Dynamic Analysis of Ransomware Behavior on Windows Operating System

Addin Amanatulloh Suparjo
Muhamad Irsan
Erwid Mustofa Jadied


DOI: https://doi.org/10.29100/jipi.v10i3.6381

Abstract


Ransomware is a type of malicious software capable of disabling computer functions or encrypting all files, resulting in significant disruption. This research dynamically analyzes ransomware behavior on the Windows 11 operating system. Several ransomware samples were executed and analyzed to obtain a list of ransomware behaviors used for performance testing on the samples. This topic is important as ransomware attacks have increased significantly and become one of the most destructive cyber threats. Ransomware attacks have caused major disruptions to services such as BSI banking, making it crucial in this digital era to understand the behavior of suspicious files or processes and how to mitigate such threats.

This study conducts dynamic analysis of ransomware behavior on the Windows 11 operating system. In an isolated environment using Virtual Machines (VMs), this research employs tools such as Process Monitor, Wireshark, and ProcDOT to collect data and visualize ransomware behavior. The results of this study include the compilation of a list of ransomware behaviors used to build a detection system that can identify samples based on detected behaviors. The developed detection system shows a good detection rate, which has a detection percentage of 69%. These results show significant potential in identifying ransomware threats, although there is still space for improvement and further development.

 


Keywords


Behavior; Cybersecurity; Detection System; Dynamic Analysis; Ransomware

Full Text:

PDF

Article Metrics :

References


A. Arabo, R. Dijoux, T. Poulain and G. Chevalier, "Detecting Ransomware Using Process Behavior Analysis," Procedia Computer Science, pp. 290-296, 2020.

K. Thummapudi, P. Lama and R. V. Boppana, "Detection of ransomware attacks using processor and disk usage data," IEEE, pp. 51395 - 51407, 2023.

T. R. Reshmi, "Information security breaches due to ransomware attacks - a systematic literature review," International Journal of Information Management Data Insights, vol. 1, 2021.

N. Hampton, Z. Baig and S. Zeadally, "Ransomware behavioural analysis on windows platforms," Journal of Information Security and Applications, vol. 40, pp. 44-51, 2018.

D. W. Fernando, N. Komninos and T. Chen, "A Study on the Evolution of Ransomware Detection Using Machine Learning and Deep Learning Techniques," Lecture notes in computer science, vol. 1, no. 2, pp. 551-604, 2020.

S. Zollner and K.-K. R. Choo, "An Automated Live Forensic and Postmortem Analysis Tool for Bitcoin on Windows Systems," IEEE Access, vol. 7, pp. 158250-158263, 2019.

M. . E. Russinovich, D. A. Solomon and A. Ionescu, Windows Internals Part 2, Redmond: Microsoft Press, 2012.

L. Chappell, Wireshark Network Analysis: The Official Wireshark Certified Network Analyst Study Guide, Carmel: Chappell University, 2017.

C. Miess, "ProcDot: The Malware Analysis Tool," CERT.at, 05 April 2013. [Online]. Available: https://www.procdot.com. [Accessed 10 Januari 2024].

K. K. Z. M. W. &. M. W. Cabaj, "Software-defined networking-based crypto ransomware detection using HTTP traffic characteristics," Journal of Network and Computer Applications, vol. 66, pp. 353-368, 2018.

BBC News Indonesia, "BSI diduga kena serangan siber, pengamat sebut sistem pertahanan bank ‘tidak kuat'," BBC News Indonesia, 16 Mei 2023. [Online]. Available: https://www.bbc.com/indonesia/articles/cn01gdr7eero. [Accessed 08 Januari 2024].

Y. Lemmou and J. Lanet, "A behavioural in‐depth analysis of ransomware infection," IET Information Security, vol. 15, no. 1, pp. 38-58, 2020.

F. De Gaspari and D. Hitaj, "Evading behavioral classifiers: a comprehensive analysis on evading ransomware detection techniques," Neural Computing and Applications, vol. 34, no. 14, pp. 12077-12096, 2022.

M. A. Ferrag, O. Friha and L. Maglaras, "Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis," IEEE Access, vol. 9, pp. 138509-138542, 2021.

S. Gulmez, A. G. Kakisim and I. Sogukpinar, "Analysis of the Dynamic Features on Ransomware Detection Using Deep Learning-based Methods," IEEE Access, 2023.

G. Karantzas and C. Patsakis, "An Empirical Assessment of Endpoint Detection and Response Systems against Advanced Persistent Threats Attack Vectors," Journal of Cybersecurity and Privacy, vol. 1, no. 3, pp. 387-421, 2021.

Z.-G. Chen, H.-S. Kang, S.-N. Yin and S.-R. Kim, "Automatic Ransomware Detection and Analysis Based on Dynamic API Calls Flow Graph," Proceedings of the International Conference on Research in Adaptive and Convergent Systems, vol. 196 , pp. 196-201, 2017.

S. Jamalpur, Y. S. Navya, P. Raja, G. Tagore and . G. R. K. Rao, "Dynamic Malware Analysis Using Cuckoo Sandbox," IEEE Access, 2018.

"Sysmon - System Monitor," Microsoft Docs., 23 07 2024. [Online]. Available: https://docs.microsoft.com/en-us/sysinternals/downloads/sysmon.. [Accessed 15 08 2024].

"Tcpdump/Libpcap public repository," Tcpdump, [Online]. Available: https://www.tcpdump.org/. [Accessed 15 08 2024].

"The Volatility Foundation - Promoting Accessible Memory Analysis Tools Within the Memory Forensics Community," Volatility, [Online]. Available: https://www.volatilityfoundation.org/. [Accessed 15 08 2024].

J. A. H. Silva, L. I. B. López and Á. L. V. Caraguay, "A Survey on Situational Awareness of Ransomware Attacks—Detection and Prevention Parameters," Remote Sens, vol. 11, no. 10, p. 1168, 2019.

D. Kirat, G. Vigna and C. Kruegel, "Barecloud: bare-metal analysis-based evasive malware detection," USENIX Security Symposium, pp. 287 - 301, 2014.


Tips Main yang Aman dan Seru

judi bolavipbet88vipbet88bolago88clubjudi