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Artificial intelligence and machine learning aided blockchain systems to address security vulnerabilities and threats in the industrial Internet of things

Artificial intelligence and machine learning aided blockchain systems to address security vulnerabilities and threats in the industrial Internet of things

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Advent of digital sensors and machines led to a significant acceleration in industrial evolution. The desire to automate industrial processes with minimum human intervention paved the way for the onset of a new era of technological nomenclature called the industrial Internet of things (IIoT). A remarkable feature of IIoT is its underlying architecture which allows the managers/engineers/supervisors to remotely operate and access the performance of their machines. Industries ranging from healthcare, finance, logistics, and power have witnessed a major performance increment and quality stabilization by transforming themselves into an IIoT empowered smart environment. However, this transformation has brought with itself a whole new set of challenges with cybersecurity being the paramount. The vulnerabilities like bugs and broken processes can lead to a serious compromise or even collapse of security mechanisms of IIoT networks. Such a situation will have a devastating impact on the financial health, reputation, and credibility of companies. After an extensive review of existing technologies, we believe that blockchain, artificial intelligence (AI), and machine learning (ML) can complement each other in building a revolutionary deterrent to negate malicious activities that in any form intend to harm the system. While, blockchain offers public/private/consortium relationships, ML and AI, on the other hand, follow the principle of supervised/ unsupervised/reinforcement learning and reactive/memory approaches, respectively. Based on the distributed ledger system, blockchain mechanisms can be aided with self-learning algorithms which will update and strengthen the database by learning each time the system suffers new forms of network attacks and intrusions. This process of learning will help build a robust system which can learn to optimize its deterrence procedures against different forms of attacks. It is due to these overwhelming benefits, blockchain, AI, and ML find applications in smart logistics, predictive maintenance, autonomous vehicles, intelligent manufacturing, and smart grid maintenance.

Chapter Contents:

  • 13.1 Introduction
  • 13.2 Birth of industrial Internet of things
  • 13.3 Application areas and current examples of its use
  • 13.3.1 Production flow monitoring
  • 13.3.2 Connected factories
  • 13.3.3 Power management
  • 13.3.4 Autonomous vehicles
  • 13.3.5 Predictive maintenance
  • 13.4 Implementation challenges of IIoT
  • 13.4.1 Data storage
  • 13.4.2 Security
  • 13.4.3 Delivering values to customers
  • 13.4.4 Technology infrastructure
  • 13.4.5 Immaturity of IIoT standards
  • 13.4.6 Visibility and connectivity
  • 13.5 Vulnerabilities, threats, and risks
  • 13.5.1 Vulnerability
  • 13.5.2 Threats
  • 13.5.3 Risk
  • 13.6 Security considerations
  • 13.6.1 Authentication
  • 13.6.2 Encryption
  • 13.6.3 Digital signatures
  • 13.6.4 Hashing
  • 13.6.5 Server security
  • 13.7 Future of IIoT
  • 13.8 Introduction to blockchain
  • 13.8.1 Types of blockchain
  • Private blockchain
  • Public blockchain
  • Federated or consortium blockchain
  • 13.8.2 Role of blockchain in industrial IoT
  • 13.8.3 Security applications of the blockchain in the IIoT
  • Applications in IIoT
  • Applications in big data and data analytics
  • Applications in vertical and horizontal integration systems
  • Applications in autonomous robots and vehicle
  • 13.8.4 Significant consensus of blockchain
  • 13.8.5 Challenges in the realization of blockchain
  • 13.9 Introduction to AI
  • 13.9.1 Types of AI
  • Reactive machine AI
  • Limited memory AI
  • Theory of mind AI
  • Self-aware AI
  • 13.9.2 Applications of artificial intelligence
  • 13.10 Introduction to machine learning
  • 13.10.1 Types of ML
  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • 13.10.2 Applications of machine learning
  • 13.10.3 Challenges in implementing machine learning and artificial intelligence-based security solutions for IIoT
  • 13.11 Conclusions
  • References

Inspec keywords: learning (artificial intelligence); production engineering computing; blockchains; Internet of Things; computer network security

Other keywords: digital sensors; industrial Internet of Things; automate industrial processes; quality stabilization; AI; smart environment; blockchain systems; self-learning algorithms; IIoT networks; industrial evolution; distributed ledger system; machine learning; reinforcement learning; acceleration; unsupervised learning; smart logistics; ML; human intervention; network attacks; cybersecurity; supervised learning; security vulnerabilities; robust system; intelligent manufacturing; artificial intelligence

Subjects: Data security; Production engineering computing; Industrial applications of IT; Ubiquitous and pervasive computing; Distributed databases

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