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Information security breaches due to ransomware attacks - a systematic literature review
Open accessAbstract
Ransomware is the most predominant cyber threat in the digital infrastructure. The attackers launching ransomware attacks use different techniques to hijack the users’ or organizations’ files and resources to demand ransom in exchange to free the encrypted/captured data or resources. Although there are many malware attacks, ransomware is considered most dangerous as it imposes a high financial burden on the organization. The crypto-currency is an untraceable payment method that the attacker uses to receive ransom from victims to conceal his/her identity and location. This still creates challenges to trace the attacker or attackers’ networks. The article uses the systematic literature review (SLR) approach to provide significant study on the ransomware attacks as it is the area that requires top most attention in critical infrastructure. The paper briefs the various types of ransomware, vulnerabilities, attack methodologies, impacts, mitigation and prevention techniques of the attacks. This research study is mainly focused on Windows OS vulnerabilities. These findings in the survey will be highly beneficial to understand the effects of ransomware attacks in critical infrastructure environments and the use of machine learning to detect and prevent these attacks.
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In Prime MotorX's perception layer, a design vulnerability in legacy wireless sensors is exploited by ransomware attacks.
“In this scenario, the exploited vulnerability is Design, and the launched attack is a Ransomware [54], [55] on the Legacy Sensor.”
From A Critical Overview of Industrial Internet of Things Security and Privacy Issues Using a Layer-Based Hacking Scenario (2023) · cites this paper as [54]
Section 3.2 describes ransomware using exploits, and Section 3.1 discusses its impact on IoT devices. Neither section identifies a design vulnerability in legacy wireless sensors or an attack on Prime MotorX's perception layer; the paper focuses on ransomware targeting Windows-based critical infrastructure devices. (LLM, conf=0.84)
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Cites
Brereton, P., Kitchenham, B. A., Budgen, D., Turner, M., & Khalil, M. (2007) · Journal of Systems and Software · doi:10.1016/j.jss.2006.07.009 · Cited by 2,394
Artificial intelligence in marketing: Systematic review and future research direction
Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2020.100002 · Cited by 905
Automatic analysis of malware behavior using machine learning
Rieck, K., Trinius, P., Willems, C., & Holz, T. (2011) · Journal of Computer Security · doi:10.3233/jcs-2010-0410 · Cited by 728
Survey of machine learning techniques for malware analysis
Ucci, D., Aniello, L., & Baldoni, R. (2018) · Computers & Security · doi:10.1016/j.cose.2018.11.001 · Cited by 631
Learning and Classification of Malware Behavior
Rieck, K., Holz, T., Willems, C., et al. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-70542-0_6 · Cited by 592
Adversarial Examples for Malware Detection
Grosse, K., Papernot, N., Manoharan, P., Michael Backes, A., & McDaniel, P. D. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66399-9_4 · Cited by 584
Android Security: A Survey of Issues, Malware Penetration, and Defenses
Faruki, P., Bharmal, A., Laxmi, V., Ganmoor, V., Gaur, M. S., Conti, M., & Rajarajan, M. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2386139 · Cited by 560
Cutting the Gordian Knot: A Look Under the Hood of Ransomware Attacks
Kharraz, A., Robertson, W., Balzarotti, D., Bilge, L., & Kirda, E. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-20550-2_1 · Cited by 458
A taxonomy of network and computer attacks
Hansman, S., & Hunt, R. (2005) · Computers & Security · doi:10.1016/j.cose.2004.06.011 · Cited by 402
Applications of text mining in services management: A systematic literature review
Kumar, S., Kar, A. K., & Ilavarasan, P. V. (2021) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2021.100008 · Cited by 331
Continella, A., Guagnelli, A., Zingaro, G., Pasquale, G. D., Barenghi, A., Zanero, S., & Maggi, F. (2016) · Annual Computer Security Applications Conference · doi:10.1145/2991079.2991110 · Cited by 273
Automated Dynamic Analysis of Ransomware: Benefits, Limitations and use for DetectionPreprint
Sgandurra, D., Muñoz-González, L., Mohsen, R., & Lupu, E. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.03020 · Cited by 241
A hybrid deep learning image-based analysis for effective malware detection
Venkatraman, S., Alazab, M., & Vinayakumar, R. (2019) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2019.06.006 · Cited by 239
A Survey on Malware Analysis Techniques: Static, Dynamic, Hybrid and Memory Analysis
Sihwail, R., Omar, K., & Ariffin, K. A. Z. (2018) · International Journal on Advanced Science Engineering and Information Technology · doi:10.18517/ijaseit.8.4-2.6827 · Cited by 234
Machine learning in cybersecurity: A review
Handa, A., Sharma, A., & Shukla, S. K. (2019) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1306 · Cited by 225
Kolodenker, E., Koch, W., Stringhini, G., & Egele, M. (2017) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3052973.3053035 · Cited by 202
Software-defined networking-based crypto ransomware detection using HTTP traffic characteristics
Cabaj, K., Gregorczyk, M., & Mazurczyk, W. (2017) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2017.10.012 · Cited by 196
An emerging threat Fileless malware: a survey and research challenges
Sudhakar, & Kumar, S. (2020) · Cybersecurity · doi:10.1186/s42400-019-0043-x · Cited by 190
Detecting Ransomware with Honeypot Techniques
Moore, C. (2016) · Cybersecurity and Cyberforensics Conference (CCC) · doi:10.1109/ccc.2016.14 · Cited by 159
RansomWall: A layered defense system against cryptographic ransomware attacks using machine learning
Shaukat, S. K., & Ribeiro, V. J. (2018) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets.2018.8328219 · Cited by 146
Redemption: Real-Time Protection Against Ransomware at End-Hosts
Kharraz, A., & Kirda, E. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66332-6_5 · Cited by 139
Machine Learning Based File Entropy Analysis for Ransomware Detection in Backup Systems
Lee, K., Lee, S., & Yim, K. (2019) · IEEE Access · doi:10.1109/access.2019.2931136 · Cited by 128
RWGuard: A Real-Time Detection System Against Cryptographic Ransomware
Mehnaz, S., Mudgerikar, A., & Bertino, E. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00470-5_6 · Cited by 123
A Cyber-Kill-Chain based taxonomy of crypto-ransomware features
Dargahi, T., Dehghantanha, A., Bahrami, P. N., Conti, M., Bianchi, G., & Benedetto, L. (2019) · Journal of Computer Virology and Hacking Techniques · doi:10.1007/s11416-019-00338-7 · Cited by 108
Automatic Ransomware Detection and Analysis Based on Dynamic API Calls Flow Graph
Chen, Z., Kang, H.-S., Yin, S.-N., et al. (2017) · ACM International Conference on Research in Adaptive and Convergent Systems (RACS) · doi:10.1145/3129676.3129704 · Cited by 100
Adversarial Examples for CNN-Based Malware Detectors
Chen, B., Ren, Z., Yu, C., Hussain, I., & Liu, J. (2019) · IEEE Access · doi:10.1109/access.2019.2913439 · Cited by 77
Blockchain for Businesses: A Systematic Literature Review
Grover, P., Kar, A. K., & Ilavarasan, P. V. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-02131-3_29 · Cited by 69
What Petya/NotPetya Ransomware Is and What Its Remidiations Are
Fayi, S. Y. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-77028-4_15 · Cited by 67
MaldomDetector: A system for detecting algorithmically generated domain names with machine learning
Almashhadani, A. O., Kaiiali, M., Carlin, D., et al. (2020) · Computers & Security · doi:10.1016/j.cose.2020.101787 · Cited by 66
2entFOX: A framework for high survivable ransomwares detection
Ahmadian, M. M., & Shahriari, H. R. (2016) · doi:10.1109/iscisc.2016.7736455 · Cited by 63
Bio-inspired for Features Optimization and Malware Detection
Razak, M. F. A., Anuar, N. B., Othman, F., et al. (2017) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-017-2951-y · Cited by 63
Detection and Elimination of Spyware and Ransomware by Intercepting Kernel-Level System Routines
Javaheri, D., Hosseinzadeh, M., & Rahmani, A. M. (2018) · IEEE Access · doi:10.1109/access.2018.2884964 · Cited by 63
A Survey on Situational Awareness of Ransomware Attacks—Detection and Prevention Parameters
Silva, J. A. H., López, L. I. B., Caraguay, Á. L. V., & Hernández-Álvarez, M. (2019) · Remote Sensing · doi:10.3390/rs11101168 · Cited by 63
Network malware classification comparison using DPI and flow packet headers
Boukhtouta, A., Mokhov, S. A., Lakhdari, N.-E., Debbabi, M., & Paquet, J. (2015) · Journal of Computer Virology and Hacking Techniques · doi:10.1007/s11416-015-0247-x · Cited by 60
Information Technology - New Generations
(2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-77028-4 · Cited by 59
UNVEIL: A large-scale, automated approach to detecting ransomware (keynote)
Kirda, E. (2017) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner.2017.7884603 · Cited by 54
A 0-Day Aware Crypto-Ransomware Early Behavioral Detection Framework
Al‐rimy, B. A. S., Maarof, M. A., & Shaid, S. Z. M. (2017) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-319-59427-9_78 · Cited by 52
The static analysis of WannaCry ransomware
Hsiao, S.-C., & Kao, D. (2018) · International Conference on Advanced Communication Technology · doi:10.23919/icact.2018.8323679 · Cited by 43
2010 International Symposium on Information Technology
(2010) · doi:10.1109/itsim16240.2010 · Cited by 43
Keshavarzi, M., & Ghaffary, H. R. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100233 · Cited by 41
On Deception-Based Protection Against Cryptographic Ransomware
Genç, Z. A., Lenzini, G., & Sgandurra, D. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-22038-9_11 · Cited by 36
Contemporary cybercrime: A taxonomy of ransomware threats & mitigation techniques
Nadir, I., & Bakhshi, T. (2018) · International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) · doi:10.1109/icomet.2018.8346329 · Cited by 34
Handbook of Research on Cloud Computing and Big Data Applications in IoT
Gupta, B. B. 1., & Agrawal, D. P. 1. (2018) · Advances in computer and electrical engineering book series · doi:10.4018/978-1-5225-8407-0 · Cited by 28
Smartphone malware detection model based on artificial immune system
Wu, B. Y., Lu, T., Zheng, K., et al. (2014) · China Communications · doi:10.1109/cc.2014.7022530 · Cited by 25
Critical Infrastructures, Key Resources, Key Assets
Gheorghe, A. V., Vamanu, D. V., Katina, P. F., et al. (2017) · Topics in safety, risk, reliability and quality · doi:10.1007/978-3-319-69224-1 · Cited by 25
Critical Infrastructures, Key Resources, and Key Assets
Gheorghe, A. V., Vamanu, D. V., Katina, P. F., et al. (2017) · Topics in safety, risk, reliability and quality · doi:10.1007/978-3-319-69224-1_1 · Cited by 18
Chadha, S., & Kumar, U. (2017) · International Conference on Computing, Communication and Automation · doi:10.1109/ccaa.2017.8229926 · Cited by 18
2017 Cybersecurity and Cyberforensics Conference (CCC)
(2017) · doi:10.1109/ccc42680.2017 · Cited by 15
Adversarial Attacks and Defenses in Malware Detection Classifiers
John, T. S., & Thomas, T. (2019) · Advances in computer and electrical engineering book series · doi:10.4018/978-1-5225-8407-0.ch007 · Cited by 13
Watch out! Doxware on the way…
Moussaileb, R., Navas, R. E., & Cuppens, N. (2020) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2020.102668 · Cited by 9
An Overview of Ransomware in the Windows Platform
Alzahrani, A., Alshehri, A., Alharthi, R. S., et al. (2017) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci.2017.106 · Cited by 7
Cybercrimes Solutions using Digital Forensic Tools
Kamble, D., Jain, N., & Deshpande, S. (2015) · International Journal of Wireless and Microwave Technologies · doi:10.5815/ijwmt.2015.06.02 · Cited by 6
Chong, H. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.10.065 · Cited by 3
Watch Out! Doxware on the Way...
Moussaileb, R., Berti, C., Deboisdeffre, G., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-41568-6_18 · Cited by 3
Alsagoff, S. N. (2010) · International Symposium on Information Technology · doi:10.1109/itsim.2010.5561600 · Cited by 2
Flagged first, then by citations
Research works printed but not linked: 3
- Kitchenham, 2004, 33, 1
- Patyal, 2017, Multi-layered defense architecture against ransomware, International Journal of Business and Cyber Security, 1
- Kiraz, 2017, Detecting large integer arithmetic for defense against crypto ransomware, Cryptology ePrint Archive, Report, 558, 2017
Cited by
Sorted by citations
Explainable Artificial Intelligence in CyberSecurity: A Survey
Capuano, N., Fenza, G., Loia, V., & Stanzione, C. (2022) · IEEE Access · doi:10.1109/access.2022.3204171 · Cited by 330
“For the specific field of phishing interesting approach is defined in [61] and for ransomware attacks in [62].”
BackgroundEyeleko, A. H., & Feng, T. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3308195 · Cited by 38
“In this scenario, the exploited vulnerability is Design, and the launched attack is a Ransomware [53], [54] on the Legacy Sensor.”
BackgroundImage mining applications for underwater environment management-A review and research agenda
Nair, R. S., Agrawal, R., Domnic, S., & Kumar, A. (2021) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2021.100023 · Cited by 34
Metadata
- Authors
- Reshmi, T. R.
- Affiliations
- Society for Electronic Transactions and Security (IN)
- Corresponding author
- Reshmi, T.R.
- ORCID
- Reshmi, T.R.
- Venue
- International Journal of Information Management Data Insights
- Publisher
- Elsevier BV
- Published
- 2021-11
- Volume
- 1
- Issue
- 2
- Pages
- 100013
- ISSN
- 2667-0968 (ISSN-L)
- OpenAlex
- W3159464979
- Cited by
- 120
- References
- 62
- Citations by year
- 2026: 11 · 2025: 17 · 2024: 33 · 2023: 31 · 2022: 19 · 2021: 9
- FWCI
- 3.98
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Journal of Information Management Data Insights (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Software › Advanced Malware Detection Techniques
- Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
- Social Sciences › Political Science and International Relations › Cybersecurity and Cyber Warfare Studies
- Keywords
- ransomware, information security breaches, critical infrastructure, ransomware prevention, machine learning-based malware detection, attack impact
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