首页|An Intrusion Detection System on The Internet of Things Using Deep Learning and Multi-objective Enhanced Gorilla Troops Optimizer

An Intrusion Detection System on The Internet of Things Using Deep Learning and Multi-objective Enhanced Gorilla Troops Optimizer

扫码查看
原文链接
  • 万方数据
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 Fl-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.

Intrusion detectionInternet of ThingsConvolutional neural networkMulti-objectiveGorilla troops optimizer

Hossein Asgharzadeh、Ali Ghaffari、Mohammad Masdari、Farhad Soleimanian Gharehchopogh

展开 >

Department of Computer Engineering,Urmia Branch,Islamic Azad University,Urmia 63896-57169,Iran

Department of Computer Engineering,Technical and Vocational University(TUV),Tehran,Iran

Department of Computer Engineering,Tabriz Branch,Islamic Azad University,Tabriz 5157944533,Iran

Department of Computer Engineering,Faculty of Engineering and Natural Science,Istinye University,34010 Istanbul,Turkey

Department of Computer Engineering,Khazar University,AZ1096 Baku,Azerbaijan

展开 >

2024

10.1007/s42235-024-00575-7

仿生工程学报(英文版)
吉林大学

仿生工程学报(英文版)

CSTPCDEI
影响因子：0.837
ISSN：1672-6529
年,卷(期)：2024.21(5)