Abstract
In order to detect malicious file system activity, some commercial and academic anti-ransomware solutions implement deception-based techniques, specifically by placing decoy files among user files. While this approach raises the bar against current ransomware, as any access to a decoy file is a sign of malicious activity, the robustness of decoy strategies has not been formally analyzed and fully tested. In this paper, we analyze existing decoy strategies and discuss how they are effective in countering current ransomware by defining a set of metrics to measure their robustness. To demonstrate how ransomware can identify existing deception-based detection strategies, we have implemented a proof-of-concept anti-decoy ransomware that successfully bypasses decoys by using a decision engine with few rules. Finally, we discuss existing issues in decoy-based strategies and propose practical solutions to mitigate them.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Some statistics show that nearly 50% of those companies who paid the ransom were actually able to recover their data back, e.g., see [7].
- 2.
Juels and Rivest, who propose honeywords to detect a password leak, call it flatness [14].
- 3.
See the manual page at http://man7.org/linux/man-pages/man3/readdir.3.html.
- 4.
For the sake of proof-of-concept: a real ransomware would use a strong key-management strategy.
- 5.
Available under GPLv3 at https://github.com/ziyagenc/decoy-updater.
- 6.
Due to the limited capability of System.IO.FileSystemWatcher class, we could observe the malicious activity, yet we were not able to identify the process ID of Replace and terminate it. That would be possible with developing a file system mini-filter, which is an implementation effort.
References
Balfanz, D., Durfee, G., Smetters, D.K., Grinter, R.E.: In search of usable security: five lessons from the field. IEEE Secur. Priv. 2(5), 19–24 (2004)
Bowen, B.M., Hershkop, S., Keromytis, A.D., Stolfo, S.J.: Baiting inside attackers using decoy documents. In: Chen, Y., Dimitriou, T.D., Zhou, J. (eds.) SecureComm 2009. LNICST, vol. 19, pp. 51–70. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-05284-2_4
Bulazel, A., Yener, B.: A survey on automated dynamic malware analysis evasion and counter-evasion: PC, mobile, and web. In: Proceedings of the 1st Reversing and Offensive-Oriented Trends Symposium, pp. 2:1–2:21. ACM, New York (2017)
Cabaj, K., Mazurczyk, W.: Using software-defined networking for ransomware mitigation: the case of cryptowall. IEEE Netw. 30(6), 14–20 (2016)
Continella, A., et al.: ShieldFS: a self-healing, ransomware-aware filesystem. In: Proceedings of the 32nd Annual Conference on Computer Security Applications, ACSAC 2016, pp. 336–347. ACM, New York (2016)
Council of European Union: Council regulation (EU) no 428/2009 (2009). https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=celex:32009R0428. Accessed 22 Feb 2019
CyberEdge: 2018 Cyberthreat Defense Report. Technical report, CyberEdge Group, LLC, March 2018. https://cyber-edge.com/wp-content/uploads/2018/03/CyberEdge-2018-CDR.pdf
European Commission: Guidance note - Research involving dual-use items. http://ec.europa.eu/research/participants/data/ref/h2020/other/hi/guide_research-dual-use_en.pdf. Accessed 22 Feb 2019
Feng, Y., Liu, C., Liu, B.: Poster: a new approach to detecting ransomware with deception. In: 38th IEEE Symposium on Security and Privacy Workshops (2017)
Genç, Z.A., Lenzini, G., Ryan, P.Y.A.: No random, no ransom: a key to stop cryptographic ransomware. In: Giuffrida, C., Bardin, S., Blanc, G. (eds.) DIMVA 2018. LNCS, vol. 10885, pp. 234–255. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93411-2_11
Greenberg, A.: The untold story of NotPetya, the most devastating cyberattack in history, August 2018. https://www.wired.com/story/notpetya-cyberattack-ukraine-russia-code-crashed-the-world/. Accessed 22 Feb 2019
Gómez-Hernández, J.,Álvarez González, L., García-Teodoro, P.: R-locker: thwarting ransomware action through a honeyfile-based approach. Comput. Secur. 73, 389–398 (2018)
Hunt, G., Brubacher, D.: Detours: binary interception of win32 functions. In: Proceedings of the 3rd Conference on USENIX Windows NT Symposium, WINSYM1999, vol. 3, p. 14. USENIX Association, Berkeley (1999)
Juels, A., Rivest, R.L.: Honeywords: making password-cracking detectable. In: Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security, CCS 2013, pp. 145–160. ACM, New York (2013)
Kharraz, A., Kirda, E.: Redemption: real-time protection against ransomware at end-hosts. In: Dacier, M., Bailey, M., Polychronakis, M., Antonakakis, M. (eds.) RAID 2017. LNCS, vol. 10453, pp. 98–119. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66332-6_5
Kolodenker, E., Koch, W., Stringhini, G., Egele, M.: Paybreak: defense against cryptographic ransomware. In: Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, pp. 599–611. ACM (2017)
Lee, J., Lee, J., Hong, J.: How to make efficient decoy files for ransomware detection? In: Proceedings of the International Conference on Research in Adaptive and Convergent Systems, RACS 2017, pp. 208–212. ACM, New York (2017)
Mehnaz, S., Mudgerikar, A., Bertino, E.: RWGuard: a real-time detection system against cryptographic ransomware. In: Bailey, M., Holz, T., Stamatogiannakis, M., Ioannidis, S. (eds.) RAID 2018. LNCS, vol. 11050, pp. 114–136. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00470-5_6
Moore, C.: Detecting ransomware with honeypot techniques. In: 2016 Cybersecurity and Cyberforensics Conference (CCC), pp. 77–81, August 2016
Moussaileb, R., Bouget, B., Palisse, A., Le Bouder, H., Cuppens, N., Lanet, J.L.: Ransomware’s early mitigation mechanisms. In: Proceedings of the 13th International Conference on Availability, Reliability and Security, ARES 2018, pp. 2:1–2:10. ACM (2018)
Rowe, N.C.: Measuring the effectiveness of honeypot counter-counterdeception. In: Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS 2006), vol. 6, pp. 129c–129c, January 2006
Russinovich, M.E., Solomon, D.A., Ionescu, A.: Windows Internals. Pearson Education (2012)
Scaife, N., Carter, H., Traynor, P., Butler, K.R.B.: Cryptolock (and drop it): stopping ransomware attacks on user data. In: 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS), pp. 303–312, June 2016
Sgandurra, D., Muñoz-González, L., Mohsen, R., Lupu, E.C.: Automated dynamic analysis of ransomware: benefits, limitations and use for detection. CoRR abs/1609.03020 (2016). http://arxiv.org/abs/1609.03020
WatchPoint Data: Cryptostopper (2018). https://www.watchpointdata.com/cryptostopper
Webroot: 2018 Webroot threat report mid-year update. Technical report, Webroot Inc., September 2018. https://www.webroot.com/download_file/2780
Yuill, J., Zappe, M., Denning, D., Feer, F.: Honeyfiles: deceptive files for intrusion detection. In: Proceedings of the IEEE Workshop on Information Assurance. United States Military Academy, West Point (2004)
Acknowledgements
This work was partially funded by European Union’s Horizon 2020 research and innovation programme under grant agreement No. 779391 (FutureTPM) and by Luxembourg National Research Fund (FNR) under the project PoC18/13234766-NoCry PoC.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Genç, Z.A., Lenzini, G., Sgandurra, D. (2019). On Deception-Based Protection Against Cryptographic Ransomware. In: Perdisci, R., Maurice, C., Giacinto, G., Almgren, M. (eds) Detection of Intrusions and Malware, and Vulnerability Assessment. DIMVA 2019. Lecture Notes in Computer Science(), vol 11543. Springer, Cham. https://doi.org/10.1007/978-3-030-22038-9_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-22038-9_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-22037-2
Online ISBN: 978-3-030-22038-9
eBook Packages: Computer ScienceComputer Science (R0)