Machine Learning Techniques for Security of Internet of Things (IoT) and Fog Computing Systems

The Internet of Things (IoT) is a network of interconnected computing devices having the ability to transfer data over a network, without needing human interaction. Software and communication technologies have seen a tremendous amount of growth in the past couple of decades, and this has resulted in a significant increase in IoT devices. Today, in 2017, IoT devices are ubiquitous and have pervaded almost every sphere of our lives ushering an era of 'smart things.' The explosive growth has resulted in much security and privacy concerns. The other concern is the scalability issue with the primary model of analyzing IoT data in the cloud. A cloud-only model leads to network congestion, data bottleneck and slower reaction times to security issues. By selectively moving computation and storage closer to the network edge, fog computing provides an effective solution for these issues. This paper presents a holistic picture of IoT, its growth, fog computing, and Machine Learning (ML) techniques for securing IoT devices and fog computing systems. It surveys ML techniques for detecting abnormalities and attacks, illustrates solutions to the data growth in IoT, and dives into the security issues concerning fog computing. It also discusses future research directions in this important topic.