4 citations · 4 across the 9 of their papers we have counts for
5 papers · 1 filter
Toward Real-World IoT Security: Concept Drift-Resilient IoT Botnet Detection via Latent Space Representation Learning and Alignment
Hassan Wasswa, Timothy Lynar
Although AI-based models have achieved high accuracy in IoT threat detection, their deployment in enterprise environments is constrained by reliance on stationary datasets that fai…
A Quantized VAE-MLP Botnet Detection Model: A Systematic Evaluation of Quantization-Aware Training and Post-Training Quantization Strategies
Hassan Wasswa, Hussein Abbass, Timothy Lynar
In an effort to counter the increasing IoT botnet-based attacks, state-of-the-art deep learning methods have been proposed and have achieved impressive detection accuracy. However,…
Graph Attention Neural Network for Botnet Detection: Evaluating Autoencoder, VAE and PCA-Based Dimension Reduction
Hassan Wasswa, Hussein Abbass, Timothy Lynar
With the rise of IoT-based botnet attacks, researchers have explored various learning models for detection, including traditional machine learning, deep learning, and hybrid approa…
Preserving Seasonal and Trend Information: A Variational Autoencoder-Latent Space Arithmetic Based Approach for Non-stationary Learning
Hassan Wasswa, Aziida Nanyonga, Timothy Lynar
AI models have garnered significant research attention towards predictive task automation. However, a stationary training environment is an underlying assumption for most models an…
Impact of Latent Space Dimension on IoT Botnet Detection Performance: VAE-Encoder Versus ViT-Encoder
Hassan Wasswa, Aziida Nanyonga, Timothy Lynar
The rapid evolution of Internet of Things (IoT) technology has led to a significant increase in the number of IoT devices, applications, and services. This surge in IoT devices, al…