Ransomware threat success factors, taxonomy, and countermeasures: A survey and research directions

A survey of network anomaly detection techniques

Ahmed, M., Mahmood, A., & Hu, J.-K. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2015.11.016 · Cited by 1,708 · see citing works

Intrusion detection system: A comprehensive review

Liao, H.-J., Lin, C. R., Lin, Y.-C., & Tung, K.-Y. (2012) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2012.09.004 · Cited by 1,640 · see citing works

A survey on automated dynamic malware-analysis techniques and tools

Egele, M., Scholte, T., Kirda, E., & Kruegel, C. (2008) · ACM Computing Surveys · doi:10.1145/2089125.2089126 · Cited by 879 · see citing works

A novel hybrid intrusion detection method integrating anomaly detection with misuse detection

Kim, G., Lee, S. M., & Kim, S. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.08.066 · Cited by 593 · see citing works

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 · see citing works

CryptoLock (and Drop It): Stopping Ransomware Attacks on User Data

Scaife, N., Carter, H., Traynor, P., & Butler, K. R. B. (2016) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2016.46 · Cited by 457 · see citing works

Malware Analysis and Classification: A Survey

Gandotra, E., Bansal, D., & Sofat, S. (2014) · Journal of Information Security · doi:10.4236/jis.2014.52006 · Cited by 424 · see citing works

Data Quality in Internet of Things: A state-of-the-art survey

Karkouch, A., Mousannif, H., Al Moatassime, H., & Noel, T. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2016.08.002 · Cited by 378 · see citing works

UNVEIL: a large-scale, automated approach to detecting ransomware

Kharraz, A., Arshad, S., Mulliner, C., Robertson, W., & Kirda, E. (2016) · USENIX Security Symposium · Cited by 362 · see citing works

A Semantic Approach to Host-Based Intrusion Detection Systems Using Contiguousand Discontiguous System Call Patterns

Creech, G., & Hu, J. (2013) · IEEE Transactions on Computers · doi:10.1109/tc.2013.13 · Cited by 357 · see citing works

BitIodine: Extracting Intelligence from the Bitcoin Network

Spagnuolo, M., Maggi, F., & Zanero, S. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-662-45472-5_29 · Cited by 307 · see citing works

A brief study of Wannacry Threat: Ransomware Attack 2017

Mohurle, S., & Patil, M. (2017) · International Journal of Advanced Research in Computer Science · doi:10.26483/ijarcs.v8i5.4021 · Cited by 276 · see citing works

Using quantum key distribution for cryptographic purposes: A survey

Alléaume, R., Branciard, C., Bouda, J., Debuisschert, T., Dianati, M., Gisin, N., Godfrey, M. S., Grangier, P., Länger, T., Lütkenhaus, N., et al. (2014) · Theoretical Computer Science · doi:10.1016/j.tcs.2014.09.018 · Cited by 252 · see citing works

Integrated Static and Dynamic Analysis for Malware Detection

Shijo, P. V., & Salim, A. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.02.149 · Cited by 249 · see citing works

HelDroid: Dissecting and Detecting Mobile Ransomware

Andronio, N., Zanero, S., & Maggi, F. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-26362-5_18 · Cited by 246 · see citing works

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 · see citing works

Unsupervised Anomaly-Based Malware Detection Using Hardware Features

Tang, A., Sethumadhavan, S., & Stolfo, S. J. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11379-1_6 · Cited by 233 · see citing works

Comparative analysis of various ransomware virii

Gazet, A. (2008) · Journal of Computer Virology and Hacking Techniques · doi:10.1007/s11416-008-0092-2 · Cited by 211 · see citing works

A fistful of Bitcoins

Meiklejohn, S., Pomarole, M., Jordan, G., Levchenko, K., McCoy, D., Voelker, G. M., & Savage, S. (2016) · Communications of the ACM · doi:10.1145/2896384 · Cited by 211 · see citing works

Detecting Environment-Sensitive Malware

Lindorfer, M., Kolbitsch, C., & Comparetti, P. M. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23644-0_18 · Cited by 203 · see citing works

PayBreak

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 · see citing works

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 · see citing works

Bitcoin over Tor isn't a Good Idea

Biryukov, A., & Pustogarov, I. (2015) · 2015 IEEE Symposium on Security and Privacy · doi:10.1109/sp.2015.15 · Cited by 193 · see citing works

Efficient and Anonymous Mobile User Authentication Protocol Using Self-Certified Public Key Cryptography for Multi-Server Architectures

He, D., Zeadally, S., Kumar, N., & Wu, W. (2016) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2016.2573746 · Cited by 191 · see citing works

Semantics-Based Online Malware Detection: Towards Efficient Real-Time Protection Against Malware

Das, S., Liu, Y., Zhang, W., & Chandramohan, M. (2015) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2015.2491300 · Cited by 187 · see citing works

Behavior-based features model for malware detection

Galal, H. S., Mahdy, Y. B., & Atiea, M. A. (2015) · Journal of Computer Virology and Hacking Techniques · doi:10.1007/s11416-015-0244-0 · Cited by 180 · see citing works

Malware Detection in Cloud Computing Infrastructures

Watson, M. R., Shirazi, S. N., Marnerides, A. K., Mauthe, A., & Hutchison, D. (2015) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2015.2457918 · Cited by 172 · see citing works

Detecting Ransomware with Honeypot Techniques

Moore, C. (2016) · Cybersecurity and Cyberforensics Conference (CCC) · doi:10.1109/ccc.2016.14 · Cited by 159 · see citing works

Cryptovirology: Extortion-Based Security Threats and Countermeasures

Young, A. L., & Yung, M. M. (1996) · Cited by 157 · see citing works

Malicious sequential pattern mining for automatic malware detection

Fan, Y., Ye, Y., & Chen, L. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.01.002 · Cited by 157 · see citing works

Using Software-Defined Networking for Ransomware Mitigation: The Case of CryptoWall

Cabaj, K., & Mazurczyk, W. (2016) · IEEE Network · doi:10.1109/mnet.2016.1600110nm · Cited by 147 · see citing works

Seeing the Unseen: Revealing Mobile Malware Hidden Communications via Energy Consumption and Artificial Intelligence

Caviglione, L., Gaggero, M., Lalande, J.-F., Mazurczyk, W., & Urbanski, M. (2015) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2015.2510825 · Cited by 142 · see citing works

A Survey of Public-Key Cryptographic Primitives in Wireless Sensor Networks

Shim, K. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2459691 · Cited by 130 · see citing works

Awareness Education as the Key to Ransomware Prevention

Luo, X. R., & Liao, Q. (2007) · Information Systems Security · doi:10.1080/10658980701576412 · Cited by 127 · see citing works

Visualizing Dynamic Bitcoin Transaction Patterns

McGinn, D., Birch, D., Akroyd, D., Molina-Solana, M., Guo, Y., & Knottenbelt, W. J. (2016) · Big Data · doi:10.1089/big.2015.0056 · Cited by 125 · see citing works

The Effective Ransomware Prevention Technique Using Process Monitoring on Android Platform

Song, S., Kim, B., & Lee, S. (2016) · Mobile Information Systems · doi:10.1155/2016/2946735 · Cited by 124 · see citing works

A survey of data mining and social network analysis based anomaly detection techniques

Kaur, R., & Singh, S. (2015) · Egyptian Informatics Journal · doi:10.1016/j.eij.2015.11.004 · Cited by 118 · see citing works

An approach for detection and family classification of malware based on behavioral analysis

Hansen, S. S., Larsen, T. M. T., Stevanovic, M., & Pedersen, J. M. (2016) · 2012 International Conference on Computing, Networking and Communications (ICNC) · doi:10.1109/iccnc.2016.7440587 · Cited by 118 · see citing works

Employing Program Semantics for Malware Detection

Naval, S., Laxmi, V., Rajarajan, M., Gaur, M. S., & Conti, M. (2015) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2015.2469253 · Cited by 113 · see citing works

A Socio-technical Approach to Preventing, Mitigating, and Recovering from Ransomware Attacks

Singh, H., & Sittig, D. F. (2016) · Applied Clinical Informatics · doi:10.4338/aci-2016-04-soa-0064 · Cited by 109 · see citing works

A comprehensive survey of modern symmetric cryptographic solutions for resource constrained environments

Kong, J. H., Ang, L.-M., & Seng, K. P. (2014) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2014.09.006 · Cited by 104 · see citing works

Ransomware Steals Your Phone. Formal Methods Rescue It

Mercaldo, F., Nardone, V., Santone, A., & Visaggio, C. A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-39570-8_14 · Cited by 104 · see citing works

A Survey of Visualization Systems for Malware Analysis

Wagner, M., Fischer, F., Luh, R., Haberson, A., Rind, A., Keim, D. A., & Wolfgang Aigner, A. (2015) · KOPS (University of Konstanz) · doi:10.2312/eurovisstar.20151114 · Cited by 102 · see citing works

R-PackDroid

Maiorca, D., Mercaldo, F., Giacinto, G., Visaggio, C. A., & Martinelli, F. (2017) · ACM Symposium on Applied Computing · doi:10.1145/3019612.3019793 · Cited by 102 · see citing works

Mathematical modeling of the propagation of malware: a review

del Rey, A. M. (2015) · Security and Communication Networks · doi:10.1002/sec.1186 · Cited by 95 · see citing works

Comparative Experimental Infection Study in Dogs with Ehrlichia canis, E. chaffeensis, Anaplasma platys and A. phagocytophilum

Nair, A. D. S., Cheng, C., Ganta, C. K., Sanderson, M. W., Alleman, A. R., Munderloh, U. G., & Ganta, R. R. (2016) · PLoS ONE · doi:10.1371/journal.pone.0148239 · Cited by 90 · see citing works

A three-way decision making approach to malware analysis using probabilistic rough sets

Nauman, M., Azam, N., & Yao, J. (2016) · Information Sciences · doi:10.1016/j.ins.2016.09.037 · Cited by 88 · see citing works

A Survey on Zero-Day Polymorphic Worm Detection Techniques

Kaur, R., & Singh, M. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2014.022714.00160 · Cited by 87 · see citing works

Automated Detection and Analysis for Android Ransomware

Yang, T., Yang, Y. C., Qian, K., Lo, D. C.-T., Qian, Y., & Tao, L. (2015) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-css-icess.2015.39 · Cited by 85 · see citing works

Ransomware: To pay or not to pay?

Everett, C. (2016) · Computer Fraud & Security · doi:10.1016/s1361-3723(16)30036-7 · Cited by 84 · see citing works

Adaptive and scalable Android malware detection through online learning

Narayanan, A. S., Liu, Y., Chen, L., & Liu, J. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727508 · Cited by 81 · see citing works

Malware behavioural detection and vaccine development by using a support vector model classifier

Wang, P., & Wang, Y.-S. (2014) · Journal of Computer and System Sciences · doi:10.1016/j.jcss.2014.12.014 · Cited by 75 · see citing works

An HMM and structural entropy based detector for Android malware: An empirical study

Canfora, G., Mercaldo, F., & Visaggio, C. A. (2016) · Computers & Security · doi:10.1016/j.cose.2016.04.009 · Cited by 75 · see citing works

Network activity analysis of CryptoWall ransomware

Cabaj, K. (2015) · PRZEGLĄD ELEKTROTECHNICZNY · doi:10.15199/48.2015.11.48 · Cited by 71 · see citing works

Connection-monitor & connection-breaker: A novel approach for prevention and detection of high survivable ransomwares

Ahmadian, M. M., Shahriari, H. R., & Ghaffarian, S. M. (2015) · doi:10.1109/iscisc.2015.7387902 · Cited by 68 · see citing works

Editorial: A special section on “Emerging Platform Technologies”

Lee, C., Han, K., & Li, J. (2016) · The Journal of Supercomputing · doi:10.1007/s11227-015-1595-5 · Cited by 67 · see citing works

Ransomware and the Legacy Crypto API

Palisse, A., Bouder, H. L., Lanet, J., Guernic, C. L., & Legay, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54876-0_2 · Cited by 66 · see citing works

2entFOX: A framework for high survivable ransomwares detection

Ahmadian, M. M., & Shahriari, H. R. (2016) · doi:10.1109/iscisc.2016.7736455 · Cited by 63 · see citing works

Support vector machines and malware detection

Singh, T., Troia, F. D., Visaggio, C. A., Austin, T. H., & Stamp, M. (2015) · Journal of Computer Virology and Hacking Techniques · doi:10.1007/s11416-015-0252-0 · Cited by 61 · see citing works

CloudRPS: a cloud analysis based enhanced ransomware prevention system

Lee, J. K., Moon, S. Y., & Park, J. H. (2016) · The Journal of Supercomputing · doi:10.1007/s11227-016-1825-5 · Cited by 57 · see citing works

An Efficient Approach to Detect TorrentLocker Ransomware in Computer Systems

Mbol, F., Robert, J., & Sadighian, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48965-0_32 · Cited by 53 · see citing works

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 · see citing works

Ransomware Digital Extortion: A Rising New Age Threat

Bhardwaj, A., Avasthi, V., Hanumat Sastry, G., & Subrahmanyam, G. V. B. (2016) · Indian Journal of Science and Technology · doi:10.17485/ijst/2016/v9i14/82936 · Cited by 50 · see citing works

DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware

Afifi, F., Anuar, N. B., Shamshirband, S., & Choo, K. R. (2016) · PLoS ONE · doi:10.1371/journal.pone.0162627 · Cited by 50 · see citing works

Detection and classification of malicious JavaScript via attack behavior modelling

Xue, Y., Wang, J., Liu, Y., Xiao, H., Sun, J., & Chandramohan, M. (2015) · International Symposium on Software Testing and Analysis · doi:10.1145/2771783.2771814 · Cited by 46 · see citing works

Malware detection using bilayer behavior abstraction and improved one-class support vector machines

Miao, Q., Liu, J., Cao, Y., & Song, J. (2015) · International Journal of Information Security · doi:10.1007/s10207-015-0297-6 · Cited by 43 · see citing works

A static heuristic approach to detecting malware targets

Zakeri, M., Daneshgar, F. F., & Abbaspour, M. (2015) · Security and Communication Networks · doi:10.1002/sec.1228 · Cited by 39 · see citing works

A Simple Method for Detection of Metamorphic Malware using Dynamic Analysis and Text Mining

Choudhary, S. P., & Vidyarthi, M. D. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.06.031 · Cited by 39 · see citing works

A Real-Time PE-Malware Detection System Based on CHI-Square Test and PE-File Features

Belaoued, M., & Mazouzi, S. (2015) · IFIP advances in information and communication technology · doi:10.1007/978-3-319-19578-0_34 · Cited by 39 · see citing works

An Approach to Detect Remote Access Trojan in the Early Stage of Communication

Jiang, D., & Omote, K. (2015) · IEEE International Conference on Advanced Information Networking and Applications (AINA) · doi:10.1109/aina.2015.257 · Cited by 37 · see citing works

POSTER

Shukla, M., Mondal, S., & Lodha, S. (2016) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2976749.2989051 · Cited by 37 · see citing works

A transparent and scalable anomaly-based DoS detection method

Joldzic, O. V., Djuric, Z. I., & Vuletic, P. V. (2016) · Computer Networks · doi:10.1016/j.comnet.2016.05.004 · Cited by 35 · see citing works

Handbook of Research on Information Security and Assurance

Gupta, J. N. D., & Sharma, S. K. (2009) · IGI Global eBooks · doi:10.4018/978-1-59904-855-0 · Cited by 34 · see citing works

Cryptoviral extortion using Microsoft's Crypto API

Young, A. L. (2006) · International Journal of Information Security · doi:10.1007/s10207-006-0082-7 · Cited by 30 · see citing works

The changing face of malware

Bridges, L. (2008) · Network Security · doi:10.1016/s1353-4858(08)70010-2 · Cited by 29 · see citing works

Ransomware

Luo, X. R., & Liao, Q. (2009) · IGI Global eBooks · doi:10.4018/978-1-59904-855-0.ch001 · Cited by 29 · see citing works

Prescience

Deo, A., Dash, S. K., Suárez‐Tangil, G., Vovk, V., & Cavallaro, L. (2016) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/2996758.2996769 · Cited by 29 · see citing works

A survey on real world botnets and detection mechanisms

Soltani, S., Seno, S. A. H., Nezhadkamali, M., & Budiarto, R. (2014) · International Journal of Information and Network Security (IJINS) · Cited by 28 · see citing works

Static analysis for the detection of metamorphic computer viruses using repeated-instructions counting heuristics

Canfora, G., Iannaccone, A. N., & Visaggio, C. A. (2013) · Journal of Computer Virology and Hacking Techniques · doi:10.1007/s11416-013-0189-0 · Cited by 28 · see citing works

Ransomware Against Police: Diagnosis of Risk Factors via Application of Cyber-Routine Activities Theory

Choi, K. S., Scott, T. M., & LeClair, D. P. (2016) · International Journal of Forensic Science & Pathology · doi:10.19070/2332-287x-1600061 · Cited by 28 · see citing works

In memory detection of Windows API call hooking technique

Shaid, S. Z. M., & Maarof, M. A. (2015) · doi:10.1109/i4ct.2015.7219584 · Cited by 27 · see citing works

A framework for empirical evaluation of malware detection resilience against behavior obfuscation

Bănescu, S., Wüchner, T., Salem, A., Guggenmos, M., Ochoa, M., & Pretschner, A. (2015) · International Conference on Malicious and Unwanted Software · doi:10.1109/malware.2015.7413683 · Cited by 24 · see citing works

Design and implementation of a malware detection system based on network behavior

Xue, L., & Sun, G. (2014) · Security and Communication Networks · doi:10.1002/sec.993 · Cited by 22 · see citing works

Polymorphic worm detection using token-pair signatures

Bayoğlu, B., & Soğukpınar, İ. (2008) · doi:10.1145/1387329.1387331 · Cited by 22 · see citing works

The Early Bird Gets the Botnet: A Markov Chain Based Early Warning System for Botnet Attacks

Abaid, Z., Sarkar, D., Kâafar, M. A., & Jha, S. K. (2016) · Conference on Local Computer Networks · doi:10.1109/lcn.2016.17 · Cited by 22 · see citing works

Data-Centric OS Kernel Malware Characterization

Rhee, J., Riley, R. D., Lin, Z., Jiang, X., & Xu, D. (2014) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2013.2291964 · Cited by 21 · see citing works

Survey on Ransomware: A New Era of Cyber Attack

Krunal, G., & Viral, P. (2017) · International Journal of Computer Applications · doi:10.5120/ijca2017914446 · Cited by 20 · see citing works

Adaptive and Scalable Android Malware Detection through Online LearningPreprint

Narayanan, A. S., Liu, Y., Chen, L., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.07150 · Cited by 17 · see citing works

Are your IT professionals prepared for the challenges to come?

Mustaca, S. (2014) · Computer Fraud & Security · doi:10.1016/s1361-3723(14)70472-5 · Cited by 15 · see citing works

Building a Cryptovirus Using Microsoft’s Cryptographic API

Young, A. L. (2005) · Lecture notes in computer science · doi:10.1007/11556992_28 · Cited by 14 · see citing works

History of malwarePreprint

Milošević, N. (2013) · arXiv (Cornell University) · Cited by 13 · see citing works

Design of Quantification Model for Prevent of Cryptolocker

Kim, D., Soh, W., & Kim, S. (2015) · Indian Journal of Science and Technology · doi:10.17485/ijst/2015/v8i19/80196 · Cited by 13 · see citing works

Improving Malware Detection Response Time with Behavior-Based Statistical Analysis Techniques

Prelipcean, D. B., Popescu, A. S., & Gavriluţ, D. T. (2015) · International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) · doi:10.1109/synasc.2015.44 · Cited by 13 · see citing works

Online malware defense using attack behavior model

Das, S., Xiao, H., Liu, Y., & Zhang, W. (2016) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2016.7527492 · Cited by 13 · see citing works

The New Codebreakers - Essays Dedicated to David Kahn on the Occasion of His 85th Birthday

Ryan, P. Y. A., Naccache, D., & Quisquater, J.-J. (2016) · International Linear Collider · Cited by 13 · see citing works

Ransomware

Bhardwaj, A. (2016) · Advances in information security, privacy, and ethics book series · doi:10.4018/978-1-5225-0864-9.ch012 · Cited by 12 · see citing works

An Anomaly Detection Model for Network Intrusions Using One-Class SVM and Scaling Strategy

Zhang, M., Xu, B., & Wang, D. (2016) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-28910-6_24 · Cited by 12 · see citing works

Extortion on the Internet: the Rise of Crypto-Ransomware

Richet, J. (2016) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 12 · see citing works

Static Analysis of Malicious Java Applets

Ganesh, N., Troia, F. D., Corrado, V. A., Austin, T. H., & Stamp, M. (2016) · ACM International Workshop on Security and Privacy Analytics (IWSPA) · doi:10.1145/2875475.2875477 · Cited by 11 · see citing works

Behavioural malware detection using efficient SVM implementation

Pluskal, O. (2015) · ACM Conference on Research in Adaptive and Convergent Systems (RACS) · doi:10.1145/2811411.2811516 · Cited by 10 · see citing works

Developing malware evaluation infrastructure

Cabaj, K., Gawkowski, P., Grochowski, K., & Kosik, A. (2016) · Annals of Computer Science and Information Systems · doi:10.15439/2016f490 · Cited by 9 · see citing works

Preventive Measures and Incident Response for Locky Ransomware

Prakash, K. P., Nafis, M. T., & Biswas, S. S. (2017) · International Journal of Advanced Research in Computer Science · doi:10.26483/ijarcs.v8i5.3311 · Cited by 9 · see citing works

Information security : 8th International Conference, ISC 2005, Singapore, September 20-23, 2005 : proceedings

Zhou, J., López, J., Deng, R. H., & Bao, F. (2005) · Cited by 6 · see citing works

Hybrid concentration based feature extraction approach for malware detection

Zhang, P., & Tan, Y. (2015) · IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) · doi:10.1109/ccece.2015.7129175 · Cited by 6 · see citing works

SSSM-semantic set and string matching based malware detection

Nhuong, N. V., Nhi, V. T. Y., Cam, N. T., Mai, P. X., & Tan, C. D. (2014) · IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA) · doi:10.1109/cisda.2014.7035642 · Cited by 6 · see citing works

Debugging using MD5 process firewall

Kumar, C. U. O., Kishore, S., & Geetha, A. (2014) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i.2014.7019657 · Cited by 6 · see citing works

Using ILP to Analyse Ransomware Attacks.

Ray, O., Hicks, S. I., & Moyle, S. (2016) · Cited by 6 · see citing works

Ransomware: A Rising Threat of new age Digital ExtortionPreprint

Bhardwaj, A., Subrahmanyam, G. V. B., Avasthi, V., & Hanumat Sastry, G. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1512.01980 · Cited by 6 · see citing works

Cryptography as an Attack Technology: Proving the RSA/Factoring Kleptographic Attack

Young, A. L., & Yung, M. M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-662-49301-4_16 · Cited by 6 · see citing works

Malware detection by token counting

Poonia, A. S., & Singh, S. R. (2014) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i.2014.7019691 · Cited by 5 · see citing works

Opcode position aware metamorphic malware detection: Signature vs histogram approach

George, N., & Vinod, P. (2015) · International Conference on Computing for Sustainable Global Development · Cited by 2 · see citing works

Cryptography as an Attack Technology: Proving the RSA/Factoring Kleptographic Attack.

Young, A. L., & Yung, M. M. (2016) · Cited by 2 · see citing works

TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems

Alsaedi, A., Moustafa, N., Tari, Z., Mahmood, A. N., & Anwar, A. (2020) · IEEE Access · doi:10.1109/access.2020.3022862 · Cited by 983 · see citing works

A Comprehensive Review of Cyber Security Vulnerabilities, Threats, Attacks, and Solutions

Aslan, Ö., Aktuğ, S. S., Ozkan-Okay, M., Yılmaz, A. A., & Akin, E. (2023) · Electronics · doi:10.3390/electronics12061333 · Cited by 746 · see citing works

A Survey of Cyber Attacks on Cyber Physical Systems: Recent Advances and Challenges

Duo, W., Zhou, M., & Abusorrah, A. M. (2022) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2022.105548 · Cited by 599 · see citing works

A survey on cybersecurity, data privacy, and policy issues in cyber-physical system deployments in smart cities

Habibzadeh, H., Nussbaum, B. H., Anjomshoa, F., Kantarcı, B., & Soyata, T. (2019) · Sustainable Cities and Society · doi:10.1016/j.scs.2019.101660 · Cited by 304 · see citing works

A Survey on Ransomware: Evolution, Taxonomy, and Defense Solutions

Oz, H., Aris, A., Levi, A., & Uluagac, A. S. (2022) · ACM Computing Surveys · doi:10.1145/3514229 · Cited by 297 · see citing works

Ransomware: Recent advances, analysis, challenges and future research directions

Beaman, C., Barkworth, A., Akande, T. D., Hakak, S., & Khan, M. K. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102490 · Cited by 269 · see citing works

Classification of ransomware families with machine learning based on N -gram of opcodes

Zhang, H., Xiao, X., Mercaldo, F., Ni, S., Martinelli, F., & Sangaiah, A. K. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.07.052 · Cited by 242 · see citing works

Internet of things and ransomware: Evolution, mitigation and prevention

Humayun, M., Jhanjhi, N. Z., Alsayat, A., & Ponnusamy, V. (2020) · Egyptian Informatics Journal · doi:10.1016/j.eij.2020.05.003 · Cited by 210 · see citing works

A Multi-Classifier Network-Based Crypto Ransomware Detection System: A Case Study of Locky Ransomware

Almashhadani, A. O., Kaiiali, M., Sezer, S., & O’Kane, P. (2019) · IEEE Access · doi:10.1109/access.2019.2907485 · Cited by 182 · see citing works

Ransomware Detection Using the Dynamic Analysis and Machine Learning: A Survey and Research Directions

Urooj, U., Al‐rimy, B. A. S., Zainal, A. B., Ghaleb, F. A. A., & Rassam, M. A. (2021) · Applied Sciences · doi:10.3390/app12010172 · Cited by 203 · see citing works

The rise of crypto-ransomware in a changing cybercrime landscape: Taxonomising countermeasures

Connolly, L. Y., & Wall, D. S. (2019) · Computers & Security · doi:10.1016/j.cose.2019.101568 · Cited by 143 · see citing works

Impact, Vulnerabilities, and Mitigation Strategies for Cyber-Secure Critical Infrastructure.

Riggs, H., Tufail, S., Parvez, I., Tariq, M., Khan, M. A., Amir, A., Vuda, K. V., & Sarwat, A. I. (2023) · Sensors · doi:10.3390/s23084060 · Cited by 124 · see citing works

A Survey on Detection Techniques for Cryptographic Ransomware

Berrueta, E., Morató, D., Magaña, E., & Izal, M. (2019) · IEEE Access · doi:10.1109/access.2019.2945839 · Cited by 115 · see citing works

A system call refinement-based enhanced Minimum Redundancy Maximum Relevance method for ransomware early detection

Ahmed, Y. A., Koçer, B., Huda, S., Al‐rimy, B. A. S., & Hassan, M. M. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102753 · Cited by 114 · see citing works

A Survey of Crypto Ransomware Attack Detection Methodologies: An Evolving Outlook

Alqahtani, A., & Sheldon, F. T. (2022) · Sensors · doi:10.3390/s22051837 · Cited by 111 · see citing works

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 · see citing works

Crypto-ransomware early detection model using novel incremental bagging with enhanced semi-random subspace selection

Al‐rimy, B. A. S., Maarof, M. A., & Shaid, S. Z. M. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.06.005 · Cited by 107 · see citing works

Evaluation metric for crypto-ransomware detection using machine learning

Kok, S. H., Azween, A., & Jhanjhi, N. Z. (2020) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2020.102646 · Cited by 106 · see citing works

An Ensemble Tree-Based Model for Intrusion Detection in Industrial Internet of Things Networks

Awotunde, J. B., Folorunso, S. O., Imoize, A. L., Odunuga, J. O., Lee, C., Li, C., & Do, D. (2023) · Applied Sciences · doi:10.3390/app13042479 · Cited by 91 · see citing works

An Adaptive Behavioral-Based Incremental Batch Learning Malware Variants Detection Model Using Concept Drift Detection and Sequential Deep Learning

Darem, A. A., Ghaleb, F. A. A., Alhashmi, A. A., Abawajy, J., Alanazi, S. M., & Al-Rezami, A. Y. (2021) · IEEE Access · doi:10.1109/access.2021.3093366 · Cited by 89 · see citing works

Ransomware early detection: A survey

Cen, M., Jiang, F., Qin, X., Jiang, Q., & Doss, R. (2023) · Computer Networks · doi:10.1016/j.comnet.2023.110138 · Cited by 89 · see citing works

Mitigating adversarial evasion attacks of ransomware using ensemble learning

Ahmed, U., Lin, J. C., & Srivastava, G. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.107903 · Cited by 86 · see citing works

An Improved Mutual Information Feature Selection Technique for Intrusion Detection Systems in the Internet of Medical Things

Alalhareth, M., & Hong, S. (2023) · Sensors · doi:10.3390/s23104971 · Cited by 85 · see citing works

Targeted Ransomware: A New Cyber Threat to Edge System of Brownfield Industrial Internet of Things

Al-Hawawreh, M. S., den Hartog, F., & Sitnikova, E. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2914390 · Cited by 81 · see citing works

Behavior-based ransomware classification: A particle swarm optimization wrapper-based approach for feature selection

Abbasi, M. S., Al-Sahaf, H., Mansoori, M., & Welch, I. S. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.108744 · Cited by 81 · see citing works

Ransomware on cyber-physical systems: Taxonomies, case studies, security gaps, and open challenges

Benmalek, M. (2024) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2023.12.001 · Cited by 99 · see citing works

Avoiding Future Digital Extortion Through Robust Protection Against Ransomware Threats Using Deep Learning Based Adaptive Approaches

Sharmeen, S., Ahmed, Y. A., Huda, S., Koçer, B., & Hassan, M. M. (2020) · IEEE Access · doi:10.1109/access.2020.2970466 · Cited by 77 · see citing works

A Survey on Windows-based Ransomware Taxonomy and Detection Mechanisms

Moussaileb, R., Cuppens-Boulahia, N., Lanet, J., & Bouder, H. L. (2021) · ACM Computing Surveys · doi:10.1145/3453153 · Cited by 77 · see citing works

Ransomware Detection Using Machine Learning: A Review, Research Limitations and Future Directions

Ispahany, J., Islam, R., Islam, M. Z., & Khan, M. A. (2024) · IEEE Access · doi:10.1109/access.2024.3397921 · Cited by 77 · see citing works

The rise of ransomware: Forensic analysis for windows based ransomware attacks

Kara, İ., & Aydos, M. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116198 · Cited by 76 · see citing works

A Risk-Assessment of Cyber Attacks and Defense Strategies in Industry 4.0 Ecosystem

Süzen, A. A. (2020) · International Journal of Computer Network and Information Security · doi:10.5815/ijcnis.2020.01.01 · Cited by 75 · see citing works

An empirical study of ransomware attacks on organizations: an assessment of severity and salient factors affecting vulnerability

Connolly, L. Y., Wall, D. S., Lang, M., & Oddson, B. E. (2020) · Journal of Cybersecurity · doi:10.1093/cybsec/tyaa023 · Cited by 75 · see citing works

Zero-day Ransomware Attack Detection using Deep Contractive Autoencoder and Voting based Ensemble Classifier

Zahoora, U., Rajarajan, M., Pan, Z., & Khan, A. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03244-6 · Cited by 74 · see citing works

Systematic literature review and metadata analysis of ransomware attacks and detection mechanisms

Maigida, A. M., Abdulhamid, S. M., Olalere, M., Alhassan, J. K., Chiroma, H., & Dada, E. G. (2019) · Journal of Reliable Intelligent Environments · doi:10.1007/s40860-019-00080-3 · Cited by 72 · see citing works

Deep Learning-Enabled Threat Intelligence Scheme in the Internet of Things Networks

Al-Hawawreh, M. S., Moustafa, N., Garg, S., & Hossain, M. S. (2020) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2020.3032415 · Cited by 72 · see citing works

A Pseudo Feedback-Based Annotated TF-IDF Technique for Dynamic Crypto-Ransomware Pre-Encryption Boundary Delineation and Features Extraction

Al‐rimy, B. A. S., Maarof, M. A., Alazab, M., Alsolami, F. J., Shaid, S. Z. M., Ghaleb, F. A. A., Al-Hadhrami, T., & Ali, A. (2020) · IEEE Access · doi:10.1109/access.2020.3012674 · Cited by 71 · see citing works

Ransomware detection using deep learning based unsupervised feature extraction and a cost sensitive Pareto Ensemble classifier

Zahoora, U., Khan, A., Rajarajan, M., Khan, S. H., Asam, M., & Jamal, T. (2022) · Scientific Reports · doi:10.1038/s41598-022-19443-7 · Cited by 71 · see citing works

Cryptographic ransomware encryption detection: Survey

Begovic, K., Al-Ali, A. K., & Malluhi, Q. M. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103349 · Cited by 71 · see citing works

Redundancy Coefficient Gradual Up-weighting-based Mutual Information Feature Selection technique for Crypto-ransomware early detection

Al‐rimy, B. A. S., Maarof, M. A., Alazab, M., Shaid, S. Z. M., Ghaleb, F. A. A., Almalawi, A., Ali, A., & Al-Hadhrami, T. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.10.002 · Cited by 70 · see citing works

RanSAP: An open dataset of ransomware storage access patterns for training machine learning models

Hirano, M., Hodota, R., & Kobayashi, R. (2021) · Forensic Science International Digital Investigation · doi:10.1016/j.fsidi.2021.301314 · Cited by 67 · see citing works

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 · see citing works

Dominant Feature Selection and Machine Learning-Based Hybrid Approach to Analyze Android Ransomware

Gera, T., Singh, J., Mehbodniya, A., Webber, J. L., Shabaz, M., & Thakur, D. S. (2021) · Security and Communication Networks · doi:10.1155/2021/7035233 · Cited by 62 · see citing works

Zero-Day Aware Decision Fusion-Based Model for Crypto-Ransomware Early Detection

Al‐rimy, B. A. S., Maarof, M. A., Prasetyo, Y. A., Shaid, S. Z. M., Ariffin, A. F. M., & CyberSecurity Malaysia, S. K. (2018) · International Journal of Integrated Engineering · doi:10.30880/ijie.2018.10.06.011 · Cited by 62 · see citing works

Detecting ransomware attacks using intelligent algorithms: recent development and next direction from deep learning and big data perspectives

Bello, I., Chiroma, H., Abdullahi, A. U., Gital, A. Y., Jauro, F., Khan, A., Okesola, J. O., & Abdulhamid, S. M. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02630-7 · Cited by 61 · see citing works

Automated dynamic approach for detecting ransomware using finite-state machine

Gowtham, R., & Menen, A. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113400 · Cited by 60 · see citing works

A multi-dimensional machine learning approach to predict advanced malware

Bahtıyar, Ş., Yaman, M. B., & Altıniğne, C. Y. (2019) · Computer Networks · doi:10.1016/j.comnet.2019.06.015 · Cited by 59 · see citing works

Security and Privacy Challenges in the Deployment of Cyber-Physical Systems in Smart City Applications: State-of-Art Work

Rani, S., Kataria, A., Chauhan, M., Rattan, P., Kumar, R., & Sivaraman, A. K. (2022) · Materials Today Proceedings · doi:10.1016/j.matpr.2022.03.123 · Cited by 57 · see citing works

AI-Based Ransomware Detection: A Comprehensive Review

Ferdous, J., Islam, R., Mahboubi, A., & Islam, M. Z. (2024) · IEEE Access · doi:10.1109/access.2024.3461965 · Cited by 57 · see citing works

Ransomware Anti-Analysis and Evasion Techniques: A Survey and Research Directions

Olaimat, M. N., Maarof, M. A., & Al‐rimy, B. A. S. (2021) · Cyber Resilience Conference · doi:10.1109/crc50527.2021.9392529 · Cited by 53 · see citing works

Securing the Industrial Internet of Things against ransomware attacks: A comprehensive analysis of the emerging threat landscape and detection mechanisms

Al-Hawawreh, M. S., Alazab, M., Ferrag, M. A., & Hossain, M. S. (2023) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2023.103809 · Cited by 53 · see citing works

The contemporary cybercrime ecosystem: A multi-disciplinary overview of the state of affairs and developments

Broadhead, S. (2018) · Computer Law and Security Review · doi:10.1016/j.clsr.2018.08.005 · Cited by 44 · see citing works

Differential area analysis for ransomware attack detection within mixed file datasets

Davies, S. R. H., Macfarlane, R., & Buchanan, W. J. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102377 · Cited by 51 · see citing works

Opportunities for Early Detection and Prediction of Ransomware Attacks against Industrial Control Systems

Gazzan, M., & Sheldon, F. T. (2023) · Future Internet · doi:10.3390/fi15040144 · Cited by 51 · see citing works

Scrutinizing Security in Industrial Control Systems: An Architectural Vulnerabilities and Communication Network Perspective

Aslam, M. M., Tufail, A., Apong, R. A. A. H. M., Silva, L. C. D., & Raza, M. T. (2024) · IEEE Access · doi:10.1109/access.2024.3394848 · Cited by 54 · see citing works

Modified Decision Tree Technique for Ransomware Detection at Runtime through API Calls

Ullah, F., Javaid, Q., Salam, A., Ahmad, M., Sarwar, N., Shah, D., & Abrar, M. Y. B. T. (2020) · Scientific Programming · doi:10.1155/2020/8845833 · Cited by 51 · see citing works

On Ransomware Family Attribution Using Pre-Attack Paranoia Activities

Molina, R. M. A., Torabi, S., Sarieddine, K., Bou‐Harb, E., Bouguila, N., & Assi, C. M. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3112056 · Cited by 48 · see citing works

A deep hybrid learning model for detection of cyber attacks in industrial IoT devices

Shahin, M., Chen, F. F., Hosseinzadeh, A., Bouzary, H., & Rashidifar, R. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-10329-6 · Cited by 48 · see citing works

Ransomware Prediction Using Supervised Learning Algorithms

Adamu, U., & Awan, I. U. (2019) · International Conference on Future Internet of Things and Cloud (FiCloud) · doi:10.1109/ficloud.2019.00016 · Cited by 47 · see citing works

Attacks on the Industrial Internet of Things – Development of a multi-layer Taxonomy

Berger, S., Bürger, O., & Röglinger, M. (2020) · Computers & Security · doi:10.1016/j.cose.2020.101790 · Cited by 47 · see citing works

A proposed Adaptive Pre-Encryption Crypto-Ransomware Early Detection Model

Urooj, U., Maarof, M. A., & Al‐rimy, B. A. S. (2021) · Cyber Resilience Conference · doi:10.1109/crc50527.2021.9392548 · Cited by 46 · see citing works

Improving operations through a lean AI paradigm: a view to an AI-aided lean manufacturing via versatile convolutional neural network

Shahin, M., Maghanaki, M., Hosseinzadeh, A., & Chen, F. F. (2024) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-024-13874-4 · Cited by 40 · see citing works

Peeler: Profiling Kernel-Level Events to Detect Ransomware

Ahmed, M. E., Kim, H., Camtepe, S. A., & Nepal‬, ‪. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88418-5_12 · Cited by 50 · see citing works

Reducing Ransomware Crime: Analysis of Victims’ Payment Decisions

Connolly, L. Y., & Borrion, H. (2022) · Computers & Security · doi:10.1016/j.cose.2022.102760 · Cited by 46 · see citing works

A Weighted Minimum Redundancy Maximum Relevance Technique for Ransomware Early Detection in Industrial IoT

Ahmed, Y. A., Huda, S., Al‐rimy, B. A. S., Alharbi, N. H., Saeed, F., Ghaleb, F. A. A., & Ali, I. M. (2022) · Sustainability · doi:10.3390/su14031231 · Cited by 45 · see citing works

On the Economic Impact of Crypto-ransomware Attacks: The State of the Art on Enterprise Systems

Zimba, A., & Chishimba, M. (2019) · European Journal for Security Research · doi:10.1007/s41125-019-00039-8 · Cited by 42 · see citing works

Ransomware as a Service using Smart Contracts and IPFS

Karapapas, C., Pittaras, I., Fotiou, N., & Polyzos, G. C. (2020) · IEEE International Conference on Blockchain and Cryptocurrency (ICBC) · doi:10.1109/icbc48266.2020.9169451 · Cited by 42 · see citing works

SINN-RD: Spline interpolation-envisioned neural network-based ransomware detection scheme

Singh, J., Sharma, K., Wazid, M., & Das, A. K. (2023) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2023.108601 · Cited by 42 · see citing works

A New Static-Based Framework for Ransomware Detection

Medhat, M., Sayed, S. G., & Abdelbaki, N. (2018) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc/picom/datacom/cyberscitec.2018.00124 · Cited by 41 · see citing works

I2CE3: A dedicated and separated attack chain for ransomware offenses as the most infamous cyber extortion

Keshavarzi, M., & Ghaffary, H. R. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100233 · Cited by 41 · see citing works

On the classification of Microsoft-Windows ransomware using hardware profile

Aurangzeb, S., Rais, R. N. B., Aleem, M., Islam, M. A., & Iqbal, M. A. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.361 · Cited by 41 · see citing works

Your files have been encrypted: a crime script analysis of ransomware attacks

Matthijsse, S. R., Goede, M. S. V. ‘. H.-D., & Leukfeldt, R. (2023) · Trends in Organized Crime · doi:10.1007/s12117-023-09496-z · Cited by 40 · see citing works

Automatically Traceback RDP‐Based Targeted Ransomware Attacks

Wang, Z., Liu, C., Qiu, J., Tian, Z., Cui, X., & Su, S. (2018) · Wireless Communications and Mobile Computing · doi:10.1155/2018/7943586 · Cited by 40 · see citing works

Leveraging Deep Learning Models for Ransomware Detection in the Industrial Internet of Things Environment

Al-Hawawreh, M. S., & Sitnikova, E. (2019) · Military Communications and Information Systems Conference · doi:10.1109/milcis.2019.8930732 · Cited by 40 · see citing works

The Evolving Menace of Ransomware: A Comparative Analysis of Pre-pandemic and Mid-pandemic Attacks

Lang, M., Connolly, L. Y., Taylor, P. J., & Corner, P. J. (2022) · Digital Threats Research and Practice · doi:10.1145/3558006 · Cited by 40 · see citing works

Review on the Security Threats of Internet of Things

Podder, P., Mondal, M. R. H., Bharati, S., & Paul, P. K. (2020) · International Journal of Computer Applications · doi:10.5120/ijca2020920548 · Cited by 39 · see citing works

Dynamic user-centric access control for detection of ransomware attacks

McIntosh, T. R., Kayes, A. S. M., Chen, Y. P., Ng, A., & Watters, P. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102461 · Cited by 38 · see citing works

Comprehensive Analysis of IoT Malware Evasion Techniques

Al-Marghilani, A. (2021) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.4296 · Cited by 36 · see citing works

Ransomware Threat and its Impact on SCADA

Butt, U., Abbod, M., Lors, A., Jahankhani, H., Jamal, A., & Kumar, A. (2019) · International Conference on Global Security, Safety, and Sustainability · doi:10.1109/icgs3.2019.8688327 · Cited by 36 · see citing works

Automated Analysis Approach for the Detection of High Survivable Ransomware

Ahmed, Y. A., Koçer, B., & Al‐rimy, B. A. S. (2020) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2020.05.021 · Cited by 36 · see citing works

Addressing Behavioral Drift in Ransomware Early Detection Through Weighted Generative Adversarial Networks

Urooj, U., Al‐rimy, B. A. S., Zainal, A. B., Saeed, F., Abdelmaboud, A., & Nagmeldin, W. (2023) · IEEE Access · doi:10.1109/access.2023.3348451 · Cited by 35 · see citing works

RansomCare: Data-centric detection and mitigation against smartphone crypto-ransomware

Faghihi, F., & Zulkernine, M. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108011 · Cited by 35 · see citing works

MFMCNS: a multi-feature and multi-classifier network-based system for ransomworm detection

Almashhadani, A. O., Carlin, D., Kaiiali, M., & Sezer, S. (2022) · Computers & Security · doi:10.1016/j.cose.2022.102860 · Cited by 33 · see citing works

Modeling and analyzing malware propagation Over wireless networks based on hypergraphs

Chen, J., Sun, S., Xia, C., Shi, D., & Chen, G. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2023.3273184 · Cited by 33 · see citing works

Industrial Internet of Things Based Ransomware Detection using Stacked Variational Neural Network

Al-Hawawreh, M. S., & Sitnikova, E. (2019) · International Conference on Big Data and Internet of Things (BDIOT) · doi:10.1145/3361758.3361763 · Cited by 33 · see citing works

A Multi-Tier Streaming Analytics Model of 0-Day Ransomware Detection Using Machine Learning

Zuhair, H., Selamat, A., & Krejcar, O. (2020) · Applied Sciences · doi:10.3390/app10093210 · Cited by 33 · see citing works

SwiftR: Cross-platform ransomware fingerprinting using hierarchical neural networks on hybrid features

Karbab, E. B., Debbabi, M., & Derhab, A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120017 · Cited by 33 · see citing works

Ransomware Impact to SCADA Systems and its Scope to Critical Infrastructure

Ibarra, J., Butt, U., Do, A., Jahankhani, H., & Jamal, A. (2019) · International Conference on Global Security, Safety, and Sustainability · doi:10.1109/icgs3.2019.8688299 · Cited by 32 · see citing works

An ontology-driven framework for knowledge representation of digital extortion attacks

Keshavarzi, M., & Ghaffary, H. R. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107520 · Cited by 32 · see citing works

Machine learning-based ransomware classification of Bitcoin transactions

Dib, O., Nan, Z., & Liu, J. (2024) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2024.101925 · Cited by 32 · see citing works

Dynamic Malware Analysis Based on API Sequence Semantic Fusion

Zhang, S., Wu, J., Zhang, M., & Wang, Y. (2023) · Applied Sciences · doi:10.3390/app13116526 · Cited by 31 · see citing works

Crypto-Ransomware: A Revision of the State of the Art, Advances and Challenges

Gómez-Hernández, J. A., Teodoro, P. G., Carrión, R. M., & Rodríguez-Gómez, R. A. (2023) · Electronics · doi:10.3390/electronics12214494 · Cited by 31 · see citing works

A comprehensive literature review on ransomware detection using deep learning

Kritika, E. (2024) · Cyber Security and Applications · doi:10.1016/j.csa.2024.100078 · Cited by 31 · see citing works

Data Flooding against Ransomware: Concepts and Implementations

Berardi, D., Giallorenzo, S., Melis, A., Melloni, S., Onori, L., & Prandini, M. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103295 · Cited by 31 · see citing works

Evaluation of Live Forensic Techniques in Ransomware Attack Mitigation

Davies, S. R. H. (2020) · Edinburgh Napier Research Repository (Edinburgh Napier University) · Cited by 31 · see citing works

Preventive portfolio against data-selling ransomware—A game theory of encryption and deception

Li, Z., & Liao, Q. (2022) · Computers & Security · doi:10.1016/j.cose.2022.102644 · Cited by 30 · see citing works

The Rise of Ransomware: A Review of Attacks, Detection Techniques, and Future Challenges

Kamil, S., Norul, H. S. A. S., Firdaus, A., & Usman, O. L. (2022) · International Conference on Business Analytics for Technology and Security (ICBATS) · doi:10.1109/icbats54253.2022.9759000 · Cited by 30 · see citing works

Real-time system call-based ransomware detection

Chew, C. J. W., Kumar, V., Patros, P., & Malik, R. (2024) · International Journal of Information Security · doi:10.1007/s10207-024-00819-x · Cited by 29 · see citing works

Ransomware Prevention and Mitigation Techniques

Alshaikh, H., Ramadan, N., & Ahmed, H. (2020) · International Journal of Computer Applications · doi:10.5120/ijca2020919899 · Cited by 29 · see citing works

A proposed Crypto-Ransomware Early Detection(CRED) Model using an Integrated Deep Learning and Vector Space Model Approach

Alqahtani, A., Gazzan, M., & Sheldon, F. T. (2020) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc47524.2020.9031182 · Cited by 29 · see citing works

Ransomware early detection: A survey

Cen, M., Jiang, F., Qin, X., Jiang, Q., & Doss, R. (2023) · Computer Networks · doi:10.1016/j.comnet.2023.110138 · Cited by 89 · see citing works

Systematic literature review and metadata analysis of ransomware attacks and detection mechanisms

Maigida, A. M., Abdulhamid, S. M., Olalere, M., Alhassan, J. K., Chiroma, H., & Dada, E. G. (2019) · Journal of Reliable Intelligent Environments · doi:10.1007/s40860-019-00080-3 · Cited by 72 · see citing works

A Survey on Ransomware Detection Techniques

Bijitha, C. V., Sukumaran, R., & Nath, H. V. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-3817-9_4 · Cited by 25 · see citing works

Categorization, Detection, and Prevention of Ransomware Attack: A Review

Pandey, P., Jain, P., Gupta, A., Gupta, V. K., Jindal, H., & Gupta, A. (2023) · International Conference on Image Information Processing (ICIIP) · doi:10.1109/iciip61524.2023.10537764 · Cited by 5 · see citing works

Ransomware Anti-Analysis and Evasion Techniques: A Survey and Research Directions

Olaimat, M. N., Maarof, M. A., & Al‐rimy, B. A. S. (2021) · Cyber Resilience Conference · doi:10.1109/crc50527.2021.9392529 · Cited by 53 · see citing works