11 citations · 14 across the 10 of their papers we have counts for
11 papers
DDQN-MLP: An Explainable and Adversarially Robust DRL-Guided Adaptive Learning Framework for Ransomware Detection
Jannatul Ferdous, Rafiqul Islam, Arash Mahboubi +1
Ransomware detection remains challenging because modern variants exhibit diverse, evasive, and partly benign-like behaviors that undermine fixed supervised learning objectives. Thi…
Hybrid Machine Learning Framework for Herd-Level Cattle Growth Pattern and Weight Gain Forecasting in Grazing-Based Production Systems
Muhammad Riaz Hasib Hossain, Rafiqul Islam, Shawn R. McGrath +2
Commercial grazing systems yield irregular livestock observations, which challenge cattle growth forecasting. This study developed a hybrid machine learning framework for herd leve…
Beyond Classification Accuracy: Quantifying Fingerprint Complexity in Encrypted Darknet Services
Javeriah Saleem, Rafiqul Islam, Md Zahidul Islam
Existing darknet traffic studies primarily evaluate service fingerprintability through classification performance, providing limited insight into why certain services are easier or…
Open-World Darknet Traffic Recognition Under Leave-One-Service-Out Evaluation
Javeriah Saleem, Rafiqul Islam, Md Zahidul Islam
Darknet traffic recognition is critical for cyber threat intelligence, as anonymity networks are often used to conceal malicious activity. However, most existing studies rely on cl…
Behavioral Information Leakage in Darknet Traffic: A Multi-Channel Analysis Across Anonymity Networks
Javeriah Saleem, Rafiqul Islam, Md Zahidul Islam
Existing darknet traffic classification studies largely emphasize predictive accuracy while offering limited insight into the behavioral mechanisms that make encrypted services dis…
Auditable Machine Unlearning for Privacy-Compliant Ransomware Detection Using Multi-Shard SISA and Deep Reinforcement Learning
Jannatul Ferdous, Rafiqul Islam, Md Zahidul Islam
Ransomware poses an escalating cybersecurity threat as attackers continuously modify behavioral patterns to evade static defenses. Although existing machine learning-based detector…
Privacy-Aware Machine Unlearning with SISA for Reinforcement Learning-Based Ransomware Detection
Jannatul Ferdous, Rafiqul Islam, Md Zahidul Islam
Ransomware detection systems increasingly rely on behavior-based machine learning to address evolving attack strategies. However, emerging privacy compliance, data governance, and…
Mob-based cattle weight gain forecasting using ML models
Muhammad Riaz Hasib Hossain, Rafiqul Islam, Shawn R McGrath +2
Forecasting mob based cattle weight gain (MB CWG) may benefit large livestock farms, allowing farmers to refine their feeding strategies, make educated breeding choices, and reduce…
Learning-based estimation of cattle weight gain and its influencing factors
Muhammad Riaz Hasib Hossain, Rafiqul Islam, Shawn R. McGrath +2
Many cattle farmers still depend on manual methods to measure the live weight gain of cattle at set intervals, which is time consuming, labour intensive, and stressful for both the…
CL3: A Collaborative Learning Framework for the Medical Data Ensuring Data Privacy in the Hyperconnected Environment
Mohamamd Zavid Parvez, Rafiqul Islam, Md Zahidul Islam
In a hyperconnected environment, medical institutions are particularly concerned with data privacy when sharing and transmitting sensitive patient information due to the risk of da…
Darknet Traffic Analysis A Systematic Literature Review
Javeriah Saleem, Rafiqul Islam, Zahidul Islam
The primary objective of an anonymity tool is to protect the anonymity of its users through the implementation of strong encryption and obfuscation techniques. As a result, it beco…