S., Dandan, S., Dawoud, D., & Reza, H. (2024). A comparative study of lightweight machine learning techniques for cyber-attacks detection in blockchain-enabled industrial supply chain. IEEE Access, 12, 102481–102491. https://doi.org/10.1109/ACCESS.2024.3432454
Lokanan, M., & Maddhesia, V. (2024). Supply chain fraud prediction with machine learning and artificial intelligence. International Journal of Production Research. https://doi.org/10.1080/00207543.2024.2361434
Kumar, A., et al. (2021). Machine learning techniques for modern cybersecurity challenges. Journal of Information Security Research, 12(3), 45–67.
Liu, H., Hu, B., & Zhang, J. (2021). Feature selection for machine learning: A survey. Journal of Computational Intelligence.
Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157–1182.
SolarWinds. (2020). SolarWinds cyberattack. SolarWinds Blog. https://www.solarwinds.com/securityadvisory
Huang, J., & Tsai, K. (2022). Securing authoritarian capitalism in the digital age: The political economy of surveillance in China. The China Journal, 88, 2–28. https://doi.org/10.1086/720144
Pigni, F., Bartosiak, M., Piccoli, G., & Ives, B. (2018). Targeting Target with a 100 million dollar data breach. Journal of Information Technology Teaching Cases, 8, 9–23. https://doi.org/10.1057/S41266-017-0028-0
Mundt, M., & Baier, H. (2022). Threat-based simulation of data exfiltration toward mitigating multiple ransomware extortions. Digital Threats: Research and Practice, 4, 1–23. https://doi.org/10.1145/3568993
Perumal, S., Sujatha, P., S., K., & Krishnan, M. (2024). Clusters in chaos: A deep unsupervised learning paradigm for network anomaly detection. Journal of Network and Computer Applications, 235, 104083. https://doi.org/10.1016/j.jnca.2024.104083
Sarker, I. H., et al. (2022). Role of machine learning in enhancing cybersecurity. Cybersecurity and Privacy Journal, 5(1), 12–34.
Nguyen, T., et al. (2021). Anomaly detection in network traffic using unsupervised learning techniques. Cybersecurity Review, 18(2), 23–39.
Jaber, A. (2024). Transforming cybersecurity dynamics: Enhanced self-play reinforcement learning in intrusion detection and prevention system. 2024 IEEE International Systems Conference (SysCon), 1–8. https://doi.org/10.1109/SysCon61195.2024.10553626
Zhao, Z., & Li, X. (2020). Machine learning approaches for cybersecurity: Applications and challenges. Journal of Cybersecurity, 8(4), 129–145. https://www.journals.elsevier.com/journal-of-cybersecurity
Sharma, R., & Gupta, N. (2022). Adaptive authentication systems using machine learning. Advances in Cybersecurity, 10(4), 67–81.
Lim, W., Yong, K., Lau, B., & Tan, C. (2024). Future of generative adversarial networks (GAN) for anomaly detection in network security: A review. Computers & Security, 139, 103733.
Yeboah-Ofori, A., & Boachie, C. (2019). Malware attack predictive analytics in a cyber supply chain context using machine learning. 2019 International Conference on Cyber Security and Internet of Things (ICSIoT), 66–73. https://doi.org/10.1109/ICSIoT47925.2019.00019
Akter, S., Hossain Faruk, M. J., Anjum, N., Masum, M., Shahriar, H., Sakib, N., Rahman, A., Wu, F., & Cuzzocrea, A. (2022). Software supply chain vulnerabilities detection in source code: Performance comparison between traditional and quantum machine learning algorithms. Proceedings of the 2022 IEEE International Conference on Big Data (Big Data), 5639–5645. https://doi.org/10.1109/BigData55660.2022.10020813
Al-Ansari, A., & Alsubait, T. (2022). Predicting cyber threats using machine learning for improving cyber supply chain. 2022 Fifth National Conference of Saudi Computers Colleges (NCCC), 123–130. https://doi.org/10.1109/NCCC57165.2022.10067692
Gokkaya, Z., et al. (2024). Software supply chain: Review of attacks, risk assessment strategies, and security controls. International Journal of Safety and Security Engineering, 11(5). https://doi.org/10.18280/ijsse.110505
Cai, Z., Huang, S., Chen, C., Lin, J., & Ou, Y. (2023). Detecting supply chain attacks with unsupervised learning. 2023 9th International Conference on Applied System Innovation (ICASI), 232–234. https://doi.org/10.1109/ICASI57738.2023.10179583
Ghozi, W., Setiono, O., Rafrastara, F., Rijati, N., & Shidik, G. (2025). Tree-based ensemble algorithms and feature selection method for intelligent distributed denial of service attack detection. Journal of Cyber Security and Mobility, 14(1), 1–24. https://doi.org/10.13052/jcsm2245-1439.1411
Korbaa, O., Jemili, F., & Meddeb, R. (2023). Intrusion detection based on ensemble learning for big data classification. Cluster Computing, 27(3), 3771–3798. https://doi.org/10.1007/s10586-023-04168-7
Lim, W., Yong, K., Lau, B., & Tan, C. (2024). Future of generative adversarial networks (GAN) for anomaly detection in network security: A review. Computers & Security, 139, 103733. https://doi.org/10.1016/j.cose.2024.103733
Sebbar, A., & Zkik, K. (2023). Enhancing resilience against DDoS attacks in SDN-based supply chain networks using machine learning. 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT), 230–234. https://doi.org/10.1109/CoDIT58514.2023.10284387
Wloomis.(2023,September 27). Software Supply Chain Security: The Dataset.DFRLab. https://dfrlab.org/2023/09/27/software-supply-chain-security-the-dataset]
Article details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
You must read and accept the copyright terms and conditions(click here for copyright terms).