Security Issues and Privacy Concerns in Industry 4.0 Applications. Группа авторов

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СКАЧАТЬ (SF). The mentioned automation creates individual manufacturing systems; the machines in industries are augmented with multiple sensors and network connectivity to monitor an entire process of the production and make decisions autonomously [12]. However, augmentation of machines and wireless connectivity can highly advance industrial and manufacturing systems, create robust response times, and allow for a near real-time machine-to-machine communication. Nevertheless, the revolution relates to the digital twin technologies; these technologies make real-world virtual versions of installations, processes, and real-time applications that can enhance testing to make cost-effective decentralized decisions.

The virtual copies allowing the cyber-physical machine to communicate with each other also create a real-time data exchange for human staff and automation interconnectivity between transparent information, processes, and technical assistance for industry 4.0 manufacturing [11]. Industry 4.0 demonstrates the business models, for example, offline programming for arc welding, take adoptive controls, and the overall processes of product design architecture and automotive industry 4.0 businesses implementation as well as a variety of smart factories all around the world.

       2.1.2.1 Machine-to-Machine (M2M) Communication

      A new concept evolved in Industry 4.0: machine-to-machine (M2M) communication, which is becoming an increasingly important technology in the entire domain. M2M refers to the concept where two devices exchange information with each other, such as sending and receiving data. The communication that occurs between devices is autonomous; no human intervention is required for the overall process of exchanging information. The wireless connectivity [13] between interrelated devices automatically exchanges and analyzes data in the cloud. The Internet of Things (IoT) enabled integrating several M2M systems and cloud computing that process all data by using the cloud web platform.

      This chapter highlights the distinct types of connectivity used between machines for communication. The most used connectivity [16] is: (i) Radiofrequency identification (RFID), which has a maximum range up to 10 meters that indicate the limitation of this type of connectivity; (ii) Bluetooth and Wireless-Fidelity (Wi-Fi), the most useable and reliable wireless connectivity for communication, with the range limitation from 10-20 meters in the case of Bluetooth and approximately 50 meters in the case of Wi-Fi; and (iii) low-frequency connectivity [15], which has a range of up to 1000 kilometers, such as GSM network and satellite.

      In general, the applications and the area of M2M connectivity that can be applied and used widely most probably apply to all domains. Likewise, it is successfully utilized in the artificial intelligence industry [14], allowing devices to communicate with each other and make autonomous decisions. Some established and used crucial industrial M2M applications that enhance the productions in the manufacturing industry are: (i) intelligent stock control, (ii) data collection for processing, (iii) just-in-time implementation, (iv) automated maintenance, and many more.

      2.1.3 Cloud Computing

The National Institute of Standard and Technology (NIST) defines cloud computing as “a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources” [17]. Cloud computing is considered as a new business model throughout the world, computing as a utility [18]; there are five essential characteristics of the cloud: (i) on-demand self-service, (ii) resource pooling, (iii) rapid expansion, (iv) broad network access, and (v) measured service. Furthermore, cloud computing has three service models [19] for cloud users, such as software, platform, and infrastructure. In the same manner, Forensics-as-a-Service is the emerging technology in the area of cloud computing that facilitates investigators to perform digital crime investigation tasks utilizing the services of the cloud. Cloud categorizes four distinct deployment models, public, private, commercial, and hybrid models, a proper way to deliver cloud services to cloud users. In cloud computing, this is intended to serve as a way of making broad comparisons of cloud deployment strategies, services models, and describing the actual baseline [20], for using cloud computing in the best manner.

       2.1.3.1 Infrastructure-as-a-Service (IaaS)

      Infrastructure-as-a-Service (IaaS) is one of the types of cloud service models, the computing infrastructure that manages the overall cloud services over the internet. On-demand quickly scales up and down infrastructure services [21], working on the mechanism like a pay-as-you-go platform where cloud users pay only for what they use. This avoids the expense of buying additional physical IT-infrastructure and managed complex server and other data center resources. In a cloud environment, IaaS offers resources as an individual service component, cloud service provider [22] manages the IT-resources in a virtualized environment, and the users can focus only on installation, configuration, and software maintenance. The cloud service providers enable users to elastically utilize virtual server and storage resources by a pay-per-use method, forming networks that tie them all together [23]. By renting IaaS from a cloud service provider, essentially, cloud users not only have on-demand hardware services but also provisioning software services that automate it.

       2.1.3.2 Challenges of Cloud Security in Fourth Industrial Revolution

The IT revolution has brought an important transformation in the industrial manufacturing processes with high impact [27]; this envelopment promotes the latest technology trends such as cloud-based systems, Internet of Things (IoT), Big Data Analysis, and others in the fourth industrial revolution. However, new innovative solutions carry challenges and limitations, such as unexpected risk, security vulnerabilities, infringing privacy, and many more [24]. Increased reliance on innovations may gain a competitive advantage [25], but on a certain note, security issues have been one of the most insignificant challengeable aspects for conducting successful business communication and transaction between machines on the cloud platform [26]. This chapter, highlighting the reflections and emphasizing the security issues and limitations of industry 4.0, raises awareness towards good security practice within the fourth industrial revolution:

       Inadequate Access Management

       Multi-tenancy

       Data Loss

       Data Breaches

       Infringing Privacy

       Cost of Transferring

      The chapter is structured as follows. The next section presents the generic model architecture for network forensics and cloud security issue in the industry 4.0 application. Section 2.3 discusses the model implementation, implementation platform used, such as Open Nebula and Network Miner for analysis of security threats. Section 2.4 focuses on the machine-to-machine communication impact on industrial 4.0 applications, and also describes an application scenario of cloud computing security in the domain of industry 4.0. Finally, we conclude our chapter in Section 2.5.

2.2 Generic Model Architecture

      This chapter considers cloud-based IT infrastructure in which a cloud service provider (CSP) executes virtual machine (VM) [28], cloud users get full control over the services like software, running on the VM, and these VMs are managed by the virtual machine monitor (VMM) [28]. CSP manages the physical machine with the help of hypervisor and provides resources on-demand to the cloud users [29]; the users have no right to access them directly. Multiple cloud users can share the same infrastructure or IT resources. In the event of malicious attacks, users’ virtual machines can easily be compromised.

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