Joint IoT/ML Platforms for Smart Societies and Environments: A Review on Multimodal Information-Based Learning for Safety and Security

Abstract

The application of the Internet of Things (IoT) is highly expected to have comprehensive economic, business, and societal implications for our smart lives; indeed, IoT technologies play an essential role in creating a variety of smart applications that improve the nature and well-being of life in the real world. Consequently, the interconnected nature of IoT systems and the variety of components of their implementation have given rise to new security concerns. Cyber-attacks and threats in the IoT ecosystem significantly impact the development of new intelligent applications. Moreover, the IoT ecosystem suffers from inheriting vulnerabilities that make its devices inoperable to benefit from instigating security techniques such as authentication, access control, encryption, and network security. Recently, great advances have been achieved in the field of Machine Intelligence (MI), Deep Learning (DL), and Machine Learning (ML), which have been applied to many important applications. ML and DL are regarded as efficient data exploration techniques for discovering “normal” and “abnormal” IoT component and device behavior inside the IoT ecosystem. Therefore, ML/DL approaches are required to convert the security of IoT systems from providing safe Device-to-Device (D2D) communication to providing security-based intelligence systems. The proposed work examines ML/DL technologies that may be utilized to provide superior security solutions for IoT devices. The potential security risks associated with the IoT are discussed, including pre-existing and newly emerging threats. Furthermore, the benefits and challenges of DL and ML techniques are examined to enhance IoT security.