DERİN ÖĞRENME TABANLI AĞ SALDIRI TESPİTİ

Year 2024, , 517 - 530, 26.09.2024

Güneş Harman , Emine Cengiz

https://doi.org/10.21923/jesd.1417622

Abstract

Teknolojik yeniliklerin amansız ilerleyişinin doğrudan bir sonucu olarak, İnternet kullanımı modern insanın yaşamı için kaçınılmaz bir koşul haline gelmiştir. İnternet, bilgi ürünlerinin yararlı ya da yararsız olabileceği durumların hem miktarını hem de çeşitliliğini artırmıştır. Farklı sistemlerin ve kullanıcıların sayısı arttıkça, bu sistemleri istismar etmenin farklı yollarının sayısının da artması şaşırtıcı değildir. Bu çeşitlilik ve büyümeyle birlikte bir güvenlik sorunu ortaya çıkmıştır. Çeşitlilik ve miktar artışı yeni sistem zayıflıklarını ve dolayısıyla yeni saldırı stratejilerini beraberinde getirmektedir. Bu soruna çözüm olarak hem iç hem de dış saldırıları tespit etmek için yöntemler önerilmektedir. Bu araştırmanın amacı, NF-BoT-IoT veri setindeki dengesiz sınıf dağılımına yönelik saldırı olarak da bilinen izinsiz girişleri tespit etmek için bir Evrişimsel Sinir Ağı kullanılmış, Sentetik Azınlık Örnekleme Tekniği, Rastgele Aşırı Örnekleme ve Rastgele Alt Örnekleme yöntemleri kullanılmıştır. Sınıflandırma modellerinin performansını değerlendirmek ve geliştirilen modeli eğitmek için veri setini bölme stratejilerinden biri olan K-Fold Cross Validation kullanılmıştır. Modelin performansı doğruluk, kesinlik, duyarlılık ve F1-skor performans kriterleri kullanılarak değerlendirilmiştir.

Keywords

Derin Öğrenme, Evrişimsel Sinir Ağları, Saldırı Tespiti, NF-BoT-IoT, Siber Güvenlik

DEEP LEARNING BASED NETWORK INTRUSION DETECTION

Abstract

As a direct consequence of the unrelenting march of technological innovation, the use of the Internet has become an unavoidable condition for the life of modern humans. The Internet has increased both the quantity and range of situations in which information products can be useful or non-useful. It’s no surprise that as the number of different systems and users has grown, so have the number of different ways to exploit those systems. A security issue has arisen with such diversity and growth. Its diversity and increase in quantity introduce new system weaknesses and thus new attack strategies. Methods for detecting both internal and external attacks are suggested as a solution to this issue. The purpose of this research, a Convolutional Neural Network was utilized to identify intrusions, also known as attacks for the imbalanced class distribution in the NF-BoT-IoT data set, Synthetic Minority Over Sampling Technique, Random Over Sampling and Random Under Sampling methods were used. K-Fold Cross Validation, one of the strategies for splitting the data set, was utilized to evaluate the performance of classification models and to train the developed model. The model’s performance was evaluated using the accuracy, precision, recall, and F1-score performance criteria.

Keywords

Deep Learning, Convolutional Neural Network, Intrusion Detection, NF-BoT-IoT, Cyber Security

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