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IoT Network Intrusion Detection Using Machine Learning Techniques

Bauyrzhan Omarov, Omirlan Auelbekov, Tursynay Koishiyeva, Ruslan Sadybekov, Yerkebulan Uxikbayev, Aizhan Bazarbayeva
Abstract: The approaches that are directly suited for the implementation of the process of detecting anomalies in data are discussed in this article, and a comparison of the most widely used ones is carried out using the following criteria: the speed of implementation and the model's data needs. As a consequence, it can be stated that determining the quality of a model at the emission detection stage is challenging, and as a result, it must be examined at the final step (through subsequent analysis of the identified anomalies of various methods). Combinations of various strategies are the most often utilized and accurate procedures, as shown by experience.
Keywords: Support vector machines / Measurement / Deep learning / Machine learning algorithms / Neural networks / Network intrusion detection / Data models

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