Publications (18)
Enhancing IoT Security via Automatic Network Traffic Analysis: The Transition from Machine Learning to Deep Learning
Mounia Hamidouche, Eugeny Popko, Bassem Ouni
This work provides a comparative analysis illustrating how Deep Learning (DL) surpasses Machine Learning (ML) in addressing tasks within Internet of Things (IoT), such as attack cl…
Harris Hawks Feature Selection in Distributed Machine Learning for Secure IoT Environments
Neveen Hijazi, Moayad Aloqaily, Bassem Ouni +2
The development of the Internet of Things (IoT) has dramatically expanded our daily lives, playing a pivotal role in the enablement of smart cities, healthcare, and buildings. Emer…
An Incremental Gray-box Physical Adversarial Attack on Neural Network Training
Rabiah Al-qudah, Moayad Aloqaily, Bassem Ouni +2
Neural networks have demonstrated remarkable success in learning and solving complex tasks in a variety of fields. Nevertheless, the rise of those networks in modern computing has…
VulnScout-C: A Lightweight Transformer for C Code Vulnerability Detection
Aymen Lassoued, Nacef Mbarek, Bechir Dardouri +3
Vulnerability detection in C programs is a critical challenge in software security. Although large language models (LLMs) achieve strong detection performance, their multi-billion-…
ShrinkBox: Backdoor Attack on Object Detection to Disrupt Collision Avoidance in Machine Learning-based Advanced Driver Assistance Systems
Muhammad Zaeem Shahzad, Muhammad Abdullah Hanif, Bassem Ouni +1
Advanced Driver Assistance Systems (ADAS) significantly enhance road safety by detecting potential collisions and alerting drivers. However, their reliance on expensive sensor tech…
PatchBlock: A Lightweight Defense Against Adversarial Patches for Embedded EdgeAI Devices
Nandish Chattopadhyay, Abdul Basit, Amira Guesmi +3
Adversarial attacks pose a significant challenge to the reliable deployment of machine learning models in EdgeAI applications, such as autonomous driving and surveillance, which re…