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Updated: Apr 7, 2026

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

Published on: January 9, 2019

Reliable evaluation for the AI-enabled intrusion detection system from data perspective.

Hui-Juan Zhang¹, Kai Yang¹, Peng Ran¹

¹Research Institute of Safety Technology, Research Institute of China Mobile, Beijing, China.

|October 31, 2025

Summary

This summary is machine-generated.

This study introduces a multi-indicator comprehensive evaluation method (MICEM) to assess intrusion detection system (IDS) data quality. MICEM addresses data tampering and corruption, ensuring reliable artificial intelligence (AI) decision-making for enhanced cybersecurity.

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Area of Science:

Cybersecurity
Artificial Intelligence
Data Science

Background:

Intrusion detection systems (IDS) are crucial for cybersecurity.
Existing AI models for IDS overlook the impact of poor data quality.
Data integrity issues like tampering and poisoning erode trust in AI-driven IDS.

Purpose of the Study:

To propose a multi-indicator comprehensive evaluation method (MICEM) for assessing intrusion detection data quality.
To enhance the reliability and usability of AI-enabled IDS by addressing data quality concerns.
To provide a data-centric approach for validating AI decision-making in cybersecurity.

Main Methods:

Established several evaluation indicators to analyze potential risks in intrusion detection data.
Developed specific quantitative methods for assessing data quality dimensions.
Conducted a comprehensive evaluation using MICEM to determine overall data quality.

Main Results:

The proposed MICEM effectively evaluates intrusion detection data quality.
Quantitative indicators were developed to identify data risks.
Comprehensive evaluation guarantees the reliability of AI-enabled IDS.

Conclusions:

MICEM ensures the reliability of AI decision-making in IDS by focusing on data quality.
The method addresses data integrity issues, mitigating trust crises in AI models.
Effectiveness and practicality were validated on benchmark and real-world datasets.