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Original Article
Prediction of Cyberattack on Software Supply Chain
Mohammed Muzaffar1
Dr. Khaja Mahabubullah2
1 Student, MCA, Deccan College of Engineering and Technology, Hyderabed, Telangana, India. 2 Professor & HOD, MCA, Deccan College of Engineering and Technology, Hyderabed, Telangana, India.
Published Online: September-October 2025
Pages: 13-18
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20250505003
References
1. S. Boyson, “Cyber supply chain risk management: Revolutionizing the strategic control of critical IT systems,” Technovation, vol. 34, no. 7, pp. 342–353, 2014. doi: 10.1016/j.technovation.2014.02.001
2. A. Arora, D. Hall, C. A. Pinto, D. Ramsey, and R. Telang, “An ounce of prevention vs. a pound of cure: How can we measure the value of IT security solutions?,” Carnegie Mellon University, Heinz College, Tech. Rep., 2004.
3. W. Alasmary, F. Alhaidari, and A. Alghamdi, “Machine learning-based cyber-attack detection approaches for Internet of Things (IoT) applications: A review,” IEEE Access, vol. 9, pp. 123612–123626, 2021. doi: 10.1109/ACCESS.2021.3108913
4. S. Cheung, U. Lindqvist, and A. Valdes, “Detecting malicious software updates in critical infrastructure using behavior profiling,” in Proc. IEEE Int. Conf. Technologies for Homeland Security (HST), 2018, pp. 1–7.
5. T. M. Wani, S. Jabin, and R. Sharma, “Supply chain cybersecurity: Threats and challenges in the digital era,” Procedia Computer Science, vol. 173, pp. 112–119, 2020. doi: 10.1016/j.procs.2020.06.014
6. N. Dhanjani, Abusing the Internet of Things: Blackouts, Freakouts, and Stakeouts. Sebastopol, CA, USA: O’Reilly Media, 2015.
7. R. Sommer and V. Paxson, “Outside the closed world: On using machine learning for network intrusion detection,” in Proc. IEEE Symp. Security and Privacy, 2010, pp. 305–316. doi: 10.1109/SP.2010.25
8. Scikit-learn Developers, “Scikit-learn: Machine learning in Python,” 2024. [Online]. Available: https://scikit-learn.org/
9. Streamlit Inc., “Streamlit documentation,” 2024. [Online]. Available: https://docs.streamlit.io/
10. SANS Institute, “Securing the software supply chain,” White Paper, 2021. [Online]. Available: https://www.sans.org/white-papers/40220/
2. A. Arora, D. Hall, C. A. Pinto, D. Ramsey, and R. Telang, “An ounce of prevention vs. a pound of cure: How can we measure the value of IT security solutions?,” Carnegie Mellon University, Heinz College, Tech. Rep., 2004.
3. W. Alasmary, F. Alhaidari, and A. Alghamdi, “Machine learning-based cyber-attack detection approaches for Internet of Things (IoT) applications: A review,” IEEE Access, vol. 9, pp. 123612–123626, 2021. doi: 10.1109/ACCESS.2021.3108913
4. S. Cheung, U. Lindqvist, and A. Valdes, “Detecting malicious software updates in critical infrastructure using behavior profiling,” in Proc. IEEE Int. Conf. Technologies for Homeland Security (HST), 2018, pp. 1–7.
5. T. M. Wani, S. Jabin, and R. Sharma, “Supply chain cybersecurity: Threats and challenges in the digital era,” Procedia Computer Science, vol. 173, pp. 112–119, 2020. doi: 10.1016/j.procs.2020.06.014
6. N. Dhanjani, Abusing the Internet of Things: Blackouts, Freakouts, and Stakeouts. Sebastopol, CA, USA: O’Reilly Media, 2015.
7. R. Sommer and V. Paxson, “Outside the closed world: On using machine learning for network intrusion detection,” in Proc. IEEE Symp. Security and Privacy, 2010, pp. 305–316. doi: 10.1109/SP.2010.25
8. Scikit-learn Developers, “Scikit-learn: Machine learning in Python,” 2024. [Online]. Available: https://scikit-learn.org/
9. Streamlit Inc., “Streamlit documentation,” 2024. [Online]. Available: https://docs.streamlit.io/
10. SANS Institute, “Securing the software supply chain,” White Paper, 2021. [Online]. Available: https://www.sans.org/white-papers/40220/
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