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Journals / ACADEMIC PLATFORM-JOURNAL OF ENGINEERING AND SCIENCE / 2019 / Cilt: 7 - Sayı: 2

The Performance Analysis of Data Mining Algorithms for Anomaly Detection

Anormal Trafik Tespiti için Veri Madenciliği Algoritmalarının Performans Analizi

Sezgin Kaçar
Ünal Çavuşoğlu
Pages
205–216
Publication Date
2022-10-11
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As a result of the tests, performance evaluation of different data mining and feature selection algorithms is presented.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, performance evaluation of data mining algorithms used to detect harmful computer network traffic is realized.
📊
Key Findings & Data:Firstly, feature selection process is performed from the NSL-KDD dataset with different feature selection algorithms.
💡
Conclusion & Impact:As a result of the tests, performance evaluation of different data mining and feature selection algorithms is presented.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

In this study, performance evaluation of data mining algorithms used to detect harmful computer network traffic is realized. Firstly, feature selection process is performed from the NSL-KDD dataset with different feature selection algorithms. As a result of this process, different datasets are created by combining different attributes. Performans tests are conducted for the detection of anormal traffic using different data mining algorithms on these data sets. As a result of the tests, performance evaluation of different data mining and feature selection algorithms is presented.

Özet

Bu çalışmada, bilgisayar ağ trafiğinde tehlike oluşturabilecek zararlı trafiğin tespit edilmesi için kullanılan veri madenciliği algoritmalarının performans değerlendirilmesi gerçekleştirilmiştir. İlk olarak, farklı özellik çıkarım algoritmaları ile NSL-KDD veri setinden nitelik çıkarım işlemi gerçekleştirilmiştir. Bu işlem sonucunda farklı niteliklerden oluşan yeni veri setleri oluşturulmuştur. Bu veri setleri üzerinde farklı veri madenciliği algoritmaları kullanılarak anormal trafik tespiti için testler yapılmıştır. Yapılan testler sonucunda, farklı veri madenciliği ve özellik çıkarım algoritmalarının performans değerlendirmesi sunulmuştur.

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Cite This Article

Kaçar, S., Çavuşoğlu, Ü. (2022). The Performance Analysis of Data Mining Algorithms for Anomaly Detection. ACADEMIC PLATFORM-JOURNAL OF ENGINEERING AND SCIENCE, Cilt: 7 - Sayı: 2, 205–216. https://ajindex.com/articles/427280

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