Secure and Safe IIoT Systems via Machine and Deep Learning Approaches Book Chapter

Lalos, AS, Kalogeras, AP, Koulamas, C et al. (2019). Secure and Safe IIoT Systems via Machine and Deep Learning Approaches . 443-470. 10.1007/978-3-030-25312-7_16

cited authors

  • Lalos, AS; Kalogeras, AP; Koulamas, C; Tselios, C; Alexakos, C; Serpanos, D


  • This chapter reviews security and engineering system safety challenges for Internet of Things (IoT) applications in industrial environments. On the one hand, security concerns arise from the expanding attack surface of long-running technical systems due to the increasing connectivity on all levels of the industrial automation pyramid. On the other hand, safety concerns magnify the consequences of traditional security attacks. Based on the thorough analysis of potential security and safety issues of IoT systems, the chapter surveys machine learning and deep learning (ML/DL) methods that can be applied to counter the security and safety threats that emerge in this context. In particular, the chapter explores how ML/DL methods can be leveraged in the engineering phase for designing more secure and safe IoT-enabled long-running technical systems. However, the peculiarities of IoT environments (e.g., resource-constrained devices with limited memory, energy, and computational capabilities) still represent a barrier to the adoption of these methods. Thus, this chapter also discusses the limitations of ML/DL methods for IoT security and how they might be overcome in future work by pursuing the suggested research directions.

publication date

  • January 1, 2019

Digital Object Identifier (DOI)

International Standard Book Number (ISBN) 13

start page

  • 443

end page

  • 470