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Proactive Threat Hunting in Critical Infrastructure Protection through Hybrid Machine Learning Algorithm Application.

Ali Shan¹, Seunghwan Myeong²

¹Center of Security Convergence & eGovernance, Inha University, Incheon 22212, Republic of Korea.

Sensors (Basel, Switzerland)

|August 10, 2024

Summary

Machine learning models enhance cyber-security for critical infrastructure. AdaBoost and Random Forest show high accuracy in proactive threat hunting, improving detection and reducing false positives.

Keywords:

cyber-attacks cyber-security machine learning threats

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Area of Science:

Cyber-security
Machine Learning
Critical Infrastructure Protection

Background:

Global cyber-security threats are escalating, particularly targeting critical infrastructure.
Conventional security measures are inadequate for proactive threat hunting.
Need for advanced methods to automate threat detection and improve response.

Purpose of the Study:

To evaluate the efficacy of various machine learning models for proactive threat hunting in cyber-security.
To compare the performance of Random Forest (RF), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), AdaBoost, and hybrid models.
To identify the most efficient model for detecting threats in critical infrastructure environments.

Main Methods:

Implementation of multiple machine learning models: RF, SVM, MLP, AdaBoost, and hybrid approaches.
Models were deployed on approach devices, access points, and principal servers for threat detection.
Performance assessment based on metrics like ROC area, accuracy, threat detection count, and false positives.

Main Results:

AdaBoost model demonstrated highest efficiency with 0.98 ROC area and 95.7% accuracy, detecting 146 threats.
Random Forest model achieved 0.98 ROC area and 95% accuracy, identifying 132 threats with fewer false positives.
Hybrid model showed promise (0.89 ROC area, 94.9% accuracy) but requires further optimization for false positive reduction.

Conclusions:

Machine learning significantly enhances cyber-security, especially for critical infrastructure.
AdaBoost and Random Forest are highly effective for proactive threat hunting, improving detection rates and accuracy.
Continuous learning capabilities of advanced ML techniques ensure adaptability to evolving cyber threats.