Technical Upgrades to and Enhancements of a System Vulnerability Analysis Tool Based on the Blackboard Architecture
- URL: http://arxiv.org/abs/2409.10892v1
- Date: Tue, 17 Sep 2024 05:06:42 GMT
- Title: Technical Upgrades to and Enhancements of a System Vulnerability Analysis Tool Based on the Blackboard Architecture
- Authors: Matthew Tassava, Cameron Kolodjski, Jeremy Straub,
- Abstract summary: Generalization logic building on the Blackboard Architecture's rule-fact paradigm was implemented in this system.
The paper concludes with a discussion of avenues of future work, including the implementation of multithreading.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
Related papers
- Data Analysis in the Era of Generative AI [56.44807642944589]
This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges.
We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow.
We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
arXiv Detail & Related papers (2024-09-27T06:31:03Z) - 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 [0.0]
SONARR is a system vulnerability analysis tool for complex mission critical systems.
This paper describes and analyzes the performance of a multi-threaded SONARR algorithm and other enhancements.
arXiv Detail & Related papers (2024-09-17T05:09:10Z) - An Extensible Framework for Architecture-Based Data Flow Analysis for Information Security [1.7749883815108154]
Security-related properties are often analyzed based on data flow diagrams (DFDs)
We present an open and framework for data flow analysis.
The framework is compatible with DFDs and can also extract data flows from the Palladio architectural description language.
arXiv Detail & Related papers (2024-03-14T13:52:41Z) - Profile of Vulnerability Remediations in Dependencies Using Graph
Analysis [40.35284812745255]
This research introduces graph analysis methods and a modified Graph Attention Convolutional Neural Network (GAT) model.
We analyze control flow graphs to profile breaking changes in applications occurring from dependency upgrades intended to remediate vulnerabilities.
Results demonstrate the effectiveness of the enhanced GAT model in offering nuanced insights into the relational dynamics of code vulnerabilities.
arXiv Detail & Related papers (2024-03-08T02:01:47Z) - It Is Time To Steer: A Scalable Framework for Analysis-driven Attack Graph Generation [50.06412862964449]
Attack Graph (AG) represents the best-suited solution to support cyber risk assessment for multi-step attacks on computer networks.
Current solutions propose to address the generation problem from the algorithmic perspective and postulate the analysis only after the generation is complete.
This paper rethinks the classic AG analysis through a novel workflow in which the analyst can query the system anytime.
arXiv Detail & Related papers (2023-12-27T10:44:58Z) - Leveraging Traceability to Integrate Safety Analysis Artifacts into the
Software Development Process [51.42800587382228]
Safety assurance cases (SACs) can be challenging to maintain during system evolution.
We propose a solution that leverages software traceability to connect relevant system artifacts to safety analysis models.
We elicit design rationales for system changes to help safety stakeholders analyze the impact of system changes on safety.
arXiv Detail & Related papers (2023-07-14T16:03:27Z) - Distributed intelligence on the Edge-to-Cloud Continuum: A systematic
literature review [62.997667081978825]
This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today.
The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed.
arXiv Detail & Related papers (2022-04-29T08:06:05Z) - Federated Learning with Unreliable Clients: Performance Analysis and
Mechanism Design [76.29738151117583]
Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients.
However, low quality models could be uploaded to the aggregator server by unreliable clients, leading to a degradation or even a collapse of training.
We model these unreliable behaviors of clients and propose a defensive mechanism to mitigate such a security risk.
arXiv Detail & Related papers (2021-05-10T08:02:27Z) - Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance.
We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems.
We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
arXiv Detail & Related papers (2020-10-19T13:09:31Z) - A Safety Framework for Critical Systems Utilising Deep Neural Networks [13.763070043077633]
This paper presents a principled novel safety argument framework for critical systems that utilise deep neural networks.
The approach allows various forms of predictions, e.g., future reliability of passing some demands, or confidence on a required reliability level.
It is supported by a Bayesian analysis using operational data and the recent verification and validation techniques for deep learning.
arXiv Detail & Related papers (2020-03-07T23:35:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.