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Towards Machine Learning Based Intrusion Detection in IoT Networks

Computers, Materials & Continua 2021年第11期69卷 1801-1821页

作者： nahida islam.fahiba farhin Ishrat Sultana M.Shamim Kaiser Md.Sazzadur Rahman Mufti Mahmud A.S.M.Sanwar Hosen Gi Hwan Cho Institute of Information Technology Jahangirnagar UniversityDhakaBangladesh Department of Computer Science Nottingham Trent UniversityNottinghamUK Division of Computer Science and Engineering Jeonbuk National UniversityJeonju54896Korea

The Internet of Things(IoT)integrates billions of self-organized and heterogeneous smart nodes that communicate with each other without human *** recent years,IoT based systems have been used in improving the experience in many applications including healthcare,agriculture,supply chain,education,transportation and traffic monitoring,utility services ***,node heterogeneity raised security concern which is one of the most complicated issues on the *** security measures,including encryption,access control,and authentication for the IoT devices are ineffective in achieving *** this paper,we identified various types of IoT threats and shallow(such as decision tree(DT),random forest(RF),support vector machine(SVM))as well as deep machine learning(deep neural network(DNN),deep belief network(DBN),long short-term memory(LSTM),stacked LSTM,bidirectional LSTM(Bi-LSTM))based intrusion detection systems(IDS)in the IoT environment have been *** performance of these models has been evaluated using five benchmark datasets such as NSL-KDD,IoTDevNet,DS2OS,IoTID20,and IoT Botnet *** various performance metrics such as Accuracy,Precision,Recall,F1-score were used to evaluate the performance of shallow/deep machine learning based *** has been found that deep machine learning IDS outperforms shallow machine learning in detecting IoT attacks.

关键词： IoT shallow machine learning deep learning data science IDS

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