DAG-Based Attack and Defense Modeling: Don't Miss the Forest for the Attack Trees

This paper presents the current state of the art on attack and defense modeling approaches that are based on directed acyclic graphs (DAGs). DAGs allow for a hierarchical decomposition of complex scenarios into simple, easily understandable and quantifiable actions. Methods based on threat trees and Bayesian networks are two well-known approaches to security modeling. However there exist more than 30 DAG-based methodologies, each having different features and goals. The objective of this survey is to present a complete overview of graphical attack and defense modeling techniques based on DAGs. This consists of summarizing the existing methodologies, comparing their features and proposing a taxonomy of the described formalisms. This article also supports the selection of an adequate modeling technique depending on user requirements

Precision Physics at LEP

1 - Introduction 2 - Small-Angle Bhabha Scattering and the Luminosity Measurement 3 - Z^0 Physics 4 - Fits to Precision Data 5 - Physics at LEP2 6 - ConclusionsComment: Review paper to appear in the RIVISTA DEL NUOVO CIMENTO; 160 pages, LateX, 70 eps figures include

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

COUNTERING SMALL UNMANNED AIRCRAFT SYSTEMS WITH ADVANCED DATA ANALYSIS AND MACHINE LEARNING

In January 2021, the DOD released its first Counter-Small Unmanned Aircraft Systems Strategy to address the growing risk to military personnel, facilities, and assets posed by the rapid technological advancement and proliferation of sUAS. Existing counter-drone capabilities—heavily reliant on electronic warfare to disrupt the communication link between user and device—no longer address an evolving threat that includes autonomous drones, COTS technology, and an increasing number of drones in the airspace that can overwhelm a C-sUAS operator. To counter the increasingly complex small drone threat, the Army-led Joint Counter-sUAS Office is pursuing materiel and non-materiel solutions for its new system-of-systems approach. One vexing C-sUAS challenge involves radar detection systems discriminating some sUAS from other flying objects, like birds, due to their comparable size, slow movement, and low altitude. Inaccurate or inefficient sUAS classification using radar data can be a force protection threat due to the limited number of electro-optical sensors and human operators for classification at-scale. This thesis uses bird and drone radar track data from two different training environments to explore hidden structure in the data, develop independent unsupervised and supervised learning models using the two datasets, and experiment with data sampling and feature engineering to improve upon model robustness to different environments and dynamic environmental conditions.Lieutenant Colonel, United States ArmyApproved for public release. Distribution is unlimited