2020
DOI: 10.1007/s11831-020-09496-0
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A Review on Machine Learning and Deep Learning Perspectives of IDS for IoT: Recent Updates, Security Issues, and Challenges

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Cited by 208 publications
(77 citation statements)
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“…Further, the readers can refer these literature [31], [32], [33] to understand the challenges, solutions and future directions in applying deep learning approaches for IDS within IoT.…”
Section: A Applied For Securing Iot Networkmentioning
confidence: 99%
“…Further, the readers can refer these literature [31], [32], [33] to understand the challenges, solutions and future directions in applying deep learning approaches for IDS within IoT.…”
Section: A Applied For Securing Iot Networkmentioning
confidence: 99%
“…Here, the algorithms are able to learn representations to predict cases that are not known, from labelled inputted data. Examples of supervised machine learning algorithms include SVMs for solving issues regarding classification and RFs for solving both regression and classification issues [64,70]. SVMs are extensively used in IDS-based research as a result of their powerful classifying and their computing practicality.…”
Section: Supervised Learningmentioning
confidence: 99%
“…In the unsupervised learning scheme, representations as well as the structure from inputted data that are not labelled are learned by the algorithms. The aim of this algorithm is the modeling of foundational data structures for the prediction of data that are not known [64,70]. Some examples of these unsupervised learning algorithms include techniques used in reducing features such as clustering techniques, PCAs, and Self-Organizational Maps (SOMs).…”
Section: Unsupervised Learningmentioning
confidence: 99%
“…While the IoT will make life easier for humans, the aspect of data protection is of great concern (Thakkar & Lohiya, 2021;Mahboub, Ahmed & Saeed, 2021). IoT platform has been a popular target for cybercriminals as it faces significant risks.…”
Section: Research Areamentioning
confidence: 99%