1 citations · 1 across the 4 of their papers we have counts for
4 papers · 1 filter
SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design
Jannatul Ferdous, Rafiqul Islam, Md Zahidul Islam
Ransomware detection is a security-critical task in which false negatives and false positives have unequal operational consequences. Conventional machine learning detectors often u…
TL-RL-FusionNet: An Adaptive and Efficient Reinforcement Learning-Driven Transfer Learning Framework for Detecting Evolving Ransomware Threats
Jannatul Ferdous, Rafiqul Islam, Arash Mahboubi +1
Modern ransomware exhibits polymorphic and evasive behaviors by frequently modifying execution patterns to evade detection. This dynamic nature disrupts feature spaces and limits t…
A Sysmon Incremental Learning System for Ransomware Analysis and Detection
Jamil Ispahany, MD Rafiqul Islam, M. Arif Khan +1
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 behavi…
iCNN-LSTM: A batch-based incremental ransomware detection system using Sysmon
Jamil Ispahany, MD Rafiqul Islam, M. Arif Khan +1
In response to the increasing ransomware threat, this study presents a novel detection system that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM)…