{
  "id": 3040,
  "url": "https://arxiv.org/abs/2607.00763v1",
  "title": "Forensic-Oriented Intrusion Detection Using Synthetic Network Traffic Data and Explainable Artificial Intelligence",
  "summary": "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, ",
  "authors": "Jose Luis Vela Alonso, Carmen Pellicer",
  "category": "research",
  "topics": "transparency,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-01T10:47:19.000Z",
  "fetched_at": "2026-07-14T16:11:46.979Z",
  "source_slug": "x-arxiv-cs-cr-ai-security",
  "source_name": "arXiv cs.CR (AI security)",
  "source_homepage": "https://arxiv.org/list/cs.CR/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/3040",
  "original_url": "https://arxiv.org/abs/2607.00763v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}