31 citations · 84 across the 11 of their papers we have counts for
5 papers · 1 filter
Exploring Feature Importance and Explainability Towards Enhanced ML-Based DoS Detection in AI Systems
Paul Badu Yakubu, Evans Owusu, Lesther Santana +3
Denial of Service (DoS) attacks pose a significant threat in the realm of AI systems security, causing substantial financial losses and downtime. However, AI systems' high computat…
Enabling Trustworthy Federated Learning in Industrial IoT: Bridging the Gap Between Interpretability and Robustness
Senthil Kumar Jagatheesaperumal, Mohamed Rahouti, Ali Alfatemi +3
Federated Learning (FL) represents a paradigm shift in machine learning, allowing collaborative model training while keeping data localized. This approach is particularly pertinent…
Redefining DDoS Attack Detection Using A Dual-Space Prototypical Network-Based Approach
Fernando Martinez, Mariyam Mapkar, Ali Alfatemi +4
Distributed Denial of Service (DDoS) attacks pose an increasingly substantial cybersecurity threat to organizations across the globe. In this paper, we introduce a new deep learnin…
Nonlinear Transformations Against Unlearnable Datasets
Thushari Hapuarachchi, Jing Lin, Kaiqi Xiong +2
Automated scraping stands out as a common method for collecting data in deep learning models without the authorization of data owners. Recent studies have begun to tackle the priva…
Advancing DDoS Attack Detection: A Synergistic Approach Using Deep Residual Neural Networks and Synthetic Oversampling
Ali Alfatemi, Mohamed Rahouti, Ruhul Amin +3
Distributed Denial of Service (DDoS) attacks pose a significant threat to the stability and reliability of online systems. Effective and early detection of such attacks is pivotal…