arXiv subjects

Matthew Tassava

Publications and source records attributed to Matthew Tassava.

6 recordsLinked to original sources

arXiv2026

Core Logic and Algorithmic Performance Enhancements for a System Vulnerability Analysis Technique for Complex Mission Critical Systems Implementation

Core logic and processing improvements were made to the software for operations and network attack results review (SONARR) and are presented, herein. Previous SONARR versions' Boolean-only logic, derived from the Blackboard Architecture, was replaced with generic logic that allows any .NET type (e.g., integers, decimals, strings) to be utilized within facts. This allows calculations and equality operations with all data types to drive the algorithm's processing of network models. Additionally, multi-compute capabilities were implemented to increase the processing power for larger workloads. In this paper, the new logic objects are described, examples are presented to illustrate the efficacy of creating digital-twin systems using the new generic logic, and performance test results are presented that illustrate the expanded processing capability from the multi-compute functionality.

cs.CR↗

arXiv2024

Technical Upgrades to and Enhancements of a System Vulnerability Analysis Tool Based on the Blackboard Architecture

A system vulnerability analysis technique (SVAT) for the analysis of complex mission critical systems (CMCS) that cannot be taken offline or subjected to the risks posed by traditional penetration testing was previously developed. This system uses path-based analysis of vulnerabilities to identify potential threats to system security. Generalization logic building on the Blackboard Architecture's rule-fact paradigm was implemented in this system, the software for operation and network attack results review (SONARR). This paper presents an overview of additional functionality that has been added to this tool and the experimentation that was conducted to analyze their efficacy and the performance benefits of the new in-memory processing capabilities of the SONARR algorithm. The results of the performance tests and their relation to networks' architecture are discussed. The paper concludes with a discussion of avenues of future work, including the implementation of multithreading, additional analysis metrics like confidentiality, integrity, and availability, and improved heuristic development.

cs.CR↗

arXiv2024

Enhancing Security Testing Software for Systems that Cannot be Subjected to the Risks of Penetration Testing Through the Incorporation of Multi-threading and and Other Capabilities

The development of a system vulnerability analysis tool (SVAT) for complex mission critical systems (CMCS) produced the software for operation and network attack results review (SONARR). This software builds upon the Blackboard Architecture and uses its a rule-fact logic to assess model networks to identify potential pathways that an attacker might take through them via the exploitation of vulnerabilities within the network. The SONARR objects and algorithm were developed previously; however, performance was insufficient for analyzing large networks. This paper describes and analyzes the performance of a multi-threaded SONARR algorithm and other enhancements which were developed to increase SONARR's performance and facilitate the analysis of large networks.

cs.CR↗

arXiv2024

Development of an AI Anti-Bullying System Using Large Language Model Key Topic Detection

This paper presents and evaluates work on the development of an artificial intelligence (AI) anti-bullying system. The system is designed to identify coordinated bullying attacks via social media and other mechanisms, characterize them and propose remediation and response activities to them. In particular, a large language model (LLM) is used to populate an enhanced expert system-based network model of a bullying attack. This facilitates analysis and remediation activity - such as generating report messages to social media companies - determination. The system is described and the efficacy of the LLM for populating the model is analyzed herein.

cs.AI↗

arXiv2023

Analysis of the Benefits and Efficacy of the Addition of Variants and Reality Paths to the Blackboard Architecture

While the Blackboard Architecture has been in use since the 1980s, it has recently been proposed for modeling computer networks to assess their security. To do this, it must account for complex network attack patterns involving multiple attack routes and possible mid-attack system state changes. This paper proposes a data structure which can be used to model paths from an ingress point to a given egress point in Blackboard Architecture-modeled computer networks. It is designed to contain the pertinent information required for a systematic traversal through a changing network. This structure, called a reality path, represents a single potential pathway through the network with a given set of facts in a particular sequence of states. Another structure, called variants, is used during traversal of nodes (called containers) modeled in the network. The two structures - reality paths and variants - facilitate the use of a traversal algorithm, which will find all possible attack paths in Blackboard Architecture-modeled networks. This paper introduces and assesses the efficacy of variants and reality paths

cs.CR↗

arXiv2023

Development of a System Vulnerability Analysis Tool for Assessment of Complex Mission Critical Systems

A system vulnerability analysis technique (SVAT) for complex mission critical systems (CMCS) was developed in response to the need to be able to conduct penetration testing on large industrial systems which cannot be taken offline or risk disablement or impairment for conventional penetration testing. SVAT-CMCS facilitates the use of known vulnerability and exploit information, incremental testing of system components and data analysis techniques to identify attack pathways in CMCSs. This data can be utilized for corrective activities or to target controlled manual follow-up testing. This paper presents the SVAT-CMCS paradigm and describes its implementation in a software tool, which was built using the Blackboard Architecture, that can be utilized for attack pathway identification. The performance of this tool is characterized using three example models. In particular, it explores the path generation speed and the impact of link cap restrictions on system operations, under different levels of network size and complexity. Accurate fact-rule processing is also tested using these models. The results show significant decreases in path generation efficiency as the link cap and network complexity increase; however, rule processing accuracy is not impacted.

cs.CR↗