3 citations · 5 across the 9 of their papers we have counts for
11 papers
Defense-in-Depth at the Perception-Reasoning Interface of LLM-Centric Agentic UAV Swarms
Mohammadhossein Homaei, Yousef Emami, Sajad Homayoun +4
Large Language Models (LLMs) increasingly support Uncrewed Aerial Vehicle (UAV) swarm operations such as data collection scheduling, where the model reads structured sensor reports…
LLM-Centric Agentic AI for UAV Swarms: Architecture, Enabling Technologies, and Open Problems
Yousef Emami, Rahim Taheri, Mohammadhossein Homaei +2
Uncrewed Aerial Vehicle (UAV) swarms have significant potential for applications such as Search and Rescue (SAR) and environmental monitoring, but their real-world deployment is li…
IE-RAP: An Intelligence and Efficient Reader Anti-Collision Protocol for Dense RFID Networks
Hadiseh Rezaei, Rahim Taheri, Mohammad Shojafar
An advanced technology known as a radio frequency identification (RFID) system enables seamless wireless communication between tags and readers. This system operates in what is ref…
A Risk-Stratified Benchmark Dataset for Bad Randomness (SWC-120) Vulnerabilities in Ethereum Smart Contracts
Hadis Rezaei, Rahim Taheri, Francesco Palmieri
Many Ethereum smart contracts rely on block attributes such as block.timestamp or blockhash to generate random numbers for applications like lotteries and games. However, these val…
Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer Networks
Ehsan Nowroozi, Imran Haider, Rahim Taheri +1
Federated Learning (FL) is a machine learning (ML) approach that enables multiple decentralized devices or edge servers to collaboratively train a shared model without exchanging r…
UNBUS: Uncertainty-aware Deep Botnet Detection System in Presence of Perturbed Samples
Rahim Taheri
A rising number of botnet families have been successfully detected using deep learning architectures. While the variety of attacks increases, these architectures should become more…
SETTI: A Self-supervised Adversarial Malware Detection Architecture in an IoT Environment
Marjan Golmaryami, Rahim Taheri, Zahra Pooranian +2
In recent years, malware detection has become an active research topic in the area of Internet of Things (IoT) security. The principle is to exploit knowledge from large quantities…
Deep Image: A precious image based deep learning method for online malware detection in IoT Environment
Meysam Ghahramani, Rahim Taheri, Mohammad Shojafar +2
The volume of malware and the number of attacks in IoT devices are rising everyday, which encourages security professionals to continually enhance their malware analysis tools. Res…
Similarity-based Android Malware Detection Using Hamming Distance of Static Binary Features
Rahim Taheri, Meysam Ghahramani, Reza Javidan +3
In this paper, we develop four malware detection methods using Hamming distance to find similarity between samples which are first nearest neighbors (FNN), all nearest neighbors (A…
On Defending Against Label Flipping Attacks on Malware Detection Systems
Rahim Taheri, Reza Javidan, Mohammad Shojafar +3
Label manipulation attacks are a subclass of data poisoning attacks in adversarial machine learning used against different applications, such as malware detection. These types of a…
Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach
Rahim Taheri, Reza Javidan, Mohammad Shojafar +2
The widespread adoption of smartphones dramatically increases the risk of attacks and the spread of mobile malware, especially on the Android platform. Machine learning-based solut…