ZAGROŻENIE ATAKIEM ZŁOŚLIWYM OPROGRAMOWANIEM RANSOMWARE W EUROPE.
- Published in:
- Journal of Modern Science, 2023, v. 52, n. 3, p. 379, doi. 10.13166/jms/173091
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Works matching Ransomware
Results: 967
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A SOCIEDADE DA INFORMAÇÃO E SEUS DESAFIOS: A NECESSIDADE DE EFETIVAÇÃO DE UMA POLÍTICA PÚBLICA DE COMBATE AO RANSOMWARE NO BRASIL.
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- Revista da Faculdade de Direito da Uerj, 2019, n. 36, p. 23
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SEGURANÇA DA INFORMAÇÃO COM FOCO NA PROPAGAÇÃO IMINENTE DE RANSOMWARE NAS CORPORAÇÕES.
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- Revista Foco (Interdisciplinary Studies Journal), 2023, v. 16, n. 5, p. 1, doi. 10.54751/revistafoco.v16n5-024
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RANSOMWARE E CIBERSEGURANÇA: A INFORMAÇÃO AMEAÇADA POR ATAQUES A DADOS.
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- Revista Thesis Juris, 2020, v. 9, n. 1, p. 208, doi. 10.5585/rtj.v9i1.16739
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5
Ransomware Detection System Based on Machine Learning.
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- Journal of Education & Science, 2021, v. 30, n. 5, p. 86, doi. 10.33899/edusj.2021.130760.1173
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6
Zsarolóvírusok és a No More Ransom projekt.
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- Academic Journal of Internal Affairs / Belügyi Szemle, 2022, v. 70, n. 9, p. 1887, doi. 10.38146/BSZ.2022.9.9
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7
How to Circumvent and Beat the Ransomware in Android Operating System—A Case Study of Locker.CB!tr.
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- Electronics (2079-9292), 2024, v. 13, n. 11, p. 2212, doi. 10.3390/electronics13112212
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Ransomware through the lens of state crime: Conceptualizing ransomware groups as cyber proxies, pirates, and privateers.
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- State Crime Journal, 2023, v. 12, n. 1, p. 1, doi. 10.13169/statecrime.12.1.0004
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9
Ransomware Detection Based on PE Header Using Convolutional Neural Networks.
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- ISeCure, 2022, v. 14, n. 2, p. 181, doi. 10.22042/ISECURE.2021.262846.595
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10
An Enhanced Minimax Loss Function Technique in Generative Adversarial Network for Ransomware Behavior Prediction.
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- Future Internet, 2023, v. 15, n. 10, p. 318, doi. 10.3390/fi15100318
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Opportunities for Early Detection and Prediction of Ransomware Attacks against Industrial Control Systems.
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- Future Internet, 2023, v. 15, n. 4, p. 144, doi. 10.3390/fi15040144
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Ransomware Detection, Avoidance, and Mitigation Scheme: A Review and Future Directions.
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- Sustainability (2071-1050), 2022, v. 14, n. 1, p. 8, doi. 10.3390/su14010008
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13
Trends in Mobile Ransomware and Incident Response from a Digital Forensics Perspective.
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- Journal of Information & Communication Convergence Engineering, 2022, v. 20, n. 4, p. 280, doi. 10.56977/jicce.2022.20.4.280
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14
Adaptive Ransomware Detection Using Similarity-Preserving Hashing.
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- Applied Sciences (2076-3417), 2024, v. 14, n. 20, p. 9548, doi. 10.3390/app14209548
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Ransomware Detection Using the Dynamic Analysis and Machine Learning: A Survey and Research Directions.
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- Applied Sciences (2076-3417), 2022, v. 12, n. 1, p. 172, doi. 10.3390/app12010172
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KRDroid: Ransomware-Oriented Detector for Mobile Devices Based on Behaviors.
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- Applied Sciences (2076-3417), 2021, v. 11, n. 14, p. 6557, doi. 10.3390/app11146557
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Introduction of the ARDS—Anti-Ransomware Defense System Model—Based on the Systematic Review of Worldwide Ransomware Attacks.
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- Applied Sciences (2076-3417), 2021, v. 11, n. 13, p. 6070, doi. 10.3390/app11136070
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A Multi-Tier Streaming Analytics Model of 0-Day Ransomware Detection Using Machine Learning.
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- Applied Sciences (2076-3417), 2020, v. 10, n. 9, p. 3210, doi. 10.3390/app10093210
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19
基于熵图像静态分析技术的勒索软件分类研究.
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- Journal of Guangxi Normal University - Natural Science Edition, 2023, v. 41, n. 3, p. 91, doi. 10.16088/j.issn.1001-6600.2022100805
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Understanding the Influence of Ransomware: An Investigation on its Development, Mitigation and Avoidance Techniques.
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- Grenze International Journal of Engineering & Technology (GIJET), 2018, v. 4, n. 3, p. 123
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21
Ransomware Detection Service: Execution and Analysis Using Machine Learning Techniques.
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- Wireless Personal Communications, 2023, v. 133, n. 2, p. 995, doi. 10.1007/s11277-023-10801-w
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22
The Legal Corner (TLC): Ransomware Attacks Against Health Care IT.
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- Journal of Informatics Nursing, 2017, v. 2, n. 4, p. 30
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Ransomware protection in loT using software defined networking.
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- International Journal of Electrical & Computer Engineering (2088-8708), 2020, v. 10, n. 3, p. 3166, doi. 10.11591/ijece.v10i3.pp3166-3175
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e MIFS: A Normalized Hyperbolic Ransomware Deterrence Model Yielding Greater Accuracy and Overall Performance.
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- Sensors (14248220), 2024, v. 24, n. 6, p. 1728, doi. 10.3390/s24061728
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25
Crypto-Ransomware Detection Through a Honeyfile-Based Approach with R-Locker.
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- Mathematics (2227-7390), 2025, v. 13, n. 12, p. 1933, doi. 10.3390/math13121933
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CINN-UTLC: A Computationally Intelligent Neural Network-Based Unsupervised Transfer Learning Algorithm for Ransomware Detection.
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- Traitement du Signal, 2025, v. 42, n. 3, p. 1601, doi. 10.18280/ts.420331
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27
Ransomware Modeling Based on a Process Mining Approach.
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- International Journal of Information & Communication Technology Research (2251-6107), 2022, v. 14, n. 3, p. 27
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28
Detecting Ransomware Using a Hybrid Detection Scheme.
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- Journal of Cybersecurity (2579-0072), 2025, v. 7, n. 1, p. 71, doi. 10.32604/jcs.2025.063711
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29
Retaliation against Ransomware in Cloud-Enabled PureOS System.
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- Mathematics (2227-7390), 2023, v. 11, n. 1, p. 249, doi. 10.3390/math11010249
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30
Convolutional Neural Network-Based Cryptography Ransomware Detection for Low-End Embedded Processors.
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- Mathematics (2227-7390), 2021, v. 9, n. 7, p. 705, doi. 10.3390/math9070705
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31
RansomFormer: A Cross-Modal Transformer Architecture for Ransomware Detection via the Fusion of Byte and API Features.
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- Electronics (2079-9292), 2025, v. 14, n. 7, p. 1245, doi. 10.3390/electronics14071245
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32
Clop Ransomware in Action: A Comprehensive Analysis of Its Multi-Stage Tactics.
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- Electronics (2079-9292), 2024, v. 13, n. 18, p. 3689, doi. 10.3390/electronics13183689
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33
A Study on Countermeasures against Neutralizing Technology: Encoding Algorithm-Based Ransomware Detection Methods Using Machine Learning †.
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- Electronics (2079-9292), 2024, v. 13, n. 6, p. 1030, doi. 10.3390/electronics13061030
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34
Crypto-Ransomware: A Revision of the State of the Art, Advances and Challenges.
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- Electronics (2079-9292), 2023, v. 12, n. 21, p. 4494, doi. 10.3390/electronics12214494
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35
Enhancing Ransomware Attack Detection Using Transfer Learning and Deep Learning Ensemble Models on Cloud-Encrypted Data.
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- Electronics (2079-9292), 2023, v. 12, n. 18, p. 3899, doi. 10.3390/electronics12183899
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36
A New Scheme for Ransomware Classification and Clustering Using Static Features.
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- Electronics (2079-9292), 2022, v. 11, n. 20, p. 3307, doi. 10.3390/electronics11203307
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37
High Performance Classification Model to Identify Ransomware Payments for Heterogeneous Bitcoin Networks.
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- Electronics (2079-9292), 2021, v. 10, n. 17, p. 2113, doi. 10.3390/electronics10172113
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38
Magniber v2 Ransomware Decryption: Exploiting the Vulnerability of a Self-Developed Pseudo Random Number Generator.
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- Electronics (2079-9292), 2021, v. 10, n. 1, p. 16, doi. 10.3390/electronics10010016
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39
Analyzing Reporting on Ransomware Incidents: A Case Study.
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- Social Sciences (2076-0760), 2023, v. 12, n. 5, p. 265, doi. 10.3390/socsci12050265
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40
Ransomware, A Tool and Opportunity for Terrorist Financing and Cyberwarfare.
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- Defence Against Terrorism Review, 2019, v. 12, p. 45
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41
An intelligent ransomware based cyberthreat detection model using multi head attention-based recurrent neural networks with optimization algorithm in IoT environment.
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- Scientific Reports, 2025, v. 15, n. 1, p. 1, doi. 10.1038/s41598-025-92711-4
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42
Machine Learning as a Tool to Combat Ransomware in Resource-Constrained Business Environment.
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- International Journal of Advanced Computer Science & Applications, 2024, v. 15, n. 12, p. 509, doi. 10.14569/ijacsa.2024.0151253
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43
Secret Sharing as a Defense Mechanism for Ransomware in Cloud Storage Systems.
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- International Journal of Advanced Computer Science & Applications, 2024, v. 15, n. 10, p. 1008, doi. 10.14569/ijacsa.2024.01510102
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44
Pre-Encryption Ransomware Detection (PERD) Taxonomy, and Research Directions: Systematic Literature Review.
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- International Journal of Advanced Computer Science & Applications, 2024, v. 15, n. 9, p. 165, doi. 10.14569/ijacsa.2024.0150917
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45
Analysis of Ransomware Impact on Android Systems using Machine Learning Techniques.
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- International Journal of Advanced Computer Science & Applications, 2023, v. 14, n. 11, p. 775, doi. 10.14569/ijacsa.2023.0141178
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46
A THREAT FOR THE TRAINS: RANSOMWARE AS A NEW RISK.
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- Interdisciplinary Description of Complex Systems, 2019, v. 17, n. 1-A, p. 1, doi. 10.7906/indecs.17.1.1
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47
Ransomware and the Robin Hood effect?: Experimental evidence on Americans' willingness to support cyber-extortion.
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- Journal of Experimental Criminology, 2023, v. 19, n. 4, p. 943, doi. 10.1007/s11292-022-09515-z
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48
The Transnational Cybercrime Extortion Landscape and the Pandemic: Changes in ransomware offender tactics, attack scalability and the organisation of offending.
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- European Law Enforcement Research Bulletin, 2021, p. 1
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49
ANALYSIS OF ENCRYPTION SCHEMES IN MODERN RANSOMWARE .
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- Rad HAZU: Matematicke Znanosti, 2021, v. 25, p. 1, doi. 10.21857/mnlqgc58gy
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50
Enhancing Cybersecurity against Ransomware Attacks Using LSTM Deep Learning: A Case Study on Android Devices.
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- Journal of Polytechnic, 2025, v. 28, n. 2, p. 491, doi. 10.2339/politeknik.1508722
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