DECEPTION BASED TECHNIQUES AGAINST RANSOMWARES: A SYSTEMATIC REVIEW
Abstract
Ransomware is the most prevalent emerging business risk nowadays. It seriously affects business continuity and operations. According to Deloitte Cyber Security Landscape 2022, up to 4000 ransomware attacks occur daily, while the average number of days an organization takes to identify a breach is 191. Sophisticated cyber-attacks such as ransomware typically must go through multiple consecutive phases (initial foothold, network propagation, and action on objectives) before accomplishing its final objective. This study analyzed decoy-based solutions as an approach (detection, prevention, or mitigation) to overcome ransomware. A systematic literature review was conducted, in which the result has shown that deception-based techniques have given effective and significant performance against ransomware with minimal resources. It is also identified that contrary to general belief, deception techniques mainly involved in passive approaches (i.e., prevention, detection) possess other active capabilities such as ransomware traceback and obstruction (thwarting), file decryption, and decryption key recovery. Based on the literature review, several evaluation methods are also analyzed to measure the effectiveness of these deception-based techniques during the implementation process.
Downloads
References
SonicWall, "2022 SonicWall Cyber Threat Report," 2022. [Online]. Available: https://www.infopoint-security.de/media/2022-sonicwall-cyber-threat-report.pdf. [Accessed 12 12 2022].
K. Mehrotra and W. Turton, "CNA Financial Paid $40 Million in Ransom After March Cyberattack," Bloomberg Law, 21 Mei 2021. [Online]. Available: https://news.bloomberglaw.com/insurance/cna-financial-paid-40-million-in-ransom-after-march-cyberattack. [Accessed 10 12 2022]
Australian National Cyber Security Centre, "Ransomware: Measures for preventing, limiting and recovering from a ransomware," Juni 2020. [Online]. Available: https://english.ncsc.nl/binaries/ncsc-en/documenten/factsheets/2020/june/30/factsheet-ransomware/71059_NCSC_FS+Ransomeware+EN_WEB.pdf. [Accessed 9 12 2022].
S. H. Kok, A. Abdullah, N. Jhanjhi and M. Supramaniam, "Ransomware, Threat and Detection Techniques: A Review," International Journal of Computer Science and Network Security, vol. 19, no. 2, pp. 136-146, 2019.
C. Beaman, A. Barkworth, T. D. Akande, S. Hakak and M. K. Khan, "Ransomware: Recent advances, analysis, challenges and future research directions," Computers & Security, 2021.
L. Y. Connoly , D. S. Wall, M. Lang and B. Oddson, "An empirical study of ransomware attacks on organizations: an assessment of severity and salient factors affecting vulnerability," Journal of Cybersecurity, vol. 6, no. 1, pp. 1-18, 2020.
Restore Record Management, "Ransomware – How safe is your organisation from this ever increasing threat?," Restore Record Management, 14 Juni 2021. [Online]. Available: https://www.restore.co.uk/Records/Resource-Hub/News/ransomware-how-safe-is-your-organisation-from-this-ever-increasing-threat. [Accessed 15 Desember 2022].
H. Madani, N. Ouerdi, A. Boumesaoud and A. Azizi, "Classification of ransomware using different types of neural networks," Science Reports, vol. 12, no. 4770, 2022.
A. M. Maigida, S. M. Abdulhamid, M. Olalere, J. K. Alhassan, H. Chiroma and E. G. Dada, "Systematic literature review and metadata analysis of ransomware attacks and detection mechanisms," Journal of Reliable Intelligent Environments, vol. 5, pp. 67-89, 2019.
B. Yamany, M. S. Elsayed, A. D. Jurcut, N. Abdelbaki and M. Azer, "A New Scheme for Ransomware Classification and Clustering Using Static Features," MDPI Electronics Journal, vol. 11, no. 20, pp. 1-26, 2022.
T. Dargahi, A. Dehghantanha and P. Bahrami, "A Cyber-Kill-Chain based taxonomy of crypto-ransomware features," Journal of Computer Virology and Hacking Techniques , vol. 15, pp. 277-305, 2019.
R. Moussaileb, N. B. Cuppens, J.-L. Lanet and H. Le Bouder, "A Survey On Windows-Based Ransomware Taxonomy And Detection Mechanisms: Case Closed?," ACM Computing Surveys, vol. 54, no. 6, pp. 1-36, 2022.
J. Papalitsas, S. Rauti, J. Tammi and V. Leppanen, A Honeypot Proxy Framework for Deceiving Attackers with Fabricated Content, Springer, 2018.
TrapX Security, "Retail Point-of-Sale," 4 April 2017. [Online]. Available: https://img1.wsimg.com/blobby/go/8794a89a-ce4f-40d0-9882-789c102f395b/downloads/1cl9dmuf7_101089.pdf?ver=1615661407182. [Accessed 21 Desember 2022].
P. V. Ross R, G. R, B. D and M. R., "Developing Cyber-Resilient Systems: A Systems Security Engineering Approach," Desember 2021. [Online]. Available: https://csrc.nist.gov/publications/detail/sp/800-160/vol-2-rev-1/final. [Accessed 12 Desember 2022].
L. Zhang and V. L. L. Thing, "Three Decades of Deception Techniques in Active cyber defense - Retrospect and outlook," Computers & Security, 2021.
X. Han , N. Kheir and D. Balzarotti, "Deception Techniques In Computer Security: A Research Perspective," ACM Computer Survey, vol. 1, no. 1, pp. 1-36, 2019.
C. Dalamagkas, P. Sarigiannidis, D. Ioannidis, E. Iturbe, O. Nikolis, F. Ramos, E. Rios, A. Sarigiannidis and D. Tzovaras, "A survey on honeypots, honeynets and their applications on smart grid," in IEEE Conference on Network Softwarization (NetSoft), 2019.
W. Fan, Z. Du, D. Fernandez and V. A. Villagra, "Enabling an anatomic view to investigate honeypot systems: A survey," IEEE Systems Journal, vol. 12, no. 4, pp. 3906-3919, 2018.
A. Mairh, D. Barik, K. Verma and D. Jena, "Honeypot in network security: A survey," in International Conference on Communication, Computing & Security, New York, 2011 .
R. M. Campbell, K. Padayachee and T. Masombuka, "A survey of honeypot research: Trends and opportunities," in 2015 10th international Conference for Internet Technology and Secured Transactions, London, 2015.
L. Zobal, D. Kolar and R. Fudjiak, "Current State of honeypots and deception strategies in cybersecurity," in 11th International Congress on Ultra Modern Telecommunications and Control System and Workshops (ICUMT), Dublin, 2019.
Fan, Wenjun; Du, Zhihui; Fernandez, David;, "Taxonomy of honeynet solutions," in SAI Intelligent Systems Conference 2015, London, 2015.
J. Yuill, M. Zappe, D. Denning and F. Feer, "Honeyfiles: Deceptive Files for Intrusion Detection," in Proceedings from the Fifth Annual IEEE SMC Information Assurance Workshop, New York, 2004.
H. Cheng, W. Li, P. Wang, C.-H. Chu and K. Liang, "Incrementally Updateable Honey Password Vaults," in The Proceedings of the 30th USENIX Security Symposium, 2021.
S. Srinivasa, J. M. Pedersen and E. Vasilomanolakis, "Towards systematic honeytoken fingerprinting," in International Conference on Security of Information and Networks (ACM SIN), Istanbul, 2020.
M. M. Andersen, H. David and E. Vasilomanolakis, "Honeysweeper: Towards stealthy honeytoken fingerprinting techniques," in 27th Nordic Conference on Secure IT Systems, Reykjavik, 2022.
J. H. Cho, D. P. Sharma, H. Alavizadeh, S. Yoon, N. Ben-Asher, T. J. Moore and F. F. Nelson, "Toward proactive, adaptive defense: A survey on moving target defense.," IEEE Communications Surveys & Tutorials, vol. 22, no. 1, pp. 709-745, 2020.
G. L. Cai, B. S. H. W. Wang and T. Z. Wang, "Moving target defense: state of the art and characteristics," Frontiers of Information Technology & Electronic Engineering, vol. 17, no. 11, pp. 1122-1153, 2016.
C. Lei, H. Q. Zhang, J. L. Tan, Y. C. Zhang and X. H. Liu, "Moving target defense techniques: A survey.," Security and Communication Networks, 2018.
M. Asif and Y. Al-Harthi, "Intrusion detection system using Honey Token based Encrypted Pointers to mitigate cyber threats for critical infrastructure networks," in 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), San Diego, 2014.
M. Almeshekah and E. Spafford, "Cyber Deception," in Cyber Security Deception, Springer, 2016.
W. Steingartner and D. Galinec, "Cyber threats and cyber deception in hybrid warfare," Acta Polytechnica Hungarica, vol. 18, no. 3, pp. 25-45, 2021.
U. Urooj, B. A. S. Al-rimy, A. Zainal, F. A. Ghaleb and M. A. Rassam, "Ransomware Detection Using the Dynamic Analysis and Machine Learning: A Survey and Research Directions," Applied Science , vol. 12, no. 1, pp. 1-45, 2022.
S. Kok, A. Abdullah, N. Jhanjhi and M. Supramaniam, "Ransomware, threat and detection techniques: A review," International Journal Computer Science Network Security, vol. 19, no. 2, 2019.
S. J. Lee, H. Y. Shim, Y. R. Lee, T. R. Park, S. H. Park and I. G. Lee, "Study on systematic ransomware detection techniques," in 2022 24th International Conference on Advanced Communication Technology (ICACT), Phoenix Pyeongchang, 2022.
C. V. Bijitha, R. Sukumaran and H. V. Nath, "A survey on ransomware detection techniques," in Secure Knowledge Management In Artificial Intelligence Era: 8th International Conference, SKM 2019, Goa, 2020.
W. Z. Zakaria, M. F. Abdollah and A. M. Ariffin, "On ransomware detection," in Proceedings of the seventh international conference on informatics and applications (ICIA2018), 2018.
L. B. Bhagwat and B. M. Patil, "Detection of ransomware attack: A review," in Proceeding of International Conference on Computational Science and Applications: ICCSA, 2019.
S. Kamil, H. S. A. S. Norul, A. Firdaus and O. L. Usman, "The rise of ransomware: A review of attacks, detection techniques, and future challenges," in International Conference on Business Analytics for Technology and Security (ICBATS), 2022.
S. R. Davies, R. Macfarlane and W. J. Buchanan, "Review of Current Ransomware Detection Techniques," in Proc. of the 7 th International Conference on Engineering and Emerging Technologies (ICEET), 2022.
J. P. Tailor and A. D. Patel, "A comprehensive survey: ransomware attacks prevention, monitoring and damage control," International Journal of Innovative Research, vol. 4, no. 15, pp. 116-121, 2017.
B. Al-Fuhaidi, W. Al-Sorori, N. Maqtary, A. Al-Hashedi and S. Al-Taweel, "Literature Review on Cyber Attacks Detection and Prevention Schemes," in International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE), 2021.
Z. Manjezi and R. Botha, "Preventing and Mitigating Ransomware - A Systematic Literature Review," in 17th International Conference, ISSA 2018, Pretoria, 2019.
A. Kapoor, A. Gupta, R. Gupta, S. Tanwar, G. Sharma and I. E. Davidson, "Ransomware detection, avoidance, and mitigation scheme: a review and future directions," Sustainability, vol. 14, no. 1, 2021.
H. Oz, A. Aris, A. Levi and A. S. Uluagac, "A survey on ransomware: Evolution, taxonomy, and defense solutions," ACM Computing Surveys (CSUR), vol. 54, no. 11, pp. 1-37, 2022.
H. R. M. Husny, M. S. M. Yussof, N. Y. Abdullah and W. H. W. Ismail, "Detection and Mitigation of Virus Ransomware," Journal of Computing Technologies and Creative Content (JTec), vol. 5, no. 1, pp. 8-13, 2020.
Z. Abdullah, F. W. Muhadi, M. M. H. I. R. A. Saudi and C. F. M. Foozy, "Android ransomware detection based on dynamic obtained features," in International Conference on Soft Computing and Data Mining, Langkawi, 2020.
S. Alsoghyer and I. Almomani, "Ransomware detection system for Android applications," Electronics, vol. 8, no. 8, 2019.
S. Sharma, R. Kumar and C. R. Krishna., "A survey on analysis and detection of Android ransomware," Concurrency and Computation: Practice and Experience, vol. 33, no. 16, 2021.
N. Alzahrani and D. Alghazzawi, "A review on android ransomware detection using deep learning techniques.," in Proceedings of the 11th international conference on management of digital EcoSystems, 2019.
R. Moussaileb, N. Cuppens, J. L. Lanet and H. L. & Bouder, "A survey on windows-based ransomware taxonomy and detection mechanisms," ACM Computing Surveys (CSUR), vol. 54, no. 6, pp. 1-36, 2021.
N. Soltani, A. M. Rahmani, M. Bohlouli and M. Hosseinzadeh, "Artificial intelligence empowered threat detection in the Internet of Things: A systematic review," Concurrency and Computation: Practice and Experience, vol. 34, no. 22, 2022.
A. Cimitile, F. Mercaldo, V. Nardone, A. Santone and C. A. Visaggio, "Talos: no more ransomware victims with formal methods," International Journal of Information Security, vol. 17, pp. 719-738, 2018.
M. Abdullahi, Y. Baashar, H. Alhussian, A. Alwadain, N. Aziz, L. Capretz and S. Abdulkadir, "Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review," Electronics, vol. 11, no. 198, 2022.
E. Berrueta, D. Morato, E. Magaña and M. Izal, "A survey on detection techniques for cryptographic ransomware," IEEE Access, vol. 7, pp. 144925-144944, 2019.
A. Alqahtani and F. T. Sheldon, "A survey of crypto ransomware attack detection methodologies: an evolving outlook.," Sensors, vol. 22, no. 5, 2022.
D. Su, J. Liu, X. Wang and W. Wang, "Detecting Android locker-ransomware on chinese social networks," IEEE Access, vol. 7, pp. 20381-20393, 2018.
D. Su, J. Liu, X. Wang and W. Wang, "Detecting Android locker-ransomware on chinese social networks," IEEE Access, vol. 7, p. 20381–20393, 2018.
H. Sultan, A. Khalique, S. I. Alam and S. Tanweer, "A SURVEY ON RANSOMEWARE: EVOLUTION, GROWTH, AND IMPACT," International Journal of Advanced Research in Computer Science, vol. 9, no. 2, 2021.
C. Moore, "Detecting Ransomware with Honeypot Techniques," in 2016 Cybersecurity and Cyberforensics Conference (CCC), Amman, Jordan, 2016.
P. Brereton, B. A. Kitchenham, D. Budgen, M. Turner and M. Khalil, "Lessons from applying the systematic literature review processwithin the software engineering domain," The Journal of System and Software, vol. 80, no. 4, pp. 571-583, 2007.
Y. Pan, X. Ge, C. Fang and Y. Fan, "A Systematic Literature Review of Android Malware Detection Using Static Analysis," IEEE Access, vol. 8, pp. 116363-116379, 2020.
D. Liebowitz, S. Nepal, K. Moore, C. J. Christoper, S. S. Kanhere, D. Nguyen, R. C. Timmer, M. Longland and K. Rathakumar, "Deception for Cyber Defence: Challenges and Opportunities," in 2021 Third IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), Atlanta, Georgia, 2021.
N. Cifranic, J. Romero-Mariona, B. Souza and R. Hallman, "Decepti-SCADA: A Framework for Actively Defending Networked Critical Infrastructures," in 5th International Conference on Internet of Things, Big Data and Security, Prague, 2020.
C. Wang and Z. Lu, "Cyber Deception: Overview and the Road Ahead," IEEE Security and Privacy Magazine, vol. 16, no. 2, pp. 80-85, 2018.
W. Wang, J. Bickford, I. Murynets, R. Subbaraman, A. G. Forte and G. Singaraju, "Detecting targeted attacks by multilayer deception," Journal of Cyber Security and Mobility, vol. 2, pp. 175-199, 2013.
T. W. Edgar and D. O. Manz, "Honeypot," in Research Methods for Cyber Security, Elsevier, 2017.
M. H. Lopez and C. F. L. Resendez, "Honeypots: Basic Concepts, Classification and Educational Use as Resources in Information Security Education and Courses," in Proceedings of the Informing Science & IT Education Conference , 2008.
N. Titarmare, N. Hargule and A. Gupta, "An Overview of Honeypot Systems," International Journal of Computer Sciences and Engineering, vol. 7, no. 2, pp. 394-397, 2019.
J. Franco, A. Aris, B. Canberk and A. S. Uluagac, "A Survey of Honeypots and Honeynets for Internet of Things, Industrial Internet of Things, and Cyber-Physical Systems," IEEE Communications Surveys & Tutorials, pp. 2351-2383, 2021.
F. A. Alaba, A. Jegede and ', "Ransomware Attacks on Remote Learning Systems in 21st Century: A Survey," Biomedical Journal of Scientific & Technical Research, vol. 35, no. 1, pp. 27322-27330, 2021.
A. P. de Barros, "RES: Protocol Anomaly Detection IDS - Honeypots," 21 Februari 2003. [Online]. Available: https://seclists.org/focus-ids/2003/Feb/95. [Accessed 10 Desember 2022].
Fortinet, "Honey Tokens," Fortinet Cyber Readiness Center and Breaking Threat Intelligence, 2023. [Online]. Available: https://www.fortinet.com/resources/cyberglossary/honey-tokens. [Accessed 13 November 2022].
L. Martin, "Cyber Kill Chain Framework," 2023, [Online]. Available: https://www.lockheedmartin.com/en-us/capabilities/cyber/cyber-kill-chain.html. [Accessed 2 Desember 2022].
Q. Khan, A. Mirza, M. Brown, O. Halling, L. Shand and A. Alam, "Ransomware Analysis using Cyber Kill Chain," in 2021 8th International Conference on Future Internet of Things and Cloud (FiCloud), Rome, 2021.
M. H. Almeshekah and E. H. Spafford, "Planning and integrating deception into computer security defenses," ACM Workshop on New Security Paradigms Workshop (NSPW), 2014.
F. Cohen, "A note on the role of deception in information protection," Computers & Securitt, 1998.
G. Kontaxis, M. Polychronakis and A. D. Keromytis, "Computational Decoys for Cloud Security," in Secure Cloud Computing, 2014.
F. Araujo, K. W. Hamlen, S. Biedermann and S. Katzenbeisser, "From patches to honeypatches: Lightweight attacker misdirection, deception, and disinformation," in ACM SIGSAC conference on computer and communications security (CCS), 2014.
S. Webb, J. Caverlee and C. Pu, "Social Honeypots: Making Friends With A Spammer Near You," 2008. [Online]. Available: https://people.engr.tamu.edu/caverlee/pubs/webb08socialhoneypots.pdf. [Accessed 21 November 2022].
K. Borders, L. Falk and A. Prakash, "OpenFire: Using deception to reduce network attacks.," Security and Privacy in Communications Networks and the Workshops, 2007.
T. Liston, " LaBrea:“Sticky” Honeypot and IDS," 2001. [Online]. Available: https://labrea.sourceforge.io/labrea-info.html. [Accessed 20 Desember 2022].
S. T. Trassare, "A technique for presenting a deceptive dynamic network topology," 2013. [Online]. Available: https://core.ac.uk/download/pdf/36725616.pdf. [Accessed 12 November 2022].
B. M. Bowen, V. Kemerlis, P. Prabhu, A. D. Keromytis and S. J. Stolf, "Automating the Injection of Believable Decoys to Detect Snooping," in Proceedings of the Third ACM Conference on Wireless Network Security, 2010.
S. Chakravarty, G. Portokalidis, M. Polychronakis and A. D. Keromytis, "Detecting traffic snooping in tor using decoys," in Workshop on Recent Advances in Intrusion Detection, 2011.
Statista Research Department, "Statista," Statista, 7 July 2022. [Online]. Available: https://www.statista.com/statistics/701020/major-operating-systems-targeted-by-ransomware/. [Accessed 19 10 2022].
T. McIntosh, A. S. M. Kayes, Y.-P. P. Chen, A. Ng and P. Watters, "Ransomware Mitigation in the Modern Era: A Comprehensive Review, Research Challenges, and Future Directions," ACM Computing Surveys, vol. 54, no. 9, p. 1–36, 2022.
Y. Feng, C. Liu and B. Liu, "Poster : A New Approach to Detecting Ransomware with Deception," in Proceedings of the 38th IEEE Symposium on Security and Privacy Workshops, San Jose, 2017.
A. Kharaz, S. Arshad, C. Mulliner, W. Robertson and E. Kirda, "UNVEIL: A large-scale, automated approach to detecting ransomware," in IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER), Klagenfurt, 2017.
S. K. Shaukat and V. J. Ribeiro, "RansomWall: A layered defense system against cryptographic ransomware attacks using machine learning," in 10th International Conference on Communication Systems & Networks (COMSNETS), Bengaluru, 2018.
U. Adamu and I. Awan, "Ransomware Prediction Using Supervised Learning Algorithms," in 7th International Conference on Future Internet of Things and Cloud (FiCloud), Istanbul, 2019.
V. Sethia and A. Jayasekar, "Malware Capturing and Analysis using Dionaea Honeypot," in 2019 International Carnahan Conference on Security Technology (ICCST), Chennai, 2019.
S. I. Sajid, J. Wei, B. Abdeen, E. Al-Shaer, M. Islam, W. Diong and L. Khan, "SODA: A System for Cyber Deception Orchestration and Automation," in ACSAC '21: Annual Computer Security Applications Conference, 2021.
Z. Wang, X. Wu, C. Liu, Q. Liu and J. Zhang, "RansomTracer: Exploiting Cyber Deception for Ransomware Tracing," in 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC), Guangzhou, 2018.
J. A. Gomez-Hernandez, L. Alvarez-Gonzalez and P. Garcia-Teodoro, "R-Locker: Thwarting Ransomware Action through a honeyfile-based approach," Computers & Security, vol. 73, pp. 389-398, 2018.
S. Mehnaz, A. Mudgerikar and E. Bertino, "RWGuard: A Real-Time Detection System Against Cryptographic Ransomware," in 21st International Symposium, RAID , Crete, 2018.
J. Lee, J. Lee and J. Hong, "How to Make Efficient Decoy Files for Ransomware Detection?," in RACS '17: Proceedings of the International Conference on Research in Adaptive and Convergent Systems, Krakow, 2017.
S. Sheen, K. A. Asmitha and S. Venkatesan, "R-Sentry: Deception based ransomware detection using file access patterns," Computers and Electrical Engineering, vol. 103, 2022.
E. Kolodenker, W. Koch, G. Stringhini and M. Egele, "PayBreak: Defense Against Cryptographic Ransomware," in Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, Abu Dhabi, 2017.
Z. Wang, C. Liu, J. Qiu, Z. Tian , X. Cui and S. Su, "Automatically Traceback RDP-Based Targeted Ransomware Attacks," Wireless Communications and Mobile Computing, pp. 1-13, 2018.
WatchPoint Data, "Canauri™: Ransomware Protection," [Online]. Available: https://www.canauri.com/features/. [Accessed 12 Desember 2022].
S. S. Chakkaravarthy, D. Sangeetha, M. V. Cruz, V. Vaidehi and B. Raman, "Design of Intrusion Detection Honeypot Using Social Leopard Algorithm to Detect IoT Ransomware Attacks," IEEE Access, vol. 8, pp. 169944-169956, 2020.
M. Karakate, H. Esaki and H. Ochiai, "SDNHive: A Proof-of-Concept SDN and Honeypot System for Defending Against Internal Threats," in ICCNS 2021: 2021 the 11th International Conference on Communication and Network Security, Weihai, 2021.
C. Pascariu and I.-D. Barbu, "Ransomware Honeypot: Honeypot solution designed to detect a ransomware infection identify the ransomware family," in 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), Pitesti, 2019.
S. Wang, H. Zhang, S. Qin, W. Li, T. Tu, A. Shen and W. Liu, "KRProtector: Detection and Files Protection for IoT Devices on Android Without ROOT Against Ransomware Based on Decoys," IEEE INTERNET OF THINGS JOURNAL, vol. 9, no. 19, pp. 18251-18266, 2022.
Z. A. Genc, G. Lenzini and D. Sgandura, "On Deception-Based Protection Against Cryptographic Ransomware," in International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, 2019.
VirusTotal, "Ransomware in a global context," VirusTotal, 2022.
J. Sun, K. Sun and Q. Li, "Towards a Believable Decoy System: Replaying Network Activities from Real System," in 2020 IEEE Conference on Communications and Network Security (CNS), Avignon, 2020.
X. Han, N. Kheir and D. Balzarotti, "Evaluation of Deception-Based Web Attacks Detection," in MTD '17: Proceedings of the 2017 Workshop on Moving Target Defense, Dallas, 2017.
H. Wu, Y. Gu, G. Cheng and Y. Zhou, "Effectiveness Evaluation Method for Cyber Deception Based on Dynamic Bayesian Attack Graph," in CSSE 2020: 2020 3rd International Conference on Computer Science and Software, 2020.
Copyright (c) 2023 canny siska georgina, Setiadi Yazid
This work is licensed under a Creative Commons Attribution 4.0 International License.
