Human Understandable Artificial Intelligence 2024

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 1763

Special Issue Editor


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Guest Editor
Department of Computer Science, North Dakota State University, Fargo, ND 58102, USA
Interests: artificial/computational Intelligence; autonomy applications in aerospace; cybersecurity; 3D printing command/control and assessment; educational assessment in computing disciplines
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Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has been shown to be effective across numerous domains; from robotics to scientific data analysis to decision support systems, it is regularly used every day. The prevalence of AI has led to increased focus on ensuring that the decisions that systems make are equitable and accurate. In order to make sure that system operate effectively, determine how they can be improved and ensure that they are fair, humans must be able to understand how AI makes decisions. To this end, some have proposed AI techniques that can be explained in terms of decision-making electronic processes. Others have argued that the decision rationale should be human understandable and understandable by those using and impacted by the systems, not just technical experts. Both techniques are areas of ongoing advancement.

This Special Issue focuses on human-understandable artificial intelligence systems. We welcome papers on new and adapted understandable AI systems as well as papers relating to questions of system ethics, AI regulation and policy and application domain efficacy. Papers on supporting technologies and educational efforts related to understandable AI are also within the scope of this Special Issue.

Dr. Jeremy Straub
Guest Editor

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Keywords

  • artificial intelligence (AI)
  • explainable artificial intelligence (EAI)
  • human-understandable
  • decision justification

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Published Papers (1 paper)

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Research

37 pages, 18036 KiB  
Article
Node Classification of Network Threats Leveraging Graph-Based Characterizations Using Memgraph
by Sadaf Charkhabi, Peyman Samimi, Sikha S. Bagui, Dustin Mink and Subhash C. Bagui
Computers 2024, 13(7), 171; https://doi.org/10.3390/computers13070171 - 15 Jul 2024
Viewed by 1295
Abstract
This research leverages Memgraph, an open-source graph database, to analyze graph-based network data and apply Graph Neural Networks (GNNs) for a detailed classification of cyberattack tactics categorized by the MITRE ATT&CK framework. As part of graph characterization, the page rank, degree centrality, betweenness [...] Read more.
This research leverages Memgraph, an open-source graph database, to analyze graph-based network data and apply Graph Neural Networks (GNNs) for a detailed classification of cyberattack tactics categorized by the MITRE ATT&CK framework. As part of graph characterization, the page rank, degree centrality, betweenness centrality, and Katz centrality are presented. Node classification is utilized to categorize network entities based on their role in the traffic. Graph-theoretic features such as in-degree, out-degree, PageRank, and Katz centrality were used in node classification to ensure that the model captures the structure of the graph. The study utilizes the UWF-ZeekDataFall22 dataset, a newly created dataset which consists of labeled network logs from the University of West Florida’s Cyber Range. The uniqueness of this study is that it uses the power of combining graph-based characterization or analysis with machine learning to enhance the understanding and visualization of cyber threats, thereby improving the network security measures. Full article
(This article belongs to the Special Issue Human Understandable Artificial Intelligence 2024)
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