arXiv subjects

Seojun Lee

Publications and source records attributed to Seojun Lee.

3 recordsLinked to original sources

arXiv2026

Fully Automated End-to-End Adversary Emulation from MITRE ATT\&CK Based Cyber Threat Intelligence Using LLMs

This paper presents a fully automated end-to-end framework for adversary emulation from MITRE ATT&CK-aligned CTI reports using LLMs. Unlike prior work, which either executes prewritten playbooks or partially automates playbook generation, our framework unifies playbook generation, execution, and failure recovery in a single workflow. In particular, although AURORA, the most recent prior study, generates playbooks from CTI reports, it still requires partial manual intervention and does not revise playbooks based on execution failures. Our framework generates Caldera playbooks from CTI reports, executes them automatically, and revises failed Abilities through a failure-type-aware recovery mechanism. Evaluated on 11 CTI reports with Claude Sonnet 4.5, GPT-4o, Gemini 2.5 Pro, and Grok 4 Fast, the framework achieved its best results with Claude Sonnet 4.5: 27.3 Abilities per playbook, 84.22% execution success after revision, and CTI Precision, Recall, and F1 of 73.95%, 52.48%, and 60.50%, respectively. The failure recovery mechanism consistently improved execution success across all evaluated LLM models by 14.59%p to 17.23%p. On the 10 CTI reports selected from AURORA's dataset, this mechanism further increased the final execution success rate, surpassing that of AURORA, which represents the state-of-the-art adversary emulation system.

cs.CR↗

arXiv2026

NeoNet: An End-to-End 3D MRI-Based Deep Learning Framework for Non-Invasive Prediction of Perineural Invasion via Generation-Driven Classification

Minimizing invasive diagnostic procedures to reduce the risk of patient injury and infection is a central goal in medical imaging. And yet, noninvasive diagnosis of perineural invasion (PNI), a critical prognostic factor involving infiltration of tumor cells along the surrounding nerve, still remains challenging, due to the lack of clear and consistent imaging criteria criteria for identifying PNI. To address this challenge, we present NeoNet, an integrated end-to-end 3D deep learning framework for PNI prediction in cholangiocarcinoma that does not rely on predefined image features. NeoNet integrates three modules: (1) NeoSeg, utilizing a Tumor-Localized ROI Crop (TLCR) algorithm; (2) NeoGen, a 3D Latent Diffusion Model (LDM) with ControlNet, conditioned on anatomical masks to generate synthetic image patches, specifically balancing the dataset to a 1:1 ratio; and (3) NeoCls, the final prediction module. For NeoCls, we developed the PNI-Attention Network (PattenNet), which uses the frozen LDM encoder and specialized 3D Dual Attention Blocks (DAB) designed to detect subtle intensity variations and spatial patterns indicative of PNI. In 5-fold cross-validation, NeoNet outperformed baseline 3D models and achieved the highest performance with a maximum AUC of 0.7903.

cs.CV↗

arXiv2015

Low-temperature, dry transfer-printing of a patterned graphene monolayer

Graphene has recently attracted much interest as a material for flexible, transparent electrodes or active layers in electronic and photonic devices. However, realization of such graphene-based devices is limited due to difficulties in obtaining patterned graphene monolayers on top of materials that are degraded when exposed to a high-temperature or wet process. We demonstrate a low-temperature, dry process capable of transfer-printing a patterned graphene monolayer grown on Cu foil onto a target substrate using an elastomeric stamp. A challenge in realizing this is to obtain a high-quality graphene layer on a hydrophobic stamp made of poly(dimethylsiloxane), which is overcome by introducing two crucial modifications to the conventional wet-transfer method - the use of a support layer composed of Au and the decrease in surface tension of the liquid bath. Using this technique, patterns of a graphene monolayer were transfer-printed on poly(3,4-ethylenedioxythiophene):polystyrene sulfonate and MoO3 both of which are easily degraded when exposed to an aqueous or aggressive patterning process. We discuss the range of application of this technique, which is currently limited by oligomer contaminants, and possible means to expand it by eliminating the contamination problem.

cond-mat.mtrl-sci↗