Using Machine Learning for Protecting the Security and Privacy of Internet of Things (IoT) Systems
Melody Moh, Robinson Raju · 2019
This chapter focuses on the type of data that is transmitted and the security and privacy implications of this. As Internet of Things (IoT) usage grows, the amount of data uploaded to the cloud by IoT systems far exceeds that done by users. A distributed denial of service (DDoS) attack is one that uses multiple network resources as the source of the attack. Depending on the nature of the learning, machine-learning algorithms can be categorized as: supervised learning, unsupervised learning, and reinforcement learning. The chapter summarizes the machine-learning algorithms that could be used for various use cases for different domains. There are multiple issues with a cloud-only architecture where data from IoT devices make it to the cloud to be processed and analyzed. Fog computing solves this by selectively moving compute, storage, and decision-making closer to the network edge where data are being generated.