11 citations · 25 across the 19 of their papers we have counts for
4 papers · 1 filter
An Adversarial Zero-Shot Learning Approach for Anomaly Detection in Multivariate IoT Traffic Data
Mahshid Rezakhani, Tolunay Seyfi, Fatemeh Afghah
Anomaly detection in Internet of Things (IoT) networks presents unique challenges due to the diversity of devices, lack of labeled data, and domain variability across environments.…
RFPrompt: Prompt-Based Expert Adaptation of the Large Wireless Model for Modulation Classification
Md Raihan Uddin, Tolunay Seyfi, Fatemeh Afghah
Automatic modulation classification (AMC) in real-world deployments demands robustness to distribution shifts arising from hardware impairments, unseen propagation environments, an…
Adapt under Attack and Domain Shift: Unified Adversarial Meta-Learning and Domain Adaptation for Robust Automatic Modulation Classification
Ali Owfi, Amirmohammad Bamdad, Tolunay Seyfi +1
Deep learning has emerged as a leading approach for Automatic Modulation Classification (AMC), demonstrating superior performance over traditional methods. However, vulnerability t…
A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data
Mahshid Rezakhani, Tolunay Seyfi, Fatemeh Afghah
In recent years, rapid technological advancements and expanded Internet access have led to a significant rise in anomalies within network traffic and time-series data. Prompt detec…