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This article provides an in-depth analysis of a cyber attack detection project transitioning from academic research to production environments, demonstrating how to select three core models (CNN, LSTM, and LightGBM) through systematic experiments and offer optimal solutions for different scenarios.
Section 01
This article provides an in-depth analysis of a cyber attack detection project transitioning from academic research to production environments, demonstrating how to select three core models (CNN, LSTM, and LightGBM) through systematic experiments and offer optimal solutions for different scenarios.
Section 02
Section 03
In today's increasingly digital world, the frequency and complexity of cyber attacks are on the rise. Traditional rule-based Intrusion Detection Systems (IDS) often struggle to handle new attack methods, while machine learning technology brings new possibilities to this field.
This project was developed by Nefise Turgut, a computer engineering student, as her graduation project. Initially, it was a comprehensive research project covering 15 different models. Through systematic experimental comparisons, three core models most suitable for production environments were finally selected, forming a complete cyber attack detection solution.
Section 04
The project uses three representative public datasets, covering the full spectrum from classic benchmark tests to modern real attack scenarios:
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As the most classic benchmark dataset in the field of intrusion detection, it contains about 500,000 records and 41 features. Although it is old, it is still a standard test set for verifying the basic capabilities of algorithms.
Section 06
A modern dataset released by the Canadian Institute for Cybersecurity, containing about 1 million records and 80 features, covering current mainstream cyber attack types and better reflecting the complexity of real network environments.
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A more balanced and realistic dataset with about 257,000 records and 42 features, achieving a good balance between academic research and industrial applications.
Section 08
After a comprehensive evaluation of 15 experimental models, the project finally identified three core models, each targeting different application scenarios:
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