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

作　　者：Hossein Asgharzadeh Ali Ghaffari Mohammad Masdari Farhad Soleimanian Gharehchopogh 

机构地区：[1]Department of Computer Engineering,Urmia Branch,Islamic Azad University,Urmia,63896-57169,Iran [2]Department of Computer Engineering,Technical and Vocational University(TUV),Tehran,Iran [3]Department of Computer Engineering,Tabriz Branch,Islamic Azad University,Tabriz,5157944533,Iran [4]Department of Computer Engineering,Faculty of Engineering and Natural Science,Istinye University,34010,Istanbul,Turkey [5]Department of Computer Engineering,Khazar University,AZ1096,Baku,Azerbaijan

出　　处：《Journal of Bionic Engineering》2024年第5期2658-2684,共27页仿生工程学报(英文版)

摘　　要：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.

关 键 词：Intrusion detection Internet of Things-Convolutional neural network MULTI-OBJECTIVE Gorilla troops optimizer 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]