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Feature Selection Using Information Gain for Improved Structural-Based Alert Correlation.

Taqwa Ahmed Alhaj¹, Maheyzah Md Siraj¹, Anazida Zainal¹

¹Information Assurance and Security Research Group, Faculty of Computing, Universiti Teknologi Malaysia, UTM, Johor Bahru, Johor, Malaysia.

|November 29, 2016

Summary

This summary is machine-generated.

This study introduces a two-tier feature selection method to improve intrusion detection alert correlation. The approach enhances attack step identification and clustering accuracy by selecting significant alert features.

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Area of Science:

Cybersecurity
Intrusion Detection Systems
Machine Learning

Background:

Structurally based alert correlation groups intrusion detection alerts by feature similarity to identify attack steps.
Manual feature selection in prior research led to inaccurate attack step identification and inconsistent clustering performance.
Existing systems struggle with large, noisy datasets, making alert analysis difficult and error-prone.

Purpose of the Study:

To enhance structural-based alert correlation by selecting accurate and significant alert features.
To improve the representation of attack steps for more effective analysis.
To address limitations of manual feature selection and noisy data in intrusion detection.

Main Methods:

A novel two-tier feature selection method is proposed.
The first tier ranks features using information gain entropy.
The second tier incorporates additional features with superior discriminative ability.

Main Results:

The proposed method effectively selects significant features for alert correlation.
Enhanced feature selection leads to improved clustering accuracy.
Performance was validated using the DARPA intrusion detection dataset.

Conclusions:

The two-tier feature selection method significantly enhances structural-based alert correlation.
Accurate feature selection is crucial for improving intrusion detection and attack step identification.
The findings contribute to more robust and accurate alert correlation systems.