Rahim Taheri

Orcid: 0000-0002-4078-3105

According to our database1, Rahim Taheri authored at least 33 papers between 2019 and 2026.

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Bibliography

2026
IE-RAP: An Intelligence and Efficient Reader Anti-Collision Protocol for Dense RFID Networks.
CoRR, February, 2026

A Risk-Stratified Benchmark Dataset for Bad Randomness (SWC-120) Vulnerabilities in Ethereum Smart Contracts.
CoRR, January, 2026

A Survey on Security and Privacy in Federated Learning-Based Intrusion Detection Systems for 5G and Beyond Networks.
IEEE Open J. Commun. Soc., 2026

2025
A Random Deep Feature Selection Approach to Mitigate Transferable Adversarial Attacks.
IEEE Trans. Netw. Serv. Manag., December, 2025

Federated learning-based robust android malware detection: label-flipping attacks and defenses.
Neural Comput. Appl., November, 2025

LFD-IDS: Bagging-Based Data Poisoning Attacks Against Cyberattack Detection in Connected Vehicle.
IEEE Trans. Intell. Transp. Syst., October, 2025

Federated RNN for Intrusion Detection System in IoT Environment Under Adversarial Attack.
J. Netw. Syst. Manag., October, 2025

Federated Learning Under Attack: Exposing Vulnerabilities Through Data Poisoning Attacks in Computer Networks.
IEEE Trans. Netw. Serv. Manag., February, 2025

PASCOINFOG/PASFOG: Privacy-Preserving Data Deduplication Algorithms for Fog Storage Systems.
IEEE Consumer Electron. Mag., January, 2025

A Survey on Privacy and Security in Distributed Cloud Computing: Exploring Federated Learning and Beyond.
IEEE Open J. Commun. Soc., 2025

GRAF-IDS: graph-based clustering as aggregation for federated intrusion detection system in IoT network.
Neural Comput. Appl., 2025

FedLLMGuard: A federated large language model for anomaly detection in 5G networks.
Comput. Networks, 2025

Adapt-LFA: Adaptive Gradient-Guided Label Flipping Attack Against Federated Learning-based Intrusion Detection in IoT.
Proceedings of the IEEE International Conference on Cyber Security and Resilience, 2025

2024
Deep Image: A precious image based deep learning method for online malware detection in IoT environment.
Internet Things, 2024

Enhancing federated learning robustness through randomization and mixture.
Future Gener. Comput. Syst., 2024

Unveiling vulnerabilities in deep learning-based malware detection: Differential privacy driven adversarial attacks.
Comput. Secur., 2024

Verifying the Robustness of Machine Learning based Intrusion Detection Against Adversarial Perturbation.
Proceedings of the IEEE International Conference on Cyber Security and Resilience, 2024

Peer-to-Peer Meets Onion: The Veilid Framework.
Proceedings of the IEEE International Conference on Cyber Security and Resilience, 2024

2023
SIEMS: A Secure Intelligent Energy Management System for Industrial IoT Applications.
IEEE Trans. Ind. Informatics, 2023

Impact of Aggregation Function Randomization against Model Poisoning in Federated Learning.
Proceedings of the 22nd IEEE International Conference on Trust, 2023

Robust Aggregation Function in Federated Learning.
Proceedings of the Advances in Information Systems, Artificial Intelligence and Knowledge Management, 2023

Moving Towards Explainable Artificial Intelligence Using Fuzzy Rule-Based Networks in Decision-Making Process.
Proceedings of the Advances in Information Systems, Artificial Intelligence and Knowledge Management, 2023

2022
SETTI: A Self-supervised AdvErsarial Malware DeTection ArchiTecture in an IoT Environment.
ACM Trans. Multim. Comput. Commun. Appl., 2022

UNBUS: Uncertainty-aware Deep Botnet Detection System in Presence of Perturbed Samples.
CoRR, 2022

Can Open and AI-Enabled 6G RAN Be Secured?
IEEE Consumer Electron. Mag., 2022

2021
Fed-IIoT: A Robust Federated Malware Detection Architecture in Industrial IoT.
IEEE Trans. Ind. Informatics, 2021

LEVER: Secure Deduplicated Cloud Storage With Encrypted Two-Party Interactions in Cyber-Physical Systems.
IEEE Trans. Ind. Informatics, 2021

Adversarial android malware detection for mobile multimedia applications in IoT environments.
Multim. Tools Appl., 2021

RSS: An Energy-Efficient Approach for Securing IoT Service Protocols Against the DoS Attack.
IEEE Internet Things J., 2021

2020
On defending against label flipping attacks on malware detection systems.
Neural Comput. Appl., 2020

Similarity-based Android malware detection using Hamming distance of static binary features.
Future Gener. Comput. Syst., 2020

Can machine learning model with static features be fooled: an adversarial machine learning approach.
Clust. Comput., 2020

2019
Automatic Clustering of Attacks in Intrusion Detection Systems.
Proceedings of the 16th IEEE/ACS International Conference on Computer Systems and Applications, 2019


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