100 citations · 118 across the 5 of their papers we have counts for
5 papers
IoT-based Android Malware Detection Using Graph Neural Network With Adversarial Defense
Rahul Yumlembam, Biju Issac, Seibu Mary Jacob +1
Since the Internet of Things (IoT) is widely adopted using Android applications, detecting malicious Android apps is essential. In recent years, Android graph-based deep learning r…
Detection of AI Generated Images Using Combined Uncertainty Measures and Particle Swarm Optimised Rejection Mechanism
Rahul Yumlembam, Biju Issac, Nauman Aslam +3
As AI-generated images become increasingly photorealistic, distinguishing them from natural images poses a growing challenge. This paper presents a robust detection framework that…
Enhancing Decision-Making in Windows PE Malware Classification During Dataset Shifts with Uncertainty Estimation
Rahul Yumlembam, Biju Issac, Seibu Mary Jacob
Artificial intelligence techniques have achieved strong performance in classifying Windows Portable Executable (PE) malware, but their reliability often degrades under dataset shif…
Insider Threat Detection Using GCN and Bi-LSTM with Explicit and Implicit Graph Representations
Rahul Yumlembam, Biju Issac, Seibu Mary Jacob +2
Insider threat detection (ITD) is challenging due to the subtle and concealed nature of malicious activities performed by trusted users. This paper proposes a post-hoc ITD framewor…
Comprehensive Botnet Detection by Mitigating Adversarial Attacks, Navigating the Subtleties of Perturbation Distances and Fortifying Predictions with Conformal Layers
Rahul Yumlembam, Biju Issac, Seibu Mary Jacob +1
Botnets are computer networks controlled by malicious actors that present significant cybersecurity challenges. They autonomously infect, propagate, and coordinate to conduct cyber…