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Journal articles on the topic 'Ransomware attack'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Urooj, Umara, Bander Ali Saleh Al-rimy, Mazen Gazzan, et al. "A Wide and Weighted Deep Ensemble Model for Behavioral Drifting Ransomware Attacks." Mathematics 13, no. 7 (2025): 1037. https://doi.org/10.3390/math13071037.

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Ransomware is a type of malware that leverages encryption to execute its attacks. Its continuous evolution underscores its dynamic and ever-changing nature. The evolving variants use varying timelines to launch attacks and associate them with varying attack patterns. Detecting early evolving variants also leads to incomplete attack patterns. To develop an early detection model for behavioral drifting ransomware attacks, a detection model should be able to detect evolving ransomware variants. To consider the behavioral drifting problem of ransomware attacks, a model should be able to generalize

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Zafri, Fizza. "Ransomware Attacks in History of Cyber World." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (2022): 39–43. http://dx.doi.org/10.22214/ijraset.2022.39758.

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Abstract: Technology advancement since last few decades creates cyber attack a critical issue. Cyber security has become an important part today. It has also become an important and crucial subject in the field of forensic science. Increased in the growth of internet technology and internet devices have increased the risk of cyber attack. Almost every organization today are depends on the internet and devices. There are many types of cyber attack. This paper is the detailed review about Ransomware attack. This paper is consisted about vast of the information about What is Ransomware Attack, ho

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Sen, Saurabh Kumar, and Anuradha Deolase. "Analyse Cyberattack at Organizations using Logistic Regression Algorithm." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (2022): 518–25. http://dx.doi.org/10.22214/ijraset.2022.40663.

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Abstract: Ransomware cyberattacks have grown in severity, effectiveness to cause damage, and ease of execution during the last decade. Advanced ransomware detection technologies must be included with traditional anti-malware procedures. The results of a study and analysis of ransomware attack risk are presented in this work, with the goal of identifying the characteristics that separate ransomware from other malware and benign executable files with the help of detected logs. The ransomware's normal behaviour and structure are determined by statically and dynamically analysing the executable bi

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Wani, Azka, and S. Revathi. "Ransomware protection in IoT using software defined networking." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (2020): 3166. http://dx.doi.org/10.11591/ijece.v10i3.pp3166-3175.

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Internet of things (IoT) is the network of physical objects connected to provide various services. IoT is expanding rapidly, and is positively influencing many areas. The impact of IoT is evident in medical field, manufacturing units and livestock. The IoT is also vulnerable to many cyber threats, owing to its limited resources and battery operation. In contemporary times the security threats like DDoS, botnet malware, man in the middle, flood attacks and ransomware are affecting the smooth functioning of IoT. Ransomware has emerged as one of the biggest threat in cyber world. Ransomware is a

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Azka, Wani, and Revathi S. "Ransomware protection in loT using software defined networking." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (2020): 3166–75. https://doi.org/10.11591/ijece.v10i3.pp3166-3175.

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Internet of things (IoT) is the network of physical objects connected to provide various services. IoT is expanding rapidly, and is positively influencing many areas. The impact of IoT is evident in medical field, manufacturing units and livestock. The IoT is also vulnerable to many cyber threats, owing to its limited resources and battery operation. In contemporary times the security threats like DDoS, botnet malware, man in the middle, flood attacks and ransomware are affecting the smooth functioning of IoT. Ransomware has emerged as one of the biggest threat in cyber world. Ransomware is a

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Vyom, Vyom, Deepak Motwani, and Pankaj Sharma. "Multiple Feature-Based Recurrent Neural Network for Highly Accurate Ransomware Detection in Android Devices." Fusion: Practice and Applications 19, no. 1 (2025): 75–83. https://doi.org/10.54216/fpa.190107.

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Ransomware or crypto-ransomware is a big headache to digital media and transactions nowadays. Generally, Ransomware affects the operating system and transfers the valuable information and data stored in the system. Some ransomware attacks the system and corrupts the system file, making it useless to the user. Data encryption with a private key is also one of the attaching fashions of some types of ransomwares. Most ransomware attacks are reported in android operating system-based devices. The solution to ransomware is only the earlier identification of an attacked pattern in the operating syst

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Jaya, M. Izham, and Mohd Faizal Ab Razak. "Dynamic Ransomware Detection for Windows Platform Using Machine Learning Classifiers." JOIV : International Journal on Informatics Visualization 6, no. 2-2 (2022): 469. http://dx.doi.org/10.30630/joiv.6.2-2.1093.

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In this world of growing technological advancements, ransomware attacks are also on the rise. This threat often affects the finance of individuals, organizations, and financial sectors. In order to effectively detect and block these ransomware threats, the dynamic analysis strategy was proposed and carried out as the approach of this research. This paper aims to detect ransomware attacks with dynamic analysis and classify the attacks using various machine learning classifiers namely: Random Forest, Naïve Bayes, J48, Decision Table and Hoeffding Tree. The TON IoT Datasets from the University of

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Ifthikhar, Nimra, Ahthasham Sajid, Adeel Zafar, Atta Ur Rahman, Rida Malik, and Hamza Razzaq. "A Comprehensive Study on Phishing Attack Detection and Mitigation via Ransomware-as-a-Service (RAAS)." Nucleus 61, no. 2 (2025): 93–100. https://doi.org/10.71330/nucleus.61.02.1402.

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Ransomware-as-a-Service (RAAS), a new cybercriminal actor, is making ransomware attacks more potent and widespread. This research comprehensively assesses Ransomware-as-a-Service (RAAS) ecosystem phishing detection and prevention solutions. Seven studies compare RAAS-enabled phishing detection and prevention effectiveness, challenges, and trends. The findings recommend a multi-layered, context-aware approach for organizational resilience to shifting cyber threats. This thorough phishing attack detection and security study examines ransomware-as-a-service. Phishing attacks leverage human weakne

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Ifthikhar, Nimra, Ahthasham Sajid, Adeel Zafar, Atta Ur Rahman, Rida Malik, and Hamza Razzaq. "A Comprehensive Study on Phishing Attack Detection and Mitigation via Ransomware-as-a-Service (RAAS)." Nucleus 61, no. 2 (2025): 93–100. https://doi.org/10.71330/thenucleus.2024.1402.

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Ransomware-as-a-Service (RAAS), a new cybercriminal actor, is making ransomware attacks more potent and widespread. This research comprehensively assesses Ransomware-as-a-Service (RAAS) ecosystem phishing detection and prevention solutions. Seven studies compare RAAS-enabled phishing detection and prevention effectiveness, challenges, and trends. The findings recommend a multi-layered, context-aware approach for organizational resilience to shifting cyber threats. This thorough phishing attack detection and security study examines ransomware-as-a-service. Phishing attacks leverage human weakne

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Arivunambi, Amirthasaravanan, and Arjun Paramarthalingam. "A Study on Two-Phase Monitoring Server for Ransomware Evaluation and Detection in IoT Environment." Journal of Trends in Computer Science and Smart Technology 4, no. 2 (2022): 72–82. http://dx.doi.org/10.36548/jtcsst.2022.2.003.

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Current trending- Internet of things (IoT) is internetworking of an assortment of hardware devices to offer a collection of applications and services. In the present-day world, ransomware cyber-attack has become one of the major attacks in IoT systems. Ransomware is a hazardous malware that targets the user’s computer inaccessible or inoperative, and then requesting the computer victim user to transfer a huge ransom to relapse the damage. At instance, the evolution rate outcomes illustrate that the level of attacks such as Locky and Cryptowall ransomware are conspicuously growing then other ra

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Victoire, Dr T. Amalraj. "Ransomware Prevention System using Python and IoT." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–7. https://doi.org/10.55041/isjem03973.

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Abstract: Ransomware attacks are a critical threat to data integrity by file encryption and ransom payment for decryption. Conventional cybersecurity mechanisms are ineffective against zero-day ransomware attacks, and hence a forward-looking mitigation solution is required. This project proposes a Ransomware Mitigation System that scans file system activity in real-time through Python to identify ransomware attack patterns through file encryption activity. During a ransomware attack, the encryption process changes file extensions, which Python identifies and notifies a .NET Windows Forms appli

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Moussaileb, Routa, Nora Cuppens, Jean-Louis Lanet, and Hélène Le Bouder. "A Survey on Windows-based Ransomware Taxonomy and Detection Mechanisms." ACM Computing Surveys 54, no. 6 (2021): 1–36. http://dx.doi.org/10.1145/3453153.

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Ransomware remains an alarming threat in the 21st century. It has evolved from being a simple scare tactic into a complex malware capable of evasion. Formerly, end-users were targeted via mass infection campaigns. Nevertheless, in recent years, the attackers have focused on targeted attacks, since the latter are profitable and can induce severe damage. A vast number of detection mechanisms have been proposed in the literature. We provide a systematic review of ransomware countermeasures starting from its deployment on the victim machine until the ransom payment via cryptocurrency. We define fo

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Prasad Purnaye, Et al. "Hypervisor-Level Ransomware Detection in Cloud Using Machine Learning." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2024): 3186–90. http://dx.doi.org/10.17762/ijritcc.v11i9.9508.

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Ransomware attack incidences have been on the rise for a few years. The attacks have evolved over the years. The severity of these attacks has only increased in the cloud era. This article discusses the evolution of ransomware attacks targeting cloud storage and explores existing ransomware detection solutions. It also presents a methodology for generating a dataset for detecting ransomware in the cloud and discusses the results, including feature selection and normalization. The article proposes a system for detecting attacks in virtualized environments using machine learning models and evalu

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Syam Muhaimin, Haslindah, and Hasni. "Analisis Serangan Ransomware Pada Sistem Keamanan Siber Bank Syariah Indoneisa (BSI) Terhadap Customer Turst." Journal Transformation of Mandalika, e-ISSN: 2745-5882, p-ISSN: 2962-2956 6, no. 2 (2025): 67–72. https://doi.org/10.36312/jtm.v6i2.4467.

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The development of digital technology has brought significant benefits to the banking industry but has also increased cybersecurity risks, including the threat of ransomware attacks. Bank Syariah Indonesia (BSI) experienced a ransomware attack that affected its operational services and customer trust. This study aims to analyze the impact of the ransomware attack on customer trust at BSI Branch Bone. The research employs a descriptive qualitative approach, with data collected through interviews with customers. The findings indicate that the ransomware attack caused concern among customers, pri

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Neprash, Hannah T., Claire C. McGlave, Dori A. Cross, et al. "Trends in Ransomware Attacks on US Hospitals, Clinics, and Other Health Care Delivery Organizations, 2016-2021." JAMA Health Forum 3, no. 12 (2022): e224873. http://dx.doi.org/10.1001/jamahealthforum.2022.4873.

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ImportanceAnecdotal evidence suggests that health care delivery organizations face a growing threat from ransomware attacks that are designed to disrupt care delivery and may consequently threaten patient outcomes.ObjectiveTo quantify the frequency and characteristics of ransomware attacks on health care delivery organizations.Design, Setting, and ParticipantsThis cohort study used data from the Tracking Healthcare Ransomware Events and Traits database to examine the number and characteristics of ransomware attacks on health care delivery organizations from 2016 to 2021. Logistic and negative

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Temara, Sheetal. "The Ransomware Epidemic: Recent Cybersecurity Incidents Demystified." Asian Journal of Advanced Research and Reports 18, no. 3 (2024): 1–16. http://dx.doi.org/10.9734/ajarr/2024/v18i3610.

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The pervasive threat of ransomware poses a significant risk to businesses across various scales as cybercriminals continue to exploit vulnerabilities causing severe disruptions and demanding substantial ransom payments. This review conducts a comprehensive literature review delving into recent ransomware attacks to analyze key aspects, including the targeted organizations, attack vectors, threat actors, propagation mechanisms, and the resulting business impact. The study goes beyond a surface examination by exploring the evolving nature of ransomware attacks, encompassing different types, atta

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Alqahtani, Abdullah, and Frederick T. Sheldon. "A Survey of Crypto Ransomware Attack Detection Methodologies: An Evolving Outlook." Sensors 22, no. 5 (2022): 1837. http://dx.doi.org/10.3390/s22051837.

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Recently, ransomware attacks have been among the major threats that target a wide range of Internet and mobile users throughout the world, especially critical cyber physical systems. Due to its unique characteristics, ransomware has attracted the attention of security professionals and researchers toward achieving safer and higher assurance systems that can effectively detect and prevent such attacks. The state-of-the-art crypto ransomware early detection models rely on specific data acquired during the runtime of an attack’s lifecycle. However, the evasive mechanisms that these attacks employ

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Seri Mughni Sulubara. "Perlindungan Data Pribadi dalam Kasus Ransomware: Apa Kata Hukum?" Eksekusi : Jurnal Ilmu Hukum dan Administrasi Negara 2, no. 4 (2024): 426–34. https://doi.org/10.55606/eksekusi.v2i4.1823.

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Ransomware attacks are an increasingly complex and costly global threat. International reports show a 300% increase in the frequency of ransomware attacks in the last five years (for example, data from IBM Security or Kaspersky). This threat not only blocks data access through encryption, but also jeopardizes privacy. This research will also examine the criminal law aspects associated with ransomware attacks, including the possible prosecution of the perpetrators of the attacks and the legal liability for victimized organizations. The theoretical study in the research titled "Personal Data Pro

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Reddy, Desai Rohith, K. Yashwanth Kumar Reddy, and T Niranjan Babu. "Ethical Ransomware Simulation: A Safe Framework for Cybersecurity Training." International Research Journal of Innovations in Engineering and Technology 09, Special Issue (2025): 201–9. https://doi.org/10.47001/irjiet/2025.inspire33.

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Ransomware is a major cybersecurity threat that causes huge financial and data losses. Most existing solutions focus on stopping ransomware after an attack, but there are not many safe ways to simulate and study ransomware in a controlled environment. This paper introduces an Ethical Ransomware Simulation Framework, a tool that allows cybersecurity students, researchers, and professionals to safely test and learn how ransomware works. The system includes custom ransomware creation, real-time monitoring, and testing of security defenses like firewalls and backups. It provides a risk-free, hands

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Blessing, Jenny, Jules Drean, and Sarah Radway. "Survey and analysis of U.S. policies to address ransomware." MIT Science Policy Review 3 (August 29, 2022): 38–46. http://dx.doi.org/10.38105/spr.iyuyqypkzm.

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Ransomware poses a critical threat to the U.S. economy and critical infrastructure. The frequency of attacks has increased dramatically in recent years, enabled by the growing prominence of cryptocurrencies, which provide an effective means of ransom payment. In this article, we review existing policies, players, and technologies involved in the ransomware ecosystem, discussing the potential efficacy of these policies and technologies in discouraging ransomware attacks. First, we survey the economic and technical forces driving ransomware attacks, outlining measures institutions can take to pr

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Tiu, Yan Lin, and Mohamad Fadli Zolkipli. "Study on Prevention and Solution of Ransomware Attack." Journal of IT in Asia 9, no. 1 (2021): 133–39. http://dx.doi.org/10.33736/jita.3402.2021.

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The development of science and technology in this era brought many advantages for peoples, organizations, enterprises, and companies merely a lot of cyber threats are occurring nowadays. Ransomware is one of the families of malicious software that spread quickly and cause a critical impact around the world. Ransomware attacks the victim by infecting the malicious file into the device; they will encrypt and deny the victim to access it. A ransom demand message will prompt the user so that they will gain the money anonymously. The victims are only allowed to access after pay the demand using cry

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B. M. Bandgar. "Analysis of Ransomware Attack Detection Using Machine Learning Algorithms." Communications on Applied Nonlinear Analysis 32, no. 8s (2025): 869–81. https://doi.org/10.52783/cana.v32.3828.

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Ransomware is one of the most prevalent and damaging forms of cyberattacks, causing substantial losses to organizations worldwide. The increasing sophistication of ransomware demands advanced detection techniques to identify and mitigate potential threats. This research explores machine learning models, including Random Forest, Gradient Boosting Machines (GBM), and Logistic Regression, to enhance ransomware attack detection. The study analyzes their performance using metrics such as precision, recall, and cross-validation accuracy. This paper aims to demonstrate the efficacy of machine learnin

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Lestari, Julia Ananda, and Ghofar Taufik. "Penerapan NIST 800-61 REV 2 Untuk Analisa Ransomware Attack Pada PT. Sembilan Pilar Semesta Dengan Berbasis SIEM." Jurnal Infortech 6, no. 1 (2024): 15–29. http://dx.doi.org/10.31294/infortech.v6i1.21767.

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Abstrak - Di era digital yang semakin maju, keamanan informasi menjadi semakin penting dan harus menjadi prioritas utama bagi organisasi dan perusahaan. Maka dari itu pengetahuan yang baik tentang risiko keamanan informasi yang diperlukan untuk melindungi data penting dan informasi rahasia dari ancaman cyber. Ransomware menjadi salah satu ancaman utama bagi infrastruktur nasional. Penggunaan SIEM atau Security Information and Event Management digunakan untuk mengumpulkan, menganalisis, dan melaporkan aktivitas jaringan yang mencurigakan atau berbahaya. PT Sembilan Pilar Semesta menerapkan Stel

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Adil Hussain Mohammed. "Invasive Weed Optimization Based Ransom-Ware Detection in Cloud Environment." September 2021 7, no. 09 (2021): 1–6. http://dx.doi.org/10.46501/ijmtst0709001.

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Cloud provide support to manage, control, monitor different organization. Due to flexible nature f cloud chance of attack on it increases by means of some software attack in form of ransomware. Many of researcher has proposed various model to prevent such attacks or to identify such activities. This paper has proposed a ransomware detection model by use of trained neural network. Training of neural network was done by filter or optimized feature set obtained from the feature reduction algorithm. Paper has proposed a Invasive Weed Optimization algorithm that filter good set of feature from the

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KOVACS, Attila Mate. "SOFT COMPUTING IN PREVENTING RANSOMWARE RELYING ON LARGER-SCALE DATA AND ANALYSIS." Strategic Impact 87, no. 2 (2023): 66–84. http://dx.doi.org/10.53477/1842-9904-23-12.

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Ransomware attacks continue to pose a significant threat to organizations and individuals worldwide. The attackers’ ability to constantly evolve and adapt their tactics challenges traditional cybersecurity approaches to keep pace. Ransomware attacks targeting the healthcare industry accounted for 45% of all reported cyberattacks. The nature and scale of attacks and the increasing healthcare technology adoption will continue to pose ransomware attack risks. However, by collecting and analyzing large volumes of data and applying soft computing techniques, cybersecurity experts can improve their

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Jin Ho Park, Jin Ho Park, Sushil Kumar Singh Jin Ho Park, Mikail Mohammed Salim Sushil Kumar Singh, Abir EL Azzaoui Mikail Mohammed Salim, and Jong Hyuk Park Abir EL Azzaoui. "Ransomware-based Cyber Attacks: A Comprehensive Survey." 網際網路技術學刊 23, no. 7 (2022): 1557–64. http://dx.doi.org/10.53106/160792642022122307010.

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<p>Internet of Things (IoT) and sensor devices have been connected due to the development of the IoT and Information Communication Technology (ICT). It offers automatic environments in smart city and IoT scenarios and describes investments in advanced resources in futuristic human lives as sustainable growth of quality-wise life with intelligent infrastructure. Nowadays, IoT devices are continuously increasing and utilized in advanced IoT applications, including Smart Homes, Smart Farming, Smart Enterprises, and others. However, security and privacy are significant challenges with Ransom

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Jegede, Abayomi, Ayotinde Fadele, Monday Onoja, Gilbert Aimufua, and Ismaila Jesse Mazadu. "Trends and Future Directions in Automated Ransomware Detection." Journal of Computing and Social Informatics 1, no. 2 (2022): 17–41. http://dx.doi.org/10.33736/jcsi.4932.2022.

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Ransomware attacks constitute major security threats to personal and corporate data and information. A successful ransomware attack results in significant security and privacy violations with attendant financial losses and reputational damages to owners of computer-based resources. This makes it imperative for accurate, timely and reliable detection of ransomware. Several techniques have been proposed for ransomware detection and each technique has its strengths and limitations. The aim of this paper is to discuss the current trends and future directions in automated ransomware detection. The

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Watney, Murdoch. "Cybersecurity Threats to and Cyberattacks on Critical Infrastructure: a Legal Perspective." European Conference on Cyber Warfare and Security 21, no. 1 (2022): 319–27. http://dx.doi.org/10.34190/eccws.21.1.196.

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Over the years cybersecurity threats to and cyberattacks on the critical infrastructure by state and non-state actors have escalated in intensity and sophistication. Cyberattacks, such as the 2017 NotPetya ransomware attack, the 2020 SolarWinds software supply chain attack and the 2021 Colonial Pipeline ransomware attack, illustrate the vulnerability of critical infrastructure to cyberattacks.  Most cyberattacks are committed across borders involving criminal hackers or state supported hackers. Furthermore, critical infrastructure is increasingly interconnected and interdependent.

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RODRIGUES GUZELLA DIAS, JHONATAN, RENATA MIRELLA FARINA, and FABIANA FLORIAN. "SEGURANÇA CIBERNÉTICA - ESTUDO DAS TÉCNICAS DE ATAQUES CIBERNÉTICOS (PHISHING, RANSMWARE, DDOS) DE ENGENHARIA SOCIAL E MEDIDAS DE PREVENÇÃO." Revista Científica Semana Acadêmica 12, no. 248 (2024): 1–16. http://dx.doi.org/10.35265/2236-6717-248-13011.

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Cyberattacks are a crucial topic in the area of information security given the increasing sophistication and diversity of threats faced by organizations and individual users. This work aims to study different cyber attack techniques, including phishing, ransomware and denial of service attacks (DDoS). The study aims to understand how these attacks are carried out, identify attack vectors and analyze the damage caused to victims. Qualitative bibliographical research was carried out. It is concluded that from the study of cyber attack techniques (phishing, ransomware and DDoS), it continues to b

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Ban, Mohammed Khammas. "Comparative analysis of various machine learning algorithms for ransomware detection." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 20, no. 1 (2022): 43–51. https://doi.org/10.12928/telkomnika.v20i1.18812.

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Recently, the ransomware attack posed a serious threat that targets a wide range of organizations and individuals for financial gain. So, there is a real need to initiate more innovative methods that are capable of proactively detect and prevent this type of attack. Multiple approaches were innovated to detect attacks using different techniques. One of these techniques is machine learning techniques which provide reasonable results, in most attack detection systems. In the current article, different machine learning techniques are tested to analyze its ability in a detection ransomware attack.

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Sathya, T., Keertika N, Shwetha S, Deepti Upodhyay, and Hasanov Muzafar. "Bitcoin Heist Ransomware Attack Prediction Using Data Science Process." E3S Web of Conferences 399 (2023): 04056. http://dx.doi.org/10.1051/e3sconf/202339904056.

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In recent years, ransomware attacks have become a more significant source of computer penetration. Only general-purpose computing systems with sufficient resources have been harmed by ransomware so far. Numerous ransomware prediction strategies have been published, but more practical machine learning ransomware prediction techniques still need to be developed. In order to anticipate ransomware assaults, this study provides a method for obtaining data from artificial intelligence and machine learning systems. A more accurate model for outcome prediction is produced by using the data science met

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Eliando, Eliando, and Ary Budi Warsito. "LockBit Black Ransomware On Reverse Shell: Analysis of Infection." CogITo Smart Journal 9, no. 2 (2023): 228–40. http://dx.doi.org/10.31154/cogito.v9i2.494.228-240.

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This research was conducted due to the widespread occurrence of ransomware attacks, especially in Indonesia, against data that is at the endpoint and has even reached the banking sector. to estimate the likelihood of future ransomware infections. LockBit 3 ransomware aka LockBit Black is ransomware that has penetrated one of the banks in Indonesia, along with a reverse shell which is an infection method that cannot be recognized by every protection so that when combined it can penetrate all sides of protection. The method used to research the combination of ransomware and reverse shell is a hy

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Et.al, MohammedIbrahim. "IoTContact: A Strategy for Predicting Contagious IoT Nodes in Mitigating Ransomware Attacks." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (2021): 1957–62. http://dx.doi.org/10.17762/turcomat.v12i3.1030.

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Although the emergence of the Internet of Things (IoT) can facilitate various aspects of people’s lives, most IoT devices are vulnerable to ransomware attacks. Ransomware attacks in IoT networks can be more devastating due to its capability of affecting billions of interconnected devices. Ransomware can take control of compromised devices or an overall system and allow limited access to user interaction with IoT devices. Hence, there is a need for a strategy that can mitigate and predicts affected IoT devices to conduct in-depth forensic analysis in the event of a ransomware attack. This paper

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Zhang, Yipeng, Min Li, Xiaoming Zhang, Yueying He, and Zhoujun Li. "Defeat Magic with Magic: A Novel Ransomware Attack Method to Dynamically Generate Malicious Payloads Based on PLC Control Logic." Applied Sciences 12, no. 17 (2022): 8408. http://dx.doi.org/10.3390/app12178408.

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The Industrial Control System (ICS) is a public facility that provides services to lots of users; thus, its security has always been a critical factor in measuring its availability. Recently, a new type of attack on ICS has occurred frequently, which realizes the extortion of users by invading the information domain and destroying the physical domain. However, due to the diversity and unavailability of an ICS control logic, the targets of such attacks are usually limited to PCs and servers, leaving more disruptive attack methods unexplored. To contribute more possible attack methods to strengt

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Krishna, B. L. "Comparative Study of Fileless Ransomware." International Journal of Trend in Scientific Research and Development 4, no. 3 (2020): 608–16. https://doi.org/10.5281/zenodo.3892449.

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A Fileless Ransomware is a new type of ransomware primarily follows the mechanism of both ransomware and fileless malware. Detecting and Defending these kinds of attacks becoming a great obstacle for IT firms. Cybercriminals found a new way of extorting ransom with vicious methods mainly from big organizations, government, Telecom Industry and many more. Traditional AV Engines are not able to defend Fileless Malware. This paper describes the mechanism of both ransomware and fileless malware, the working of fileless ransomware, what are the possible attack vectors of fileless ransomware, variat

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Novita, Aliya Putri, Fahmi Fatmanegara, Faithly Jabez Jefune Runtuwene, Joeanna Talita Samuela, and Muhammad Fahri Syahbani. "CYBER SECURITY THREATS; ANALISIS DAN MITIGASI RESIKO RANSOMWARE DI INDONESIA." Jurnal Ilmiah Sistem Informasi 3, no. 1 (2023): 160–69. http://dx.doi.org/10.46306/sm.v3i1.91.

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Ransomware is a type of malware that encrypts a victim's data and demands a ransom payment for its decryption. These attacks can cause significant financial losses, disrupt business operations, and compromise sensitive data. Therefore, it is crucial to understand the mechanisms of ransomware and implement appropriate preventive measures. Preventive Measures against Ransomware Attacks; First, strengthen infrastructure security: Implement best practices such as firewalls, antivirus software, and data encryption to safeguard systems. Second, enhance employee security awareness: Provide cybersecur

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Owolafe, Otasowie, and Aderonke F. Thompson. "Analysis of Crypto-Ransomware Using Network Traffic." Journal of Information Security and Cybercrimes Research 5, no. 1 (2022): 72–79. http://dx.doi.org/10.26735/jvuj3498.

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Ransomware is a form of malware attack that makes use of encryption to make information inaccessible for the motive of gathering a specified amount of payment. Many victims of this attack who couldn’t recover their information from backups have been compelled to decide between losing the information or paying the sum requested by the attacker. This research shows some of the various samples of ransomware, the phases of attack, and the chance of recognizing ransomware by the network traffic patterns it generates. Traffic generated from the infected system was considered. Experimental results fr

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Poniatowska-Jaksch, Małgorzata. "Threat of ransomware attack in Europe." Journal of Modern Science 52, no. 3 (2023): 379–95. http://dx.doi.org/10.13166/jms/173091.

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Cel pracyNegatywną konsekwencją cyfryzacji gospodarki jest rozwój przestępczości cyfrowej, a w tej grupie bardzo dużym zagrożeniem jest atak złośliwym oprogramowania ransomware w modelu RaaS. Celem artykułu jest próba określenia skali i identyfikacja kierunków (sektorów) ataków ransomware w 11 państwach UE w powiązaniu z integracją technologii cyfrowych przez przedsiębiorstwa i poziomem społecznych kompetencji cyfrowych.Materiał i metodyWydzielenie grup państw o wysokim, średnim i niskim poziomie zjawiska ransomware umożliwiło zastosowanie autorskiego syntetycznego wskaźnik skonstruowanego na

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Wira Zanoramy A. Zakaria, Nur Mohammad Kamil Mohammad Alta, Mohd Faizal Abdollah, Othman Abdollah, and S.M. Warusia Mohamed S.M.M Yassin. "Early Detection of Windows Cryptographic Ransomware Based on Pre-Attack API Calls Features and Machine Learning." Journal of Advanced Research in Applied Sciences and Engineering Technology 39, no. 2 (2024): 110–31. http://dx.doi.org/10.37934/araset.39.2.110131.

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Ransomware attacks are currently one of cybersecurity's greatest and most alluring threats. Antivirus software is frequently ineffective against zero-day malware and ransomware attacks; consequently, significant network infections could result in substantial data loss. Such attacks are also becoming more dynamic and capable of altering their signatures, resulting in a race to the bottom regarding weaponry. Cryptographic ransomware exploits crypto-viral extortion techniques. The malware encrypts the victim's data and demands payment in exchange. The attacker would release the data decryption ke

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Najam, Hassan. "Ransomware Attack on Medstar: Ethical Position statement." SEISENSE Journal of Management 1, no. 4 (2018): 29–31. https://doi.org/10.5281/zenodo.1435408.

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Medstar Health was the target of ransomware attack in 2016. The attack impacted the provision of healthcare services to the patient. Medstar opted not to pay the ransom and, instead, responded by shutting down its electronic medical record (EMR) systems and restoring the data from the backups. The paper analyzes the event, Medstar’s response and its negligent behavior that allowed the vulnerability to be exploited. The author provides an ethical position statement and recommendation to reduce the chances of any future attacks.

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Wang, ZiHan, ChaoGe Liu, Jing Qiu, ZhiHong Tian, Xiang Cui, and Shen Su. "Automatically Traceback RDP-Based Targeted Ransomware Attacks." Wireless Communications and Mobile Computing 2018 (December 6, 2018): 1–13. http://dx.doi.org/10.1155/2018/7943586.

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While various ransomware defense systems have been proposed to deal with traditional randomly-spread ransomware attacks (based on their unique high-noisy behaviors at hosts and on networks), none of them considered ransomware attacks precisely aiming at specific hosts, e.g., using the common Remote Desktop Protocol (RDP). To address this problem, we propose a systematic method to fight such specifically targeted ransomware by trapping attackers via a network deception environment and then using traceback techniques to identify attack sources. In particular, we developed various monitors in the

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Fang, Xiang, Eric Song, Cheng Ning, Huseyn Huseynov, and Tarek Saadawi. "Crypto-Ransomware Detection Through a Honeyfile-Based Approach with R-Locker." Mathematics 13, no. 12 (2025): 1933. https://doi.org/10.3390/math13121933.

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Ransomware is a group of malware that aims to make computing resources unavailable, demanding a ransom amount to return control back to users. Ransomware can be classified into two types: crypto-ransomware and locker ransomware. Crypto-ransomware employs strong encryption and prevents users’ access to the system. Locker ransomware makes access unavailable to users either by locking the boot sector or the user’s desktop. The proposed solution is an anomaly-based ransomware detection and prevention system consisting of post- and pre-encryption detection stages. The developed IDS is capable of de

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Turner, Adam B., Stephen McCombie, and Allon J. Uhlmann. "Discerning payment patterns in Bitcoin from ransomware attacks." Journal of Money Laundering Control 23, no. 3 (2020): 545–89. http://dx.doi.org/10.1108/jmlc-02-2020-0012.

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Purpose The purpose of this paper is to investigate available forensic data on the Bitcoin blockchain to identify typical transaction patterns of ransomware attacks. Specifically, the authors explore how distinct these patterns are and their potential value for intelligence exploitation in support of countering ransomware attacks. Design/methodology/approach The authors created an analytic framework – the Ransomware–Bitcoin Intelligence–Forensic Continuum framework – to search for transaction patterns in the blockchain records from actual ransomware attacks. Data of a number of different ranso

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Mohamed, Tasnem Magdi Hassin, Bander Ali Saleh Al-rimy, and Sultan Ahmed Almalki. "A Ransomware Early Detection Model based on an Enhanced Joint Mutual Information Feature Selection Method." Engineering, Technology & Applied Science Research 14, no. 4 (2024): 15400–15407. http://dx.doi.org/10.48084/etasr.7092.

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Crypto ransomware attacks pose a significant threat by encrypting users' data and demanding ransom payments, causing permanent data loss if not detected and mitigated before encryption occurs. The existing studies have faced challenges in the pre-encryption phase due to elusive attack patterns, insufficient data, and the lack of comprehensive information, often confusing the current detection techniques. Selecting appropriate features that effectively indicate an impending ransomware attack is a critical challenge. This research addresses this challenge by introducing an Enhanced Joint Mutual

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Collier, Roger. "NHS ransomware attack spreads worldwide." Canadian Medical Association Journal 189, no. 22 (2017): E786—E787. http://dx.doi.org/10.1503/cmaj.1095434.

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Andersen, Erik Sørup. "How to mitigate ransomware risk through data and risk quantification." Cyber Security: A Peer-Reviewed Journal 8, no. 2 (2025): 131. http://dx.doi.org/10.69554/ztgt3456.

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Ransomware attacks have, over the past years, been the most frequent cyberattack type and a growing community of adversaries continues to innovate methods for extorting organisations into paying ransom. Yet this risk is still, to many organisations, not well understood. Some refer to the averages reported in the media of the size of ransom and cost of ransomware attacks. But these numbers can be very far from the actual risk of a particular organisation. The nature of the risk, comprising many attack techniques and paths through an organisation’s IT assets affecting a range of systems, data an

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Salunke, Mangesh D., Subhash G. Rathod, Hemantkumar B. Jadhav, Meghna Yashwante, Vaibhav D. Rewaskar, and Pranjali V. Deshmukh. "Implementation of Decoy Deception based Detection System for Ransomware Attack." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 8s (2023): 714–19. http://dx.doi.org/10.17762/ijritcc.v11i8s.7673.

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Ransomware poses a dangerous threat to cybersecurity. Data as well as rights owned by the user are adversely impacted. The situation has become considerably more critical as a result of the emergence of new ransomware varieties and Ransomware-as-a-Service. In this paper, we presented a novel deception-based and behaviour-based method for real-time ransomware detection. In order to avoid any loss before ransomware is discovered, we build pretend files and directories for nefarious behaviours. We conducted a pilot study using Locky, and the results demonstrate the effectiveness of our strategy w

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Turner, Adam B., Stephen McCombie, and Allon J. Uhlmann. "A target-centric intelligence approach to WannaCry 2.0." Journal of Money Laundering Control 22, no. 4 (2019): 646–65. http://dx.doi.org/10.1108/jmlc-01-2019-0005.

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Purpose This paper aims to demonstrate the utility of a target-centric approach to intelligence collection and analysis in the prevention and investigation of ransomware attacks that involve cryptocurrencies. The paper uses the May 2017 WannaCry ransomware usage of the Bitcoin ecosystem as a case study. The approach proves particularly beneficial in facilitating information sharing and an integrated analysis across intelligence domains. Design/methodology/approach This study conducted data collection and analysis of the component Bitcoin elements of the WannaCry ransomware attack. A note of bo

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Kim, TaeGuen. "Deception-based Method for Ransomware Detection." Journal of Internet Services and Information Security 13, no. 3 (2023): 191–201. http://dx.doi.org/10.58346/jisis.2023.i3.012.

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Ransomware is a rapidly growing malware threat that encrypts a user's files and demands a ransom for the decryption key. It has caused significant financial harm worldwide and is difficult to detect, especially when it's a new, unknown zero-day ransomware. Most commercial antivirus software relies on signature-based detection, which can be slow and inadequate for swiftly identifying suspicious programs. To tackle these challenges, this paper presents a ransomware protection method utilizing decoy files. Our deception-based protection method enhances ransomware detection with a fair decoy deplo

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Md. Tauhidur Rahman Rafi, Iffath Tanjim Moon, Md. Musfiqur Rahman Mridha, Md. Shahid Ahammed Shakil, Md. Jamil Chaudhary, and Md. Taufiq Khan. "Enhancing Cybersecurity: Comparative Insights in Machine Learning Models for Ransomware Detection." European Journal of Artificial Intelligence and Machine Learning 4, no. 3 (2025): 1–7. https://doi.org/10.24018/ejai.2025.4.3.60.

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Ransomware is a new cybersecurity attack with huge financial and operational impact in industries globally. In this paper, an investigation of utilizing machine learning algorithms for ransomware detection is performed and compared with conventional methods, which consistently fall prey to dynamically altering attacks. Various algorithms, such as Support Vector Machines, Random Forest, Gradient Boosting, Artificial Neural Networks, Logistic Regression and ensemble methods, have been evaluated, with ensemble method of Gradient Boosting and Logistic Regression proving validation accuracy of 100%

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