166 citations · 291 across the 36 of their papers we have counts for
11 papers · 1 filter
KAN-Robust-Bench: A Benchmark for Evaluating the Robustness of Kolmogorov-Arnold Networks
Mohammad Meymani, Roozbeh Razavi-Far
While machine learning models have demonstrated strong performance in many domains, these models have shown profound vulnerabilities when they are exposed to adversarial threats. W…
TSAI-MetaFraud: A Benchmark Dataset for Financial Fraud Transaction and Behavioral Risk Detection in Metaverse Ecosystems
Refat Ishrak Hemel, Ehsan Hallaji, Roozbeh Razavi-Far
The emergence of metaverse platforms has created virtual economies that introduce new challenges related to fraud, bot activity, and illicit financial behavior. Despite growing int…
Quantum Adversarial Machine Learning: From Classical Adaptations to Quantum-Native Methods
Roozbeh Razavi-Far, Mohammad Meymani, Erfan Mahmoudinia +6
Machine learning has revolutionized numerous industrial domains. Despite recent advances, machine learning models remain vulnerable to adversarial threats. Adversarial machine lear…
Divided We Fall: Defending Against Adversarial Attacks via Soft-Gated Fractional Mixture-of-Experts with Randomized Adversarial Training
Mohammad Meymani, Roozbeh Razavi-Far
Machine learning is a powerful tool enabling full automation of a huge number of tasks without explicit programming. Despite recent progress of machine learning in different domain…
FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in Federated Learning
Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif
Federated learning systems are increasingly threatened by data poisoning attacks, where malicious clients compromise global models by contributing tampered updates. Existing defens…
TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning
Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif
The server-less nature of Decentralized Federated Learning (DFL) requires allocating the aggregation role to specific participants in each federated round. Current DFL architecture…
Federated Continual Learning: Concepts, Challenges, and Solutions
Parisa Hamedi, Roozbeh Razavi-Far, Ehsan Hallaji
Federated Continual Learning (FCL) has emerged as a robust solution for collaborative model training in dynamic environments, where data samples are continuously generated and dist…
Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review
Hossein Hassani, Ehsan Hallaji, Roozbeh Razavi-Far +2
Reinforcement learning (RL) is a sub-domain of machine learning, mainly concerned with solving sequential decision-making problems by a learning agent that interacts with the decis…
Learning From High-Dimensional Cyber-Physical Data Streams for Diagnosing Faults in Smart Grids
Hossein Hassani, Ehsan Hallaji, Roozbeh Razavi-Far +1
The performance of fault diagnosis systems is highly affected by data quality in cyber-physical power systems. These systems generate massive amounts of data that overburden the sy…
A Stream Learning Approach for Real-Time Identification of False Data Injection Attacks in Cyber-Physical Power Systems
Ehsan Hallaji, Roozbeh Razavi-Far, Meng Wang +2
This paper presents a novel data-driven framework to aid in system state estimation when the power system is under unobservable false data injection attacks. The proposed framework…
Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms
Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif
The advent of federated learning has facilitated large-scale data exchange amongst machine learning models while maintaining privacy. Despite its brief history, federated learning…