The State of the Art in BGP Visualization Tools: A Mapping of Visualization Techniques to Cyberattack Types

Raynor, Justin; Crnovrsanin, Tarik; Bartolomeo, Sara Di; South, Laura; Saffo, David; Dunne, Cody

doi:10.1109/tvcg.2022.3209412

IEEE Trans. Visual. Comput. Graphics

2022

DOI: 10.1109/tvcg.2022.3209412

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The State of the Art in BGP Visualization Tools: A Mapping of Visualization Techniques to Cyberattack Types

Justin Raynor

Tarik Crnovrsanin

Sara Di Bartolomeo

et al.

Abstract: Fig. 1. Timeline of attacks on the BGP system (bottom) and of the tools that have been proposed to visualize them. The bottom nodes and the corresponding edges are colored categorically by the type of attack. Each tool is connected to the specific attacks that were used to demonstrate or validate the tool design, and are colored to show the number of connected attacks.

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The Missing Link in Network Intrusion Detection: Taking AI/ML Research Efforts to Users

Dietz,

Mühlhauser,

Kögel

et al. 2024

IEEE Access

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Intrusion Detection Systems (IDS) tackle the challenging task of detecting network attacks as fast as possible. As this is getting more complex in modern enterprise networks, Artificial Intelligence (AI) and Machine Learning (ML) have gained substantial popularity in research. However, their adoption into real-world IDS solutions remains poor. Academic research often overlooks the interconnection of users and technical aspects. This leads to less explainable AI/ML models that hinder trust among AI/ML non-experts. Additionally, research often neglects secondary concerns such as usability and privacy. If IDS approaches conflict with current regulations or if administrators cannot deal with attacks more effectively, enterprises will not adopt the IDS in practice. To identify those problems systematically, our literature survey takes a user-centric approach; we examine IDS research from the perspective of stakeholders by applying the concept of personas. Further, we investigate multiple factors limiting the adoption of AI/ML in security and suggest technical, non-technical, and user-related considerations to enhance the adoption in practice. Our key contributions are threefold. (i) We derive personas from realistic enterprise scenarios, (ii) we provide a set of relevant hypotheses in the form of a review template, and (iii), based on our reviews, we derive design guidelines for practical implementations. To the best of our knowledge, this is the first paper that analyzes practical adoption barriers of AI/ML-based intrusion detection solutions concerning appropriateness of data, reproducibility, explainability, practicability, usability, and privacy. Our guidelines may help researchers to holistically evaluate their AI/ML-based IDS approaches to increase practical adoption.

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The Missing Link in Network Intrusion Detection: Taking AI/ML Research Efforts to Users

Dietz,

Mühlhauser,

Kögel

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

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The State of the Art in BGP Visualization Tools: A Mapping of Visualization Techniques to Cyberattack Types

Cited by 1 publication

References 67 publications

The Missing Link in Network Intrusion Detection: Taking AI/ML Research Efforts to Users

The Missing Link in Network Intrusion Detection: Taking AI/ML Research Efforts to Users

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