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Selection of effective machine learning algorithm and Bot-IoT attacks traffic identification for internet of things in smart city
Abstract
Abstract Identifying cyber attacks traffic is very important for the Internet of things (IoT) security in smart city. Recently, the research community in the field of IoT Security endeavor hard to build anomaly, intrusion and cyber attacks traffic identification model using Machine Learning (ML) algorithms for IoT security analysis. However, the critical and significant problem still not studied in depth that is how to select an effective ML algorithm when there are numbers of ML algorithms for cyber attacks detection system for IoT security. In this paper, we proposed a new framework model and a hybrid algorithm to solve this problem. Firstly BoT-IoT identification dataset is applied and its 44 effective features are selected from a number of features for the machine learning algorithm. Then five effective machine learning algorithm is selected for the identification of malicious and anomaly traffic identification and also select the most widely ML algorithm performance evaluation metrics. To find out which ML algorithm is effective and should be used to select for IoT anomaly and intrusion traffic identification, a bijective soft set approach and its algorithm is applied. Then we applied the proposed algorithm based on bijective soft set approach. Our experimental results show that the proposed model with the algorithm is effective for the selection ML algorithm out of numbers of ML algorithms.
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Metadata
- Authors
- Shafiq, M., Tian, Z., Sun, Y., Du, X., & Guizani, M.
- Venue
- Future Generation Computer Systems
- Publisher
- Elsevier BV
- Published
- 2020-06
- Volume
- 107
- Pages
- 433-442
- ISSN
- 0167-739X (ISSN-L)
- OpenAlex
- W3006526650
- Cited by
- 384
- References
- 54
- FWCI
- 31.71
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- Funders
- National Key research and Development Plan (2018YFB0803504)
- Guangdong Province Key Research and Development Plan (2019B010137004)
- National Natural Science Foundation of China (61871140)
- Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme
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