Challenges and new approaches in the applications of Internet of Things, image processing and machine learning

Abstract

In the current era of the fourth industrial revolution, the digital world has a lot of data, such as IoT data, cyber security data, mobile phone data, business data, social media data, health data, etc. To intelligently analyze this data and develop the corresponding intelligent and automated programs, the knowledge of artificial intelligence, especially machine learning, is the key. There are different types of machine learning algorithms such as supervised, unsupervised, semi-supervised and reinforcement learning in the area. Deep learning, which is part of a broader family of machine learning methods, can intelligently analyze large-scale data. Nodes participating in IoT networks are usually resource-constrained, which makes them attractive targets for cyber attacks. The unique characteristics of IoT nodes make existing solutions insufficient to cover the entire security spectrum of IoT networks. Machine learning and deep learning techniques, which can provide embedded intelligence in IoT devices and networks, can be used to tackle various security problems. In this article, we systematically review the security requirements, attack vectors, and current security solutions for IoT networks. We then highlight the gaps in these security solutions that call for ML approaches. We also discuss in detail the existing ML solutions to address various security problems in IoT networks.