Ransomware reloaded: Re-examining its trend, research and mitigation in the era of data exfiltration

T McIntosh, T Susnjak, T Liu, D Xu, P Watters… - ACM Computing …, 2024 - dl.acm.org
Ransomware has grown to be a dominant cybersecurity threat by exfiltrating, encrypting, or
destroying valuable user data and causing numerous disruptions to victims. The severity of …

An intelligent ransomware attack detection and classification using dual vision transformer with Mantis Search Split Attention Network

K Ashwini, KB Nagasundara - Computers and Electrical Engineering, 2024 - Elsevier
Ransomware attacks pose significant cybersecurity threats, compromising computer
systems, data centers and various applications across sectors. Their sophistication …

Ransomware detection using machine learning: A review, research limitations and future directions

J Ispahany, MDR Islam, MZ Islam, MA Khan - IEEE Access, 2024 - ieeexplore.ieee.org
Ransomware attacks are on the rise in terms of both frequency and impact. The shift to
remote work due to the COVID-19 pandemic has led more people to work online, prompting …

Top Cyber Threats: The Rise of Ransomware

A Djenna, M Belaoued, N Lifa - IFIP International Conference on …, 2024 - Springer
Ransomware stands out as a particularly malicious type of cyberattack, wielding the
potential to inflict severe financial, operational, and reputational harm. The insidious nature …

A Sysmon Incremental Learning System for Ransomware Analysis and Detection

J Ispahany, MD Islam, MA Khan, MD Islam - arXiv preprint arXiv …, 2025 - arxiv.org
In the face of increasing cyber threats, particularly ransomware attacks, there is a pressing
need for advanced detection and analysis systems that adapt to evolving malware …

Towards Effective Machine Learning Models for Ransomware Detection via Low-Level Hardware Information

C Woralert, C Liu, Z Blasingame - … Support for Security and Privacy 2024, 2024 - dl.acm.org
In recent years, ransomware attacks have grown dramatically. New variants continually
emerging make tracking and mitigating these threats increasingly difficult using traditional …

iCNN-LSTM: A batch-based incremental ransomware detection system using Sysmon

J Ispahany, MD Islam, MA Khan, MD Islam - arXiv preprint arXiv …, 2025 - arxiv.org
In response to the increasing ransomware threat, this study presents a novel detection
system that integrates Convolutional Neural Networks (CNNs) and Long Short-Term …

Context-Aware Anomaly-based Detection for Ransomware using Multivariate Feature

M Pratiwi, YH Choi - 2024 IEEE Conference on …, 2024 - ieeexplore.ieee.org
In this research, we introduce a context-aware anomaly detection-based method for
ransomware detection. We extracted the ransomware installation and communication stage …

A Comparison of One-class and Two-class Models for Ransomware Detection via Low-level Hardware Information

C Woralert, C Liu, Z Blasingame… - 2023 Asian Hardware …, 2023 - ieeexplore.ieee.org
Recent years have witnessed a dramatic growth in ransomware attacks. Even though many
tools have been developed to help combat against these attacks, new varieties of …

[PDF][PDF] DEVELOPING EFFECTIVE SOLUTIONS: RESEARCH DIRECTIONS AND IMPLEMENTATION STRATEGIES FOR EARLY RANSOMWARE DETECTION

AHR ABOGAMOUS - Journal of Theoretical and Applied Information …, 2024 - jatit.org
Ransomware attacks, employing advanced encryption to hold data hostage, pose a critical
threat to targets ranging from individuals to critical infrastructure. Our study, analyzing over …