An intrusion detection system on the internet of things using deep learning and multi-objective enhanced gorilla troops optimizer
Yükleniyor...
Dosyalar
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
In recent years, developed Intrusion Detection Systems (IDSs) perform a vital function in improving security and anomaly detection. The effectiveness of deep learning-based methods has been proven in extracting better features and more accurate classification than other methods. In this paper, a feature extraction with convolutional neural network on Internet of Things (IoT) called FECNNIoT is designed and implemented to better detect anomalies on the IoT. Also, a binary multi-objective enhance of the Gorilla troops optimizer called BMEGTO is developed for effective feature selection. Finally, the combination of FECNNIoT and BMEGTO and KNN algorithm-based classification technique has led to the presentation of a hybrid method called CNN-BMEGTO-KNN. In the next step, the proposed model is implemented on two benchmark data sets, NSL-KDD and TON-IoT and tested regarding the accuracy, precision, recall, and F1-score criteria. The proposed CNN-BMEGTO-KNN model has reached 99.99% and 99.86% accuracy on TON-IoT and NSL-KDD datasets, respectively. In addition, the proposed BMEGTO method can identify about 27% and 25% of the effective features of the NSL-KDD and TON-IoT datasets, respectively.
Açıklama
Anahtar Kelimeler
Intrusion Detection, Internet of Things, Convolutional Neural Network, Multi-Objective, Gorilla Troops Optimizer
Kaynak
Journal of bionic engineering
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
21
Sayı
5
Künye
Asgharzadeh, H., Ghaffari, A., Masdari, M., & Gharehchopogh, F. S. (2024). An Intrusion Detection System on The Internet of Things Using Deep Learning and Multi-objective Enhanced Gorilla Troops Optimizer. Journal of Bionic Engineering, 21(5), 2658-2684.
