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“…These models learn the discriminating features of benign traffic and malicious traffic using different architectures such as Random Forest (RF) [ 18 ], Support Vector Machine (SVM) [ 19 ], Deep Neural Network (DNN) [ 20 ], Recurrent Neural Network (RNN) [ 21 ], Long Short-Term Memory (LSTM) [ 22 ] and Gated Recurrent Unit (GRU) [ 23 ]. For an in-depth understanding, comprehensive reviews and surveys on the application of ML and DL in intrusion detection are presented in [ 24 , 25 , 26 , 27 , 28 , 29 ].…”
Section: Introductionmentioning
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“…These models learn the discriminating features of benign traffic and malicious traffic using different architectures such as Random Forest (RF) [ 18 ], Support Vector Machine (SVM) [ 19 ], Deep Neural Network (DNN) [ 20 ], Recurrent Neural Network (RNN) [ 21 ], Long Short-Term Memory (LSTM) [ 22 ] and Gated Recurrent Unit (GRU) [ 23 ]. For an in-depth understanding, comprehensive reviews and surveys on the application of ML and DL in intrusion detection are presented in [ 24 , 25 , 26 , 27 , 28 , 29 ].…”
Section: Introductionmentioning
“…The work in [59] analyzed the research landscape about Deep Learning (DL) approaches applied to IoT security. The study is based on three main research questions, namely, the security aspects involved, the used DL network architectures and the engaged datasets.…”
Section: Literature Surveymentioning
“…The majority of the literature includes high-level guidelines and baseline requirements for IoT security for IoT vendors in 2020, discussion of security for IoT with AI (artificial intelligence) [29,30], or with the cloud [31], and the user's quality of experience (QoE) [32,33] in 2021. Our extensive literature search failed to produce any literature concerned with benchmarks or suggestions for secure development for IoT vendors.…”
Section: Background and Necessity For This Studymentioning