18 citations · 53 across the 17 of their papers we have counts for
18 papers
Semantic Causal-Factor Inference from Aviation Incident Narratives Using A Variational Autoencoder with Cosine-Similarity-Based Reconstruction
AZIIDA NANYONGA1, HASSAN WASSWA, UGUR TURHAN +2
Aviation accident and incident investigations generate extensive unstructured textual information containing evidence relevant to the causes and contributing factors of safety occu…
How the Graph Construction Technique Shapes Performance in IoT Botnet Detection
Hassan Wasswa, Hussein Abbass, Timothy Lynar
The increasing incidence of IoT-based botnet attacks has driven interest in advanced learning models for detection. Recent efforts have focused on leveraging attention mechanisms t…
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,…
Are GNNs Worth the Effort for IoT Botnet Detection? A Comparative Study of VAE-GNN vs. ViT-MLP and VAE-MLP Approaches
Hassan Wasswa, Hussein Abbass, Timothy Lynar
Due to the exponential rise in IoT-based botnet attacks, researchers have explored various advanced techniques for both dimensionality reduction and attack detection to enhance IoT…
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…
Enhancing IoT-Botnet Detection using Variational Auto-encoder and Cost-Sensitive Learning: A Deep Learning Approach for Imbalanced Datasets
Hassan Wasswa, Timothy Lynar, Hussein Abbass
The Internet of Things (IoT) technology has rapidly gained popularity with applications widespread across a variety of industries. However, IoT devices have been recently serving a…
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…
IoT Botnet Detection: Application of Vision Transformer to Classification of Network Flow Traffic
Hassan Wasswa, Timothy Lynar, Aziida Nanyonga +1
Despite the demonstrated effectiveness of transformer models in NLP, and image and video classification, the available tools for extracting features from captured IoT network flow…
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…
Phase of Flight Classification in Aviation Safety using LSTM, GRU, and BiLSTM: A Case Study with ASN Dataset
Aziida Nanyonga, Hassan Wasswa, Graham Wild
Safety is the main concern in the aviation industry, where even minor operational issues can lead to serious consequences. This study addresses the need for comprehensive aviation…
Exploring Aviation Incident Narratives Using Topic Modeling and Clustering Techniques
Aziida Nanyonga, Hassan Wasswa, Ugur Turhan +2
Aviation safety is a global concern, requiring detailed investigations into incidents to understand contributing factors comprehensively. This study uses the National Transportatio…
Aviation Safety Enhancement via NLP & Deep Learning: Classifying Flight Phases in ATSB Safety Reports
Aziida Nanyonga, Hassan Wasswa, Graham Wild
Aviation safety is paramount, demanding precise analysis of safety occurrences during different flight phases. This study employs Natural Language Processing (NLP) and Deep Learnin…
Natural Language Processing and Deep Learning Models to Classify Phase of Flight in Aviation Safety Occurrences
Aziida Nanyonga, Hassan Wasswa, Oleksandra Molloy +2
The air transport system recognizes the criticality of safety, as even minor anomalies can have severe consequences. Reporting accidents and incidents play a vital role in identify…
Sequential Classification of Aviation Safety Occurrences with Natural Language Processing
Aziida Nanyonga, Hassan Wasswa, Ugur Turhan +2
Safety is a critical aspect of the air transport system given even slight operational anomalies can result in serious consequences. To reduce the chances of aviation safety occurre…
Comparative Study of Deep Learning Architectures for Textual Damage Level Classification
Aziida Nanyonga, Hassan Wasswa, Graham Wild
Given the paramount importance of safety in the aviation industry, even minor operational anomalies can have significant consequences. Comprehensive documentation of incidents and…
Comparative Analysis of Topic Modeling Techniques on ATSB Text Narratives Using Natural Language Processing
Aziida Nanyonga, Hassan Wasswa, Ugur Turhan +2
Improvements in aviation safety analysis call for innovative techniques to extract valuable insights from the abundance of textual data available in accident reports. This paper ex…
Topic Modeling Analysis of Aviation Accident Reports: A Comparative Study between LDA and NMF Models
Aziida Nanyonga, Hassan Wasswa, Graham Wild
Aviation safety is paramount in the modern world, with a continuous commitment to reducing accidents and improving safety standards. Central to this endeavor is the analysis of avi…