Chapter 8. IIoT Security Using Emerging Technologies

"Security-enabled data, and the language of AI, will drive our future connected, driverless world."                                             – Sudha Jamthe, Author and CEO of IoT Disruptions

In the present century, we are thriving in a technology hotbed. Every industry—be it consumer, retail, or manufacturing—is embracing disruptive innovations that challenge our operational status quo. Just consider how fast we have digitally traveled from smartphones to smart homes, smart farming, and smart cars. Today, we rely on technology to solve any problem, be it individual, social, national, or industrial.

In the earlier chapters of this book, we discussed various technologies to arrive at a framework for industrial IoT security. The framework is by no means fixed and final. It needs to evolve, with newer innovations and attack vectors. After all, new technologies serve as hotbeds for new threats, as well. Just as the threat landscape never stops...

Blockchain to secure IIoT transactions

IoT communications predominantly use broker or brokerless publish-subscribe models. Standards such as MQTT and DDS support these communication models. In the case of MQTT, the broker serves as the central supervisory entity, which can be either on-premise or in the cloud. This centralized, supervisory modus operandi is highly scalable. However, any attack on the central node (such as a denial of service) can potentially melt down communication in its entirety. From a security standpoint, the main promise of distributed ledger technology, or blockchain, is to overcome the vulnerabilities related to the single point of failure in centrally controlled architectures.

Decentralized identity management, tamper-proofing the supply chain, and so on, are supported by blockchain, as well.

Blockchain essentially provides a distributed transaction environment where-in, just like the DNA in every cell, a complete copy of the blockchain is held by every participant...

Cognitive countermeasures – AI, machine learning, and deep learning

Cognitive computing is highly relevant and increasingly indispensable to industrial IoT use cases, where machines can make autonomous decisions based on IoT device data, and can also protect themselves against external threats and malicious attacks. This may not be merely sci-fi imagination, as it was a decade ago.

Computer visionaries such as Alan Turing were optimistic about artificial intelligence (AI) since the 1950s. However, the recent spike in interest and research on AI owe to faster, cheaper, and more powerful parallel processing using GPUs, coupled with a steady growth in data sciences. Pure AI—where machines and robots can operate and decide with full autonomy—is still a long way away. However, practical AI, where cognitive computing augments human expertise, is already a reality. Machine learning and its specialized branch, called deep learning, are currently the main drivers behind cognitive IoT and practical...

Time-sensitive networking – Next-gen industrial connectivity

Time-sensitive networking (TSN) is an evolution of the IEEE 802.1 Ethernet standard (IE3- TSN). TSN is not an IIoT-security-specific protocol, but its design offers multiple security benefits (and a few challenges). TSN is still in an early adoption phase. In this section, we will discuss the relevance of TSN as an emerging technology in secured IIoT deployments.

IEEE 802.1 Ethernet, although a widely deployed low-cost layer 2 technology, fails to match the deterministic performance requirements of industrial automation and control applications. To achieve deterministic performance, most industrial enterprises still continue to use fieldbus technologies and their proprietary enhancements to Ethernet (such as EtherCat, PROFINET, or SERCOS III). These proprietary protocols are not built for security and interoperability. The result has been fragmented industrial networks that are incapable of integrating with advanced analytics services...

Other Promising Trends

It is perhaps worthwhile to note that there are other disruptive concepts, such as quantum computing and Artificial General Intelligence (AGI), that will redefine how we treat security today. Quantum computing, although rather nascent at present, uses the concept of quantum mechanical paradigms that could render current cryptography algorithms ineffective in several instances. The importance of hardware-based trust is now well acknowledged for industrial networks. MIT's physical unclonable function (PUF) is one of the emerging technologies to tamper-proof devices at the silicon level.

As noted earlier in this chapter, security is a dynamic discipline, where threats and countermeasures evolve with time. As such, product developers need to ensure that their solutions can stand the test of time-not just for a year or two, but for the foreseeable future. Emerging technologies and their impact on the security landscape in the years to come is a vital consideration in any...

Summary

This chapter presented a few emerging concepts and enabling technologies for IIoT security. The current state of the art and practical viability of these technologies were also discussed. Intense research and development and pilots are in progress, as these technologies continue to mature. To gain an in-depth understanding and to keep up with the evolutionary track of these enablers, the interested reader is encouraged to utilize the references provided in this chapter.

The next chapter presents real-world scenarios to analyze the anatomy of an industrial cyberattack and to see how enterprises are implementing IIoT security best practices to protect their connected assets and infrastructures.