Attack Tree

Attack trees are hierarchical models used to visually represent potential attack scenarios, aiding in risk assessment and security planning. Current research focuses on extending their capabilities to analyze increasingly complex systems, such as those incorporating machine learning or self-adaptation, often employing specialized algorithms for optimal agent scheduling or integrating attack trees with fault trees for comprehensive safety and security analysis. This work is significant for improving the effectiveness of security analysis in diverse domains, enabling more robust and proactive security measures against sophisticated threats.

Papers

December 28, 2023

Attack Tree Analysis for Adversarial Evasion Attacks
Yuki Yamaguchi, Toshiaki Aoki
Machine Learning Adversarial Attack Adversarial Evasion Attack Attack Tree

September 18, 2023

Towards Model Co-evolution Across Self-Adaptation Steps for Combined Safety and Security Analysis
Thomas Witte, Raffaela Groner, Alexander Raschke, Matthias Tichy, Irdin Pekaric, Michael Felderer
Self Adaptive System Safety Enhancement Self Evolution Network Attack Attack Tree

May 8, 2023

Optimal Scheduling of Agents in ADTrees: Specialised Algorithm and Declarative Models
Jaime Arias, Carlos Olarte, Laure Petrucci, Łukasz Maśko, Wojciech Penczek, Teofil Sidoruk
Practical Algorithm Agent Smith Multi Agent Setting Optimal Scheduling Optimal Attack Declarative Model Attack Tree

Attack Tree

Papers

Attack Tree Analysis for Adversarial Evasion Attacks

Towards Model Co-evolution Across Self-Adaptation Steps for Combined Safety and Security Analysis

Optimal Scheduling of Agents in ADTrees: Specialised Algorithm and Declarative Models