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Evidence record 6281 · automatically gathered

Attribution-Driven Explainable Intrusion Detection with Encoder-Based Large Language Models

Software-Defined Networking (SDN) improves network flexibility but also increases the need for reliable and interpretable intrusion detection. Large Language Models (LLMs) have recently been explored for cybersecurity tasks due to their strong representation learning capabilities; however, their lack of transparency limits their practical adoption in security-critical environments. Understanding how LLMs make decisions is therefore essential. This paper presents an attribution-driven analysis of

Record details

Published: 7 April 2026
Source: arXiv
Category: Research
Topics: Transparency · Environment
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (7 April 2026), “Attribution-Driven Explainable Intrusion Detection with Encoder-Based Large Language Models,” evidence record 6281, https://ethics.ai/record/6281 (originally published by arXiv).

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