13 citations · 13 across the 4 of their papers we have counts for
5 papers · 1 filter
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…
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…
Social Engineering Attacks: A Systemisation of Knowledge on People Against Humans
Scott Thomson, Michael Bewong, Arash Mahboubi +1
Our systematisation of knowledge on Social Engineering Attacks (SEAs), identifies the human, organisational, and adversarial dimensions of cyber threats. It addresses the growing r…
Setup Once, Secure Always: A Single-Setup Secure Federated Learning Aggregation Protocol with Forward and Backward Secrecy for Dynamic Users
Nazatul Haque Sultan, Yan Bo, Yansong Gao +10
Federated Learning (FL) enables multiple users to collaboratively train a machine learning model without sharing raw data, making it suitable for privacy-sensitive applications. Ho…
Malicious Package Detection using Metadata Information
S. Halder, M. Bewong, A. Mahboubi +7
Protecting software supply chains from malicious packages is paramount in the evolving landscape of software development. Attacks on the software supply chain involve attackers inj…