activity
20222026
most citedLiuer Mihou: A Practical Framework for Generating and Evaluating Grey-box Adversarial Attacks against NIDS

2 citations · 4 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CR2026

APT-Agent: Automated Penetration Testing using Large Language Models

William Guanting Li, Alsharif Abuadbba, Kristen Moore +1

Penetration testing is essential to securing modern web infrastructures, yet traditional manual methods struggle to keep pace with their scale and complexity. Large Language Models…

cs.AI2026

Risk-Aware Human-in-the-Loop Framework with Adaptive Intrusion Response for Autonomous Vehicles

Dawood Wasif, Terrence J. Moore, Seunghyun Yoon +4

Autonomous vehicles must remain safe and effective when encountering rare long-tailed scenarios or cyber-physical intrusions during driving. We present RAIL, a risk-aware human-in-…

cs.HC2025

DASH: Deception-Augmented Shared Mental Model for a Human-Machine Teaming System

Zelin Wan, Han Jun Yoon, Nithin Alluru +6

We present DASH (Deception-Augmented Shared mental model for Human-machine teaming), a novel framework that enhances mission resilience by embedding proactive deception into Shared…

cs.RO2025

DriveMind: A Dual Visual Language Model-based Reinforcement Learning Framework for Autonomous Driving

Dawood Wasif, Terrence J. Moore, Chandan K. Reddy +5

End-to-end autonomous driving systems map sensor data directly to control commands, but remain opaque, lack interpretability, and offer no formal safety guarantees. While recent vi…

cs.HC2025★ 2 cited

Advancing Human-Machine Teaming: Concepts, Challenges, and Applications

Dian Chen, Han Jun Yoon, Zelin Wan +9

Human-Machine Teaming (HMT) is revolutionizing collaboration across domains such as defense, healthcare, and autonomous systems by integrating AI-driven decision-making, trust cali…

cs.LG2022

Semantic Preserving Adversarial Attack Generation with Autoencoder and Genetic Algorithm

Xinyi Wang, Simon Yusuf Enoch, Dong Seong Kim

Widely used deep learning models are found to have poor robustness. Little noises can fool state-of-the-art models into making incorrect predictions. While there is a great deal of…

cs.CR2022★ 2 cited

Liuer Mihou: A Practical Framework for Generating and Evaluating Grey-box Adversarial Attacks against NIDS

Ke He, Dan Dongseong Kim, Jing Sun +3

Due to its high expressiveness and speed, Deep Learning (DL) has become an increasingly popular choice as the detection algorithm for Network-based Intrusion Detection Systems (NID…