7 papers
Robustness of Anomaly Detection Models for Industrial Control Systems under Training-Time Data Contamination
Mustafa Umut Ozbek, Taiwo Ojo, Pooria Madani +2
Machine-learning-based anomaly detection is increasingly used in industrial control systems (ICS), yet most studies assume that detector training data is trustworthy. In practice,…
Robust Ensemble of Selectively Strengthened and Augmented Predictors
Parsa Memarzadehsaghezi, Zahra Hashemi, Pooria Madani +1
Evasion attacks present a significant challenge to the robustness of machine learning (ML)-based classifiers, particularly in critical applications such as fraud detection and cybe…
SecRL-Prune: Structured Reinforcement Learning-Based Pruning of CodeLLMs for Preserving Adversarial Code Mutation
Parsa Memarzadehsaghezi, Pooria Madani, Khalil El-Khatib
Large code language models (CodeLLMs) can generate and rewrite programs, enabling functionality-preserving code mutation that may be used to create diverse malware variants and eva…
Quantum Entanglement and Measurement Noise: A Novel Approach to Satellite Node Authentication
Pooria Madani, Carolyn McGregor
In this paper, we introduce a novel authentication scheme for satellite nodes based on quantum entanglement and measurement noise profiles. Our approach leverages the unique noise…
Metamorphic Malware Evolution: The Potential and Peril of Large Language Models
Pooria Madani
Code metamorphism refers to a computer programming exercise wherein the program modifies its own code (partial or entire) consistently and automatically while retaining its core fu…
Noise as a Double-Edged Sword: Reinforcement Learning Exploits Randomized Defenses in Neural Networks
Steve Bakos, Pooria Madani, Heidar Davoudi
This study investigates a counterintuitive phenomenon in adversarial machine learning: the potential for noise-based defenses to inadvertently aid evasion attacks in certain scenar…