Young Hun Lee | Searcharxiv

arXiv2026

GARAGE: Characterizing the Automation Boundary in LLM-based Attack Graph Generation

While modern vehicle security depends on effective Cyber Threat Intelligence (CTI) synthesis, current automated tools struggle with unstructured data and automotive-specific architectural nuances. To bridge this gap, we introduce GARAGE, a RAG-powered framework that converts fragmented CTI into an actionable, domain-specific knowledge base for automated attack graph generation. GARAGE synthesizes a dataset of 12,786 CVEs and 140 incident reports into a STIX 2.1 and Auto-ISAC ATM-compliant knowledge base. By formalizing tactical-pattern-level scenarios through granular kill chain analysis, GARAGE achieves threat generation capabilities. Our 320 Leave-One-Out experiments reveal that the framework can accurately transfer security knowledge to entirely unseen vehicle architectures. Furthermore, we position GARAGE as a scalable TARA support tool within human-in-the-loop workflows, offering a comprehensive cost-performance analysis to guide its deployment across various LLM tiers.

cs.CR↗

arXiv2022

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

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 (NIDSes). Unfortunately, DL algorithms are vulnerable to adversarial examples that inject imperceptible modifications to the input and cause the DL algorithm to misclassify the input. Existing adversarial attacks in the NIDS domain often manipulate the traffic features directly, which hold no practical significance because traffic features cannot be replayed in a real network. It remains a research challenge to generate practical and evasive adversarial attacks. This paper presents the Liuer Mihou attack that generates practical and replayable adversarial network packets that can bypass anomaly-based NIDS deployed in the Internet of Things (IoT) networks. The core idea behind Liuer Mihou is to exploit adversarial transferability and generate adversarial packets on a surrogate NIDS constrained by predefined mutation operations to ensure practicality. We objectively analyse the evasiveness of Liuer Mihou against four ML-based algorithms (LOF, OCSVM, RRCF, and SOM) and the state-of-the-art NIDS, Kitsune. From the results of our experiment, we gain valuable insights into necessary conditions on the adversarial transferability of anomaly detection algorithms. Going beyond a theoretical setting, we replay the adversarial attack in a real IoT testbed to examine the practicality of Liuer Mihou. Furthermore, we demonstrate that existing feature-level adversarial defence cannot defend against Liuer Mihou and constructively criticise the limitations of feature-level adversarial defences.

cs.CR↗