2.4k citations · 2.8k across the 28 of their papers we have counts for
33 papers
A Client-level Assessment of Collaborative Backdoor Poisoning in Non-IID Federated Learning
Phung Lai, Guanxiong Liu, NhatHai Phan +3
Federated learning (FL) enables collaborative model training using decentralized private data from multiple clients. While FL has shown robustness against poisoning attacks with ba…
Genetic Algorithm-Based Dynamic Backdoor Attack on Federated Learning-Based Network Traffic Classification
Mahmoud Nazzal, Nura Aljaafari, Ahmed Sawalmeh +8
Federated learning enables multiple clients to collaboratively contribute to the learning of a global model orchestrated by a central server. This learning scheme promotes clients'…
Multi-Instance Adversarial Attack on GNN-Based Malicious Domain Detection
Mahmoud Nazzal, Issa Khalil, Abdallah Khreishah +2
Malicious domain detection (MDD) is an open security challenge that aims to detect if an Internet domain is associated with cyber-attacks. Among many approaches to this problem, gr…
Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach
Mahmoud Nazzal, Abdallah Khreishah, Joyoung Lee +3
Prediction of taxi service demand and supply is essential for improving customer's experience and provider's profit. Recently, graph neural networks (GNNs) have been shown promisin…
IMA-GNN: In-Memory Acceleration of Centralized and Decentralized Graph Neural Networks at the Edge
Mehrdad Morsali, Mahmoud Nazzal, Abdallah Khreishah +1
In this paper, we propose IMA-GNN as an In-Memory Accelerator for centralized and decentralized Graph Neural Network inference, explore its potential in both settings and provide a…
Heterogeneous Randomized Response for Differential Privacy in Graph Neural Networks
Khang Tran, Phung Lai, NhatHai Phan +5
Graph neural networks (GNNs) are susceptible to privacy inference attacks (PIAs), given their ability to learn joint representation from features and edges among nodes in graph dat…
An Adaptive Black-box Defense against Trojan Attacks (TrojDef)
Guanxiong Liu, Abdallah Khreishah, Fatima Sharadgah +1
Trojan backdoor is a poisoning attack against Neural Network (NN) classifiers in which adversaries try to exploit the (highly desirable) model reuse property to implant Trojans int…
How to Backdoor HyperNetwork in Personalized Federated Learning?
Phung Lai, NhatHai Phan, Issa Khalil +2
This paper explores previously unknown backdoor risks in HyperNet-based personalized federated learning (HyperNetFL) through poisoning attacks. Based upon that, we propose a novel…
Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions
Izzat Alsmadi, Kashif Ahmad, Mahmoud Nazzal +4
The growing use of social media has led to the development of several Machine Learning (ML) and Natural Language Processing(NLP) tools to process the unprecedented amount of social…
Smart Traffic Monitoring System using Computer Vision and Edge Computing
Guanxiong Liu, Hang Shi, Abbas Kiani +5
Traffic management systems capture tremendous video data and leverage advances in video processing to detect and monitor traffic incidents. The collected data are traditionally for…
A Synergetic Attack against Neural Network Classifiers combining Backdoor and Adversarial Examples
Guanxiong Liu, Issa Khalil, Abdallah Khreishah +1
In this work, we show how to jointly exploit adversarial perturbation and model poisoning vulnerabilities to practically launch a new stealthy attack, dubbed AdvTrojan. AdvTrojan i…
ManiGen: A Manifold Aided Black-box Generator of Adversarial Examples
Guanxiong Liu, Issa Khalil, Abdallah Khreishah +5
Machine learning models, especially neural network (NN) classifiers, have acceptable performance and accuracy that leads to their wide adoption in different aspects of our daily li…
Using Single-Step Adversarial Training to Defend Iterative Adversarial Examples
Guanxiong Liu, Issa Khalil, Abdallah Khreishah
Adversarial examples have become one of the largest challenges that machine learning models, especially neural network classifiers, face. These adversarial examples break the assum…
Using Intuition from Empirical Properties to Simplify Adversarial Training Defense
Guanxiong Liu, Issa Khalil, Abdallah Khreishah
Due to the surprisingly good representation power of complex distributions, neural network (NN) classifiers are widely used in many tasks which include natural language processing,…
ZK-GanDef: A GAN based Zero Knowledge Adversarial Training Defense for Neural Networks
Guanxiong Liu, Issa Khalil, Abdallah Khreishah
Neural Network classifiers have been used successfully in a wide range of applications. However, their underlying assumption of attack free environment has been defied by adversari…
GanDef: A GAN based Adversarial Training Defense for Neural Network Classifier
Guanxiong Liu, Issa Khalil, Abdallah Khreishah
Machine learning models, especially neural network (NN) classifiers, are widely used in many applications including natural language processing, computer vision and cybersecurity.…
A Hybrid RF-VLC System for Energy Efficient Wireless Access
Abdallah Khreishah, Sihua Shao, Ammar Gharaibeh +3
In this paper, we propose a new paradigm in designing and realizing energy efficient wireless indoor access networks, namely, a hybrid system enabled by traditional RF access, such…
Hierarchical Capacity Provisioning for Fog Computing
Abbas Kiani, Nirwan Ansari, Abdallah Khreishah
The concept of fog computing is centered around providing computation resources at the edge of network, thereby reducing the latency and improving the quality of service. However,…
Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges
Hazim Shakhatreh, Ahmad Sawalmeh, Ala Al-Fuqaha +6
The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains including real-time monitoring, providing wireless coverage, remote sensing, sea…
Optimal Placement of a UAV to Maximize the Lifetime of Wireless Devices
Hazim Shakhatreh, Abdallah Khreishah
Unmanned aerial vehicles (UAVs) can be used as aerial wireless base stations when cellular networks go down. Prior studies on UAV-based wireless coverage typically consider downlin…
Maximizing Indoor Wireless Coverage Using UAVs Equipped with Directional Antennas
Hazim Shakhatreh, Abdallah Khreishah
Unmanned aerial vehicles (UAVs) can be used to provide wireless coverage during emergency cases where each UAV serves as an aerial wireless base station when the cellular network g…
The Indoor Mobile Coverage Problem Using UAVs
Hazim Shakhatreh, Abdallah Khreishah, Issa Khalil
Unmanned aerial vehicles (UAVs) can be used as aerial wireless base stations when cellular networks are not operational due to natural disasters. They can also be used to supplemen…
Providing Wireless Coverage to High-rise Buildings Using UAVs
Hazim Shakhatreh, Abdallah Khreishah, Bo Ji
Unmanned aerial vehicles (UAVs) can be used as aerial wireless base stations when cellular networks go down. Prior studies on UAV-based wireless coverage typically consider an Air-…
Efficient 3D Placement of a UAV Using Particle Swarm Optimization
Hazim Shakhatreh, Abdallah Khreishah, Ayoub Alsarhan +3
Unmanned aerial vehicles (UAVs) can be used as aerial wireless base stations when cellular networks go down. Prior studies on UAV-based wireless coverage typically consider an Air-…