Investigation of performance of machine learning methods for cyber-attack detection

Yıl 2021, , 782 - 791, 04.07.2021

Fatih Demir

https://doi.org/10.25092/baunfbed.876338

Cited By: 2

Öz

The increase in internet based devices gets security problems in cyber environment. Malwares can disturb the functioning of systems and compromise data privacy due to vulnerabilities in systems. Intrusion Detection Systems (STS) are improved to determine and classify attacks. Artificial intelligence-based methods are used more frequently to improve STS systems. In this study, literature studies using ISCX-2012 data set, which was widely used in the development of STS systems, were reviewed. Besides, by using this data set, cyber-attacks were detected with 100% accuracy with a powerful machine learning-based approach. Feature and hyperparameter selection algorithms are used to increase the classification accuracy performance of the proposed method. These machine learning approaches are thought to be useful for developing artificial intelligence-based STS systems.

Anahtar Kelimeler

Cyber-attack, intrusion detection systems, machine learning

Siber saldırı tespiti için makine öğrenmesi yöntemlerinin performanslarının incelenmesi

Öz

İnternet tabanlı cihazların kullanımının artması, siber ortamda güvenlik sorunlarına yol açmaktadır. Kötü amaçlı yazılımlar, sistemlerin işleyişini bozabilir ve sistemlerdeki güvenlik açıkları nedeniyle veri gizliliğini tehlikeye atabilir. Siber saldırıları belirlemek ve sınıflandırmak için Saldırı Tespit Sistemleri (STS) geliştirilmektedir. Yapay zeka tabanlı yöntemler, STS sistemlerini iyileştirmek için daha sık kullanılmaktadır. Bu çalışmada, STS sistemlerinin geliştirilmesinde yaygın olarak kullanılan ISCX-2012 veri setinin kullanıldığı literatür çalışmaları gözden geçirilmiştir. Ayrıca bu veri seti kullanılarak makine öğrenmesi tabanlı güçlü bir yaklaşım ile siber saldırılar %100 doğrulukla tespit edilmiştir. Önerilen yöntemin sınıflandırma doğruluğu performansını artırmak için öznitelik ve hiperparametre seçme algoritmaları kullanılmıştır. Önerilen yaklaşımın yapay zeka temelli STS sistemleri geliştirmek için faydalı olacağı düşünülmektedir.

Anahtar Kelimeler

Siber Saldırı, Saldırı Tespit Sistemleri, Makine Öğrenmesi

Kaynakça

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