Forensic-Oriented Intrusion Detection Using Synthetic Network Traffic Data and Explainable Artificial Intelligence
arXiv:2607.00763v1 Announce Type: cross Abstract: 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, and explainability within a single pipeline govern
The increasing sophistication of cyber threats and the growing demand for accountability in AI-driven systems are driving the development of forensic-oriented intrusion detection solutions.
This development addresses a critical gap in cybersecurity by integrating explainable AI with synthetic data for court-defensible forensic analysis, enhancing trust and legal validity in digital investigations.
The ability to generate traceable, reproducible, and court-defensible insights from intrusion detection systems marks a significant shift from opaque, unexplainable AI outputs in security.
- · Cybersecurity industry
- · Law enforcement
- · Organizations handling sensitive data
- · AI developers focused on transparency
- · Cybercriminals
- · Traditional, less transparent intrusion detection systems
Improved detection and prosecution of cybercrimes due to reliable, explainable evidence.
Increased adoption of explainable AI across other critical security and analytical domains requiring high assurance.
A potential shift in legal standards for digital evidence, placing higher emphasis on AI explainability and data traceability.
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Read at arXiv cs.LG