Comparative Analysis of Distinctive Features of the Ransomware Tactics in Relation to Other Malware

Authors

  • Simon Kihiu The University of Nairobi, School of computing and Informatics, P.O Box 30197, Nairobi, GPO, Kenya
  • Elisha Abade The University of Nairobi, School of computing and Informatics, P.O Box 30197, Nairobi, GPO, Kenya

Keywords:

Ransomware, Ransom, Malware, Cybercriminal, Cybersecurity

Abstract

Ransomware have become a real threat to the use of technology. Unlike other forms of malware that could target systems by deleting or editing some files and creating backdoor for the attacker to access the system, ransomware have gone a notch higher by targeting humans. This is achieved when a ransomware encrypts data of the infected computer and a note demanding for a ransom to be paid is printed on the screen. Due to the advancement in technology, ransomware use advanced and secure encryption algorithm that is difficult to decrypt even when the computational power is not limited. In this work, we present some of the major behavioral characteristics that we found to be common with ransomware and not with other malware. Our results show that a careful analysis of suspicious network and file activities can help detect a ransomware attack. Further, careful analysis of ransomware behavior can help develop a system that can detect an impeding ransomware attack and thereby eliminate it.

References

. A. Clark, Q. Zhu, R. Poovendran, & T. Başar, (2013, June). An impact-aware defense against stuxnet. In 2013 American Control Conference (pp. 4140-4147). IEEE.

. D.S. Wall, “Dis-organised crime: Towards a distributed model of the organization of cybercrime.” The European Review of Organised Crime, vol. 2, 2015.

. Internet security threat report. “ISTR Internet security threat report”. Internet: http://book.itep.ru/depository/surveys/ISTR22_Main-FINAL-APR24.pdf. 2017

. T.S.Rajput. “Evolving Threat Agents: Ransomware and their Variants.” International Journal of Computer Applications, vol. 164, pp.28-34, 2015.

. K. S. Choi, T.M. Scott, & D.P. LeClair. “Ransomware against police: diagnosis of risk factors via application of cyber-routine activities theory”. International Journal of Forensic Science & Pathology. 2016.

. D. Nieuwenhuizen. “Abehavioural-based approach to ransomware detection”. Whitepaper. MWR Labs Whitepaper. 2017.

. F. Mbol, J.M Robert, & A. Sadighian, (2016, November). An efficient approach to detect torrentlocker ransomware in computer systems. In International Conference on Cryptology and Network Security (pp. 532-541). Springer, Cham.

. N. Hampton, & Z.A. Baig,. Ransomware: Emergence of the cyber-extortion menace. 2015

. Kaspersky. (2015). “No Ransom: The National high tech crime unit of the Netherlands’ police and Kaspersky lab helps victims to escape from Coinvault ransomware”. Internet: https://www.kaspersky.com/about/press-releases/2015_no-ransom-the-national-high-tech-crime-unit-of-the-netherlands-police-and-kaspersky-lab-help-victims-to-escape-from-coinvault-ransomware,2016

. P. Zavarsky, & D. Lindskog. “Experimental analysis of ransomware on windows and android platforms: Evolution and characterization.” Procedia Computer Science, vol.94, pp.465-472, 2016.

. N. Andronio, S. Zanero, & F. Maggi, (2015, November). Heldroid: Dissecting and detecting mobile ransomware. In International Symposium on Recent Advances in Intrusion Detection (pp. 382-404). Springer, Cham.

. N. Scaife., H. Carter., P. Traynor., & K.R. Butler., (2016, June). Cryptolock (and drop it): stopping ransomware attacks on user data. In 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS) (pp. 303-312). IEEE.

. Internet security threat report. “ISTR Internet security threat report”. Internet: www.itu.int/en/ITUD/Cybersecurity/Documents/Symantec_annual_internet_threat_report_ITU2015.pdf. 2015

. A. Tseng, Y. Chen, Y. Kao, & T. Lin. “Deep learning for ransomware detection”. IEICE Tech. Rep., vol. 116, pp.87-92, 2016.

. A. Kharraz, W. Robertson, D. Balzarotti, L. Bilge, & E. Kirda, (2015, July). Cutting the gordian knot: A look under the hood of ransomware attacks. In International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment (pp. 3-24). Springer, Cham.

. A. Ali, R. Murthy, & F. Kohun. “Recovering from the nightmare of ransomware-how savvy users get hit with viruses and malware: a personal case study.” Issues in Information Systems, vol.17,2016.

. D. Morato, E. Berrueta, E. Magaña, E., & M. Izal,. (2018). Ransomware early detection by the analysis of file sharing traffic. Journal of Network and Computer Applications, vol. 124, pp.14-32.

. J. Huang, J. Xu, X. Xing, P. Liu, & M.K. Qureshi. (2017, October). Flashguard: Leveraging intrinsic flash properties to defend against encryption ransomware. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 2231-2244.

. L. Cavallaro, P. Saxena & R. Sekar, (2007). Anti-taint-analysis: Practical evasion techniques against information flow based malware defense. Secure Systems Lab at Stony Brook University, Tech. Rep, pp.1-18.

Downloads

Published

2020-07-27

How to Cite

Kihiu, S. ., & Abade, E. . (2020). Comparative Analysis of Distinctive Features of the Ransomware Tactics in Relation to Other Malware. International Journal of Computer (IJC), 38(1), 173–182. Retrieved from https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/1671

Issue

Section

Articles