Forensic-Oriented Intrusion Detection Using Synthetic Network Traffic Data and Explainable Artificial Intelligence
Digital forensic investigations of network intrusions require analytical outputs that are traceable, reproducible, and court-defensible - requirements existing machine learning pipelines do not satisfy, since they treat original evidence as training data and produce opaque classifications without instance-level justification. This paper presents a forensic-oriented intrusion detection framework resolving both problems simultaneously, integrating synthetic data generation, binary classification,
Record details
Published: 1 July 2026
Source: arXiv cs.CR (AI security)
Category: Research
Topics: Transparency · Finance, VC & PE
Retrieved: 14 July 2026
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ethics.ai (1 July 2026), “Forensic-Oriented Intrusion Detection Using Synthetic Network Traffic Data and Explainable Artificial Intelligence,” evidence record 3040, https://ethics.ai/record/3040 (originally published by arXiv cs.CR (AI security)).
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