Attack Detection in Software-Defined Network Technology Data Using A Transformer-Based Convolutional Neural Network Model with An Archimedean Optimization Algorithm

Year 2022, Volume: 34 Issue: 1, 341 - 349, 20.03.2022

Mesut Toğaçar

https://doi.org/10.35234/fumbd.1026610

Cited By: 1

Abstract

Recently, there has been an increase in the number of people who do their daily work with the help of technological devices. During this time, the concept of the Internet of Things has emerged, where smart devices can communicate with each other. While all these developments make people's lives easier, on the other hand, they make it necessary to develop systems that can ensure secure transmission of data. In this study, a hybrid approach based on artificial intelligence was developed to detect attacks on software-defined network data. The dataset includes normal, denial of service, brute force, cross-site scripting and SQL injection network attacks. Archimedes optimization algorithm has been used as a preprocessing step in the proposed approach. Thanks to Archimedes optimization algorithm, the selection of efficient features in the dataset was done. Then, the dataset was trained using the transformer-based convolutional neural network model. The softmax method was used as a classifier for detecting normal or attack network traffic. The overall accuracy achieved in the experimental analysis of this study was 98.94%.

Keywords

Deep learning, decision support system, internet of things, data security, software defined network

Arşimet Optimizasyon Algoritması ile Trafo Tabanlı Evrişimsel Sinir Ağı Modelini Kullanarak Yazılım Tanımlı Ağ Teknolojisi Verilerinde Saldırı Tespiti

Abstract

Son zamanlarda insanların teknoloji cihazları kullanarak günlük işlerini idame etmesindeki oran artmıştır. Akıllı cihazların birbirleriyle iletişim sağlayabildiği şu zamanda nesnelerin interneti kavramı ortaya çıkmıştır. Bütün bu gelişmeler insan hayatını daha da kolaylaştırırken diğer taraftan verilerin iletimini güvenli bir şekilde aktarılmasını sağlayabilen sistemlerin tasarlanmasını zorunlu hale getirmiştir. Bu çalışmada yazılım tanımlı ağ verilerinde saldırı tespitini gerçekleştirebilen yapay zekâ tabanlı hibrit bir yaklaşım geliştirilmiştir. Veri kümesi normal, dağıtılmış hizmet reddi, kaba kuvvet saldırıları, siteler arası betik çalıştırma ve SQL enjeksiyon ağ saldırı türlerini içermektedir. Önerilen yaklaşımda ön işlem adımı olarak Arşimet optimizasyon algoritması kullanılmıştır. Arşimet optimizasyon algoritması sayesinde veri kümesindeki verimli özelliklerin seçimi gerçekleştirilmiştir. Ardından trafo tabanlı evrişimsel sinir ağı modeli kullanılarak veri kümesi eğitilmiştir. Ağ trafiğinin normal veya saldırı tespitinde softmax yöntemi sınıflandırıcı olarak kullanılmıştır. Bu çalışmanın deneysel analizinde %98,94 genel doğruluk başarısı elde edilmiştir.

Keywords

Derin öğrenme, karar destek sistemi, nesnelerin interneti, veri güvenliği, yazılım tabanlı ağ

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