16 citations · 61 across the 30 of their papers we have counts for
11 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.…
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…
ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicing
Fatemeh Lotfi, Hossein Rajoli, Fatemeh Afghah
Advanced wireless networks must support highly dynamic and heterogeneous service demands. Open Radio Access Network (O-RAN) architecture enables this flexibility by adopting modula…
Prompt-Tuned LLM-Augmented DRL for Dynamic O-RAN Network Slicing
Fatemeh Lotfi, Hossein Rajoli, Fatemeh Afghah
Modern wireless networks must adapt to dynamic conditions while efficiently managing diverse service demands. Traditional deep reinforcement learning (DRL) struggles in these envir…
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…
Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classification
Amirmohammad Bamdad, Ali Owfi, Fatemeh Afghah
DL-based automatic modulation classification (AMC) models are highly susceptible to adversarial attacks, where even minimal input perturbations can cause severe misclassifications.…
Open RAN LSTM Traffic Prediction and Slice Management using Deep Reinforcement Learning
Fatemeh Lotfi, Fatemeh Afghah
With emerging applications such as autonomous driving, smart cities, and smart factories, network slicing has become an essential component of 5G and beyond networks as a means of…
A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management
Sayed Pedram Haeri Boroujeni, Abolfazl Razi, Sahand Khoshdel +7
Wildfires have emerged as one of the most destructive natural disasters worldwide, causing catastrophic losses in both human lives and forest wildlife. Recently, the use of Artific…
A Meta-learning based Generalizable Indoor Localization Model using Channel State Information
Ali Owfi, ChunChih Lin, Linke Guo +3
Indoor localization has gained significant attention in recent years due to its various applications in smart homes, industrial automation, and healthcare, especially since more pe…
Autoencoder-based Radio Frequency Interference Mitigation For SMAP Passive Radiometer
Ali Owfi, Fatemeh Afghah
Passive space-borne radiometers operating in the 1400-1427 MHz protected frequency band face radio frequency interference (RFI) from terrestrial sources. With the growth of wireles…
Synthetic ECG Signal Generation Using Generative Neural Networks
Edmond Adib, Fatemeh Afghah, John J. Prevost
Electrocardiogram (ECG) datasets tend to be highly imbalanced due to the scarcity of abnormal cases. Additionally, the use of real patients' ECGs is highly regulated due to privacy…