4 papers
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.…
RTLGuard: A Lightweight Teacher-Student Defense for Poisoned RTL Code Generation Models
Mahshid Rezakhani, Kimia Azar, Hadi Kamali
The rapid advancement of large language models (LLMs) is driving a shift toward automated register transfer level (RTL) code generation, enabling designers to translate high-level…
SafeTune: Mitigating Data Poisoning in LLM Fine-Tuning for RTL Code Generation
Mahshid Rezakhani, Nowfel Mashnoor, Kimia Azar +1
As large language models (LLMs) are increasingly fine-tuned for hardware tasks like RTL code generation, the scarcity of high-quality datasets often leads to the use of rapidly ass…
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…