ABSTRACT
Ransomware attacks have evolved into one of the most pressing cybersecurity threats faced by modern enterprises, necessitating proactive defense mechanisms that go beyond traditional reactive strategies. This paper explores the integration of predictive analytics and early detection systems to develop a robust ransomware defense framework capable of identifying and mitigating threats before they materialize. By leveraging machine learning models, behavioral analysis, and anomaly detection techniques, organizations can anticipate ransomware activities and strengthen their cyber resilience.
Key findings indicate that predictive analytics significantly enhances threat detection accuracy by identifying deviations from normal system behaviors, thereby reducing false positives and improving response times. Moreover, early detection systems, when combined with real-time monitoring, automated incident response, and threat intelligence feeds, offer enterprises the ability to contain and neutralize ransomware attacks at their inception. Case studies and experimental results demonstrate that integrating predictive analytics with security information and event management (SIEM) systems reduces ransomware dwell time and limits organizational exposure to financial and operational disruptions.
The paper concludes that a proactive ransomware defense framework, underpinned by predictive analytics and early detection systems, is essential for modern enterprises seeking to mitigate the growing sophistication of cyber threats. Future research should focus on enhancing model accuracy, minimizing computational overhead, and improving adaptability to emerging ransomware variants. By adopting these advanced security measures, organizations can transition from a reactive to a proactive cybersecurity posture, ensuring stronger defenses against evolving ransomware threats.
References
- [1] Amro, M.Y., Dwieb, M., Hammad, J.A. and Wibawa, A.P., 2024. Ransomware detection: patterns, algorithms, and defense strategies. Bulletin of Social Informatics Theory and Application, 8(1), pp.165-172.
- [2] Beaman, C., Barkworth, A., Akande, T.D., Hakak, S. and Khan, M.K., 2021. Ransomware: Recent advances, analysis, challenges and future research directions. Computers & security, 111, p.102490. DOI: 10.1016/j.cose.2021.102190
- [3] Begovic, K., Al-Ali, A. and Malluhi, Q., 2023. Cryptographic ransomware encryption detection: Survey. Computers & Security, 132, p.103349.
- [4] Bhardwaj, A. and Kaushik, K., 2022. Predictive analytics-based cybersecurity framework for cloud infrastructure. International Journal of Cloud Applications and Computing (IJCAC), 12(1), pp.1-20.
- [5] Chowdhury, R.H., Prince, N.U. & Abdullah, S.M. (2024) The role of predictive analytics in cybersecurity: Detecting and preventing threat
- [6] Danish, M., 2024. Enhancing Cyber Security through Predictive Analytics: Real-Time Threat Detection and Response. arXiv preprint arXiv:2407.10864.
- [7] Dopamu, O.M., 2024. Cloud-based ransomware attack on US financial institutions: an in-depth analysis of tactics and counter measures. Int J Sci Res (IJSR), 13(2), pp.1872-81.
- [8] Duary, S., Choudhury, P., Mishra, S., Sharma, V., Rao, D.D. and Aderemi, A.P., 2024, February. Cybersecurity threats detection in intelligent networks using predictive analytics approaches. In 2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM)(pp. 1-5). IEEE.
- [9] Eastman, R., Versace, M. and Webber, A., 2015. Big data and predictive analytics: on the cybersecurity front line. IDC Whitepaper, February.
- Gallegos-Segovia, P.L., Bravo-Torres, J.F., Larios-Rosillo, V.M., Vintimilla-Tapia, P.E., Yuquilima-Albarado, I.F. and Jara-Saltos, J.D., 2017, October. Social engineering as an attack vector for ransomware. In 2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON)(pp. 1-6). IEEE.
- Habeeb, M.S. (2024) Predictive Analytics and Cybersecurity.
- LaRocque, A., Gross, G., Lindholm, F., Greco, P., Dupont, B. and Kruger, J., 2024. Effective ransomware detection using autonomous patternbased signature extraction.
- Lee, T., 2024. A Comprehensive Analysis of Challenges and Strategies in Enhancing Cyber Security for the Defense Industry.
- Malik, V., Khanna, A. and Sharma, N., 2024. Trends in ransomware attacks: Analysis and future predictions. International Journal of Global Innovations and Solutions (IJGIS).
- Manjezi, Z. and Botha, R.A., 2019. Preventing and Mitigating Ransomware: A Systematic Literature Review. In Information Security: 17th International Conference, ISSA 2018, Pretoria, South Africa, August 15–16, 2018, Revised Selected Papers 17(pp. 149-162). Springer International Publishing.
- McIntosh, T., Kayes, A.S.M., Chen, Y.P.P., Ng, A. and Watters, P., 2021. Ransomware mitigation in the modern era: A comprehensive review, research challenges, and future directions. ACM Computing Surveys (CSUR), 54(9), pp.1-36. DOI: 1145/3479393.
- Ogungbemi, O.S., Ezeugwa, F.A., Olaniyi, O.O., Akinola, O.I. and Oladoyinbo, O.B., 2024. Overcoming remote workforce cyber threats: A comprehensive ransomware and bot net defense strategy utilizing VPN networks. Available at SSRN 4911878.
- O’Kane, P., Sezer, S. and Carlin, D., 2018. Evolution of ransomware. Iet Networks, 7(5), pp.321-327. DOI: 1049/iet-net.2017.0207.
- Oz, H., Aris, A., Levi, A. and Uluagac, A.S., 2022. A survey on ransomware: Evolution, taxonomy, and defense solutions. ACM Computing Surveys (CSUR), 54(11s), pp.1-37. DOI: 1145/3514229.
- Rahman, M.K., Dalim, H.M. and Hossain, M.S., 2023. AI-Powered solutions for enhancing national cybersecurity: predictive analytics and threat mitigation. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 14(1), pp.1036-1069.
- Rana, M.U., Shah, M.A., Alnaeem, M.A. and Maple, C., 2024. Ransomware attacks in cyber-physical systems: Countermeasure of attack vectors through automated web defenses. IEEE Access.
Download all article in PDF
