Enhancing Anomaly-Based Intrusion Detection Systems with Process Mining

2026-04-20 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Expert, medium

Summary

A new method enhances anomaly-based Intrusion Detection Systems (IDSs) by integrating process mining techniques to provide process-based alarm severity ratings and explanations for alerts. Deep learning-based IDSs, while effective, often lack trustworthiness due to their black-box nature. This proposed approach addresses this limitation by offering packet-level sequencing analysis, which helps prioritize critical alerts and maintain visibility into network behavior. The method minimizes disruption by allowing misclassified benign traffic to pass. Evaluated on the USB-IDS-TC dataset, which contains anomalous traffic from Slowloris DoS attacks, the system successfully discriminates between low- to very-high-severity alarms. It achieves up to 99.94% recall and 99.99% precision, effectively discarding false positives while assigning varying degrees of severity to true positives.

Key takeaway

For research scientists developing or deploying anomaly-based IDSs, you should consider integrating process mining techniques to improve the explainability and trustworthiness of your systems. This approach allows for granular severity ratings and process-based explanations, which can significantly enhance alert prioritization and reduce false positives, especially in environments susceptible to sophisticated attacks like Slowloris DoS.

Key insights

Process mining enhances IDS trustworthiness by providing severity-rated, process-based explanations for network intrusion alerts.

Principles

• Trustworthiness requires process-based explanations.
• Prioritize critical alerts with severity ratings.
• Maintain network visibility while minimizing disruption.

Method

The method applies process mining to network packet sequencing to generate severity ratings and explanations for IDS alarms, distinguishing true positives by severity and discarding false positives.

In practice

• Apply process mining to network traffic logs.
• Use packet-level sequencing for alert context.
• Integrate severity ratings into IDS dashboards.

Topics

• Anomaly-based IDS
• Process Mining
• Explainable AI
• Network Intrusion Detection
• DoS Attacks

Best for: Research Scientist, AI Scientist, AI Security Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.