Book Image

Practical Industrial Internet of Things Security

By : Sravani Bhattacharjee
Book Image

Practical Industrial Internet of Things Security

By: Sravani Bhattacharjee

Overview of this book

Securing connected industries and autonomous systems is of primary concern to the Industrial Internet of Things (IIoT) community. Unlike cybersecurity, cyber-physical security directly ties to system reliability as well as human and environmental safety. This hands-on guide begins by establishing the foundational concepts of IIoT security with the help of real-world case studies, threat models, and reference architectures. You’ll work with practical tools to design risk-based security controls for industrial use cases and gain practical knowledge of multi-layered defense techniques, including identity and access management (IAM), endpoint security, and communication infrastructure. You’ll also understand how to secure IIoT lifecycle processes, standardization, and governance. In the concluding chapters, you’ll explore the design and implementation of resilient connected systems with emerging technologies such as blockchain, artificial intelligence, and machine learning. By the end of this book, you’ll be equipped with the all the knowledge required to design industry-standard IoT systems confidently.
Table of Contents (22 chapters)
Title Page
Copyright and Credits
Dedication
Packt Upsell
Foreword
Contributors
Disclaimer
Preface
I
I
Index

Machine learning enabled endpoint security


Cybersecurity countermeasures have traditionally been reactive; in other words, the vaccine comes only after the virus has infected the system. The countermeasure typically follows the evaluation and remedy of a security incident. Cryptographic measurements and controls (to create trusted IIoT ecosystems) and anomaly detection functions address this reactive behavior. Host intrusion detection (HID) and host intrusion protection (HIP) are examples of dynamic integrity attestation controls to proactively secure an endpoint.

In IT environments, network and application blacklisting policies are commonly used. Whitelisting is more common in OT environments. But, when new exploits of zero-day vulnerabilities are detected, these policies are updated after the fact. AI/machine learning allows us to dynamically update blacklisting and whitelisting policies, based on anomalous behavior.

Machine learning extensively uses mathematical models based on historic...