2.4k citations · 3.3k across the 39 of their papers we have counts for
9 papers · 1 filter
Meta Reinforcement Learning for Strategic IoT Deployments Coverage in Disaster-Response UAV Swarms
Marwan Dhuheir, Aiman Erbad, Ala Al-Fuqaha
In the past decade, Unmanned Aerial Vehicles (UAVs) have grabbed the attention of researchers in academia and industry for their potential use in critical emergency applications, s…
Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally
Shawqi Al-Maliki, Adnan Qayyum, Hassan Ali +5
Deep Neural Networks (DNNs) have been the driving force behind many of the recent advances in machine learning. However, research has shown that DNNs are vulnerable to adversarial…
Membership Inference Attacks on DNNs using Adversarial Perturbations
Hassan Ali, Adnan Qayyum, Ala Al-Fuqaha +1
Several membership inference (MI) attacks have been proposed to audit a target DNN. Given a set of subjects, MI attacks tell which subjects the target DNN has seen during training.…
Consistent Valid Physically-Realizable Adversarial Attack against Crowd-flow Prediction Models
Hassan Ali, Muhammad Atif Butt, Fethi Filali +2
Recent works have shown that deep learning (DL) models can effectively learn city-wide crowd-flow patterns, which can be used for more effective urban planning and smart city manag…
Addressing Data Distribution Shifts in Online Machine Learning Powered Smart City Applications Using Augmented Test-Time Adaptation
Shawqi Al-Maliki, Faissal El Bouanani, Mohamed Abdallah +2
Data distribution shift is a common problem in machine learning-powered smart city applications where the test data differs from the training data. Augmenting smart city applicatio…
Secure and Robust Machine Learning for Healthcare: A Survey
Adnan Qayyum, Junaid Qadir, Muhammad Bilal +1
Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ra…
Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning
Inaam Ilahi, Muhammad Usama, Junaid Qadir +4
Deep Reinforcement Learning (DRL) has numerous applications in the real world thanks to its outstanding ability in quickly adapting to the surrounding environments. Despite its gre…
Exploiting Unlabeled Data in Smart Cities using Federated Learning
Abdullatif Albaseer, Bekir Sait Ciftler, Mohamed Abdallah +1
Privacy concerns are considered one of the main challenges in smart cities as sharing sensitive data brings threatening problems to people's lives. Federated learning has emerged a…
Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward
Adnan Qayyum, Muhammad Usama, Junaid Qadir +1
Connected and autonomous vehicles (CAVs) will form the backbone of future next-generation intelligent transportation systems (ITS) providing travel comfort, road safety, along with…