RANSOMWARE DEFENSE IN THE CLOUD ENVIRONMENTS: ADAPTIVE STRATEGIES FOR EVOLVING THREATS

Durga Prasada Rao Sanagana

Author:

Durga Prasada Rao Sanagana

Published in

Journal of Science Technology and Research

( Volume , Issue )

Page No: 210-217

Volume , Issue

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Abstract

: Ransomware attacks have become a predominant threat to cloud environments, necessitating robust and adaptive defense strategies. This manuscript delves into the intricacies of ransomware threats in cloud ecosystems, outlines current vulnerabilities, and presents adaptive strategies for mitigation and defense. By examining recent case studies, threat vectors, and evolving tactics, we aim to provide comprehensive guidance for securing cloud environments against ransomware. Our analysis is supplemented with best practices, advanced detection techniques, and recommendations for enhancing resilience against future ransomware attacks.

Keywords

: Ransomware, Cloud Security, Adaptive Defense, Cybersecurity, Threat Mitigation, Cloud Environments, Incident Response and Data Protection

References

1. Prasad, B. S., Gupta, S., Borah, N., Dineshkumar, R., Lautre, H. K., & Mouleswararao, B. (2023). Predicting diabetes with multivariate analysis an innovative KNN-based classifier approach. Preventive Medicine, 174, 107619.
2. Prasad, B. V. V. S., and Sheba Angel. “Predicting future resource requirement for efficient resource management in cloud.” International Journal of Computer Applications 101, no. 15 (2014): 19-23.
3. Prasad, B. V., and S. Salman Ali. “Software–defined networking based secure rout-ing in mobile ad hoc network.” International Journal of Engineering & Technology 7.1.2 (2017): 229.
4. Alapati, N., Prasad, B. V. V. S., Sharma, A., Kumari, G. R. P., Veeneetha, S. V., Srivalli, N., … & Sahitya, D. (2022, November). Prediction of Flight-fare using machine learning. In 2022 International Conference on Fourth Industrial Revolution Based Technology and Practices (ICFIRTP) (pp. 134-138). IEEE.
5. Kumar, B. R., Ashok, G., & Prasad, B. S. (2015). Tuning PID Controller Parameters for Load Frequency Control Considering System Uncertainties. Int. Journal of Engineering Research and Applications, 5(5), 42-47.
6. Ali, S. S., & Prasad, B. V. V. S. (2017). Secure and energy aware routing protocol (SEARP) based on trust-factor in Mobile Ad-Hoc networks. Journal of Statistics and Management Systems, 20(4), 543–551. https://doi.org/10.1080/09720510.2017.1395174
7. Onyema, E. M., Balasubaramanian, S., Iwendi, C., Prasad, B. S., & Edeh, C. D. (2023). Remote monitoring system using slow-fast deep convolution neural network model for identifying anti-social activities in surveillance applications. Measurement: Sensors, 27, 100718.
8. Syed, S. A., & Prasad, B. V. V. S. (2019, April). Merged technique to prevent SYBIL Attacks in VANETs. In 2019 International Conference on Computer and Information Sciences (ICCIS) (pp. 1-6). IEEE.
9. Patil, P. D., & Chavan, N. (2014). Proximate analysis and mineral characterization of Barringtonia species. International Journal of Advances in Pharmaceutical Analysis, 4(3), 120-122.
10. Desai, Mrunalini N., Priya D. Patil, and N. S. Chavan. “ISOLATION AND CHARACTERIZATION OF STARCH FROM MANGROVES Aegiceras corniculatum (L.) Blanco and Cynometra iripa Kostel.” (2011).
11. Patil, P. D., Gokhale, M. V., & Chavan, N. S. (2014). Mango starch: Its use and future prospects. Innov. J. Food Sci, 2, 29-30.
12. Priya Patil, D., N. S. Chavan, and B. S. Anjali. “Sonneratia alba J. Smith, A Vital Source of Gamma Linolenic Acid (GLA).” Asian J Pharm Clin Res 5.1 (2012): 172-175.
13. Priya, D., Patil, A., Niranjana, S., & Chavan, A. (2012). Potential testing of fatty acids from mangrove Aegiceras corniculatum (L.) Blanco. Int J Pharm Sci, 3, 569-71.
14. Priya, D., Patil, A., Niranjana, S., & Chavan, A. (2012). Potential testing of fatty acids from mangrove Aegiceras corniculatum (L.) Blanco. Int J Pharm Sci, 3, 569-71.
15. Patil, Priya D., and N. S. Chavan. “A comparative study of nutrients and mineral composition of Carallia brachiata (Lour.) Merill.” International Journal of Advanced Science and Research 1 (2015): 90-92.
16. Patil, P. D., & Chavan, N. S. (2013). A need of conservation of Bruguiera species as a famine food. Annals Food Science and Technology, 14, 294-297.
17. Bharathi, G. P., Chandra, I., Sanagana, D. P. R., Tummalachervu, C. K., Rao, V. S., &Neelima, S. (2024). AI-driven adaptive learning for enhancing business intelligence simulation games. Entertainment Computing, 50, 100699.
18. Nagarani, N., et al. “Self-attention based progressive generative adversarial network optimized with momentum search optimization algorithm for classification of brain tumor on MRI image.” Biomedical Signal Processing and Control 88 (2024): 105597.
19. Reka, R., R. Karthick, R. Saravana Ram, and Gurkirpal Singh. “Multi head self-attention gated graph convolutional network based multi‑attack intrusion detection in MANET.” Computers & Security 136 (2024): 103526.
20. Meenalochini, P., R. Karthick, and E. Sakthivel. “An Efficient Control Strategy for an Extended Switched Coupled Inductor Quasi-Z-Source Inverter for 3 Φ Grid Connected System.” Journal of Circuits, Systems and Computers 32.11 (2023): 2450011.
21. Karthick, R., et al. “An optimal partitioning and floor planning for VLSI circuit design based on a hybrid bio-inspired whale optimization and adaptive bird swarm optimization (WO-ABSO) algorithm.” Journal of Circuits, Systems and Computers 32.08 (2023): 2350273.
22. Jasper Gnana Chandran, J., et al. “Dual-channel capsule generative adversarial network optimized with golden eagle optimization for pediatric bone age assessment from hand X-ray image.” International Journal of Pattern Recognition and Artificial Intelligence 37.02 (2023): 2354001.
23. Rajagopal RK, Karthick R, Meenalochini P, Kalaichelvi T. Deep Convolutional Spiking Neural Network optimized with Arithmetic optimization algorithm for lung disease detection using chest X-ray images. Biomedical Signal Processing and Control. 2023 Jan 1;79:104197.
24. Karthick, R., and P. Meenalochini. “Implementation of data cache block (DCB) in shared processor using field-programmable gate array (FPGA).” Journal of the National Science Foundation of Sri Lanka 48.4 (2020).
25. Karthick, R., A. Senthilselvi, P. Meenalochini, and S. Senthil Pandi. “Design and analysis of linear phase finite impulse response filter using water strider optimization algorithm in FPGA.” Circuits, Systems, and Signal Processing 41, no. 9 (2022): 5254-5282.
26. Kanth, T. C. (2024). AI-POWERED THREAT INTELLIGENCE FOR PROACTIVE SECURITY MONITORING IN CLOUD INFRASTRUCTURES.
27. Karthick, R., and M. Sundararajan. “SPIDER-based out-of-order execution scheme for HtMPSOC.” International Journal of Advanced Intelligence paradigms 19.1 (2021): 28-41.
28. Karthick, R., Dawood, M.S. & Meenalochini, P. Analysis of vital signs using remote photoplethysmography (RPPG). J Ambient Intell Human Comput 14, 16729–16736 (2023). https://doi.org/10.1007/s12652-023-04683-w
29. Selvan, M. A., & Amali, S. M. J. (2024). RAINFALL DETECTION USING DEEP LEARNING TECHNIQUE.

ABSTRACT:

Ransomware attacks pose a critical threat to cloud environments, affecting data security and operational continuity. In response, organizations must adopt adaptive strategies for evolving threats to effectively mitigate these risks. This paper explores the complexity of ransomware behavior in cloud ecosystems by analyzing threat vectors, known vulnerabilities, and recent attack scenarios. We examine how attackers exploit misconfigured services, weak credentials, and inadequate backup protocols. In addition, the paper discusses dynamic countermeasures, including real-time anomaly detection, automated response systems, and encryption-aware backups. By integrating case studies and industry reports, we offer a detailed roadmap for building resilient cloud infrastructure. Our findings emphasize the importance of proactive security design, continuous monitoring, and AI-driven threat detection. Ultimately, this research equips cloud users and administrators with the tools and frameworks needed to safeguard digital assets and ensure business continuity in the face of increasingly sophisticated ransomware attacks.

INTRODUCTION:

Cloud computing offers unmatched scalability and flexibility, but it also introduces new security challenges. One of the most dangerous among these is ransomware. These attacks are becoming more advanced, often bypassing traditional security systems. To combat this, cloud users must adopt adaptive strategies for evolving threats. Unlike static defenses, adaptive strategies adjust to the changing nature of ransomware, allowing for faster and more precise responses. In this paper, we examine how ransomware infiltrates cloud environments through common vulnerabilities such as misconfigured APIs, insecure user roles, and unmonitored access. We highlight the need for layered defense mechanisms that include threat intelligence, automated detection tools, and incident response plans. Moreover, we explore how cloud-native solutions can be configured to detect and isolate malicious activity in real time. This introduction sets the stage for understanding how organizations can strengthen their cloud defenses against ransomware using flexible, forward-thinking approaches.

RANSOMWARE DEFENSE IN THE CLOUD ENVIRONMENTS: ADAPTIVE STRATEGIES FOR EVOLVING THREATS