arXiv subjects

Nathan D. Schiele

Publications and source records attributed to Nathan D. Schiele.

3 recordsLinked to original sources

arXiv2026

Attack Tree Distance: a practical examination of tree difference measurement within cyber security

Attack trees are a popular threat modeling method. In practice, there is often a need to compare attack tree models produced by human experts, based on both the structure of the tree and the meaning of the node labels. In this work, we investigate the problem of comparing attack trees and measuring their similarity. We define five different measures for measuring the distance between two attack trees: Label Distance (LD), Tree Edit Distance (TED), Radical Distance (RD), Multiset Distance (MSD) and Weighted Sum Distance (WSD). We further propose a repeatable method of both theoretical and experimental attack tree distance measures validation. Our theoretical validation consists of a series of basic transformations to evaluate the behavior of distance measures with respect to specific types of transformations that may appear between two attack trees. To experimentally validate our distance measures, we designed and executed a human study ($n=39$) to collect a dataset of attack trees to be used for evaluation and comparison of the measures. From our theoretical and experimental results, we find that applying semantic similarity as a means of comparing node labels is a valid approach. Further, we find four of the five attack tree distance measures are valid approaches in certain, varying circumstances. Our results suggest that these methods can already be used to identify similar real-world attack trees. Overall, this work lays the groundwork for improved threat model analysis, validation of AI-generated attack trees, and future research into threat similarity measurement in cybersecurity.

cs.CR↗

arXiv2025

Empirical assessment of the perception of graphical threat model acceptability

Threat modeling (TM) is an important aspect of risk analysis and secure software engineering. Graphical threat models are a recommended tool to analyze and communicate threat information. However, the comparison of different graphical threat models, and the acceptability of these threat models for an audience with a limited technical background, is not well understood, despite these users making up a sizable portion of the cybersecurity industry. We seek to compare the acceptability of three general, graphical threat models, Attack-Defense Trees (ADTs), Attack Graphs (AGs), and CORAS, for users with a limited technical background. We conducted a laboratory study with 38 bachelor students who completed tasks with the three threat models across three different scenarios assigned using a Latin square design. Threat model submissions were qualitatively analyzed, and participants filled out a perception questionnaire based on the Method Evaluation Model (MEM). We find that both ADTs and CORAS are broadly acceptable for a wide range of scenarios, and both could be applied successfully by users with a limited technical background; further, we also find that the lack of a specific tool for AGs may have impacted the perceived usefulness of AGs. We can recommend that users with a limited technical background use ADTs or CORAS as a general graphical TM method. Further research on the acceptability of AGs to such an audience and the effect of a dedicated TM tool support is needed.

cs.CR↗

arXiv2022

Comparative Evaluation of Bipartite, Node-Link, and Matrix-Based Network Representations

This work investigates and compares the performance of node-link diagrams, adjacency matrices, and bipartite layouts for visualizing networks. In a crowd-sourced user study (n = 150), we measure the task accuracy and completion time of the three representations for different network classes and properties. In contrast to the literature, which covers mostly topology-based tasks (e.g., path finding) in small datasets, we mainly focus on overview tasks for large and directed networks. We consider three overview tasks on networks with 500 nodes: (T1) network class identification, (T2) cluster detection, and (T3) network density estimation, and two detailed tasks: (T4) node in-degree vs. out-degree and (T5) representation mapping, on networks with 50 and 20 nodes, respectively. Our results show that bipartite layouts are beneficial for revealing the overall network structure, while adjacency matrices are most reliable across the different tasks.

cs.HC↗