Security of smart manufacturing systems

Abstract

A B S T R A C TA revolution in manufacturing systems is underway: substantial recent investment has been directed towards the development of smart manufacturing systems that are able to respond in real time to changes in customer demands, as well as the conditions in the supply chain and in the factory itself. Smart manufacturing is a key component of the broader thrust towards Industry 4.0, and relies on the creation of a bridge between digital and physical environments through Internet of Things (IoT) technologies, coupled with enhancements to those digital environments through greater use of cloud systems, data analytics and machine learning. Whilst these individual technologies have been in development for some time, their integration with industrial systems leads to new challenges as well as potential benefits. In this paper, we explore the challenges faced by those wishing to secure smart manufacturing systems. Lessons from history suggest that where an attempt has been made to retrofit security on systems for which the primary driver was the development of functionality, there are inevitable and costly breaches. Indeed, today's manufacturing systems have started to experience this over the past few years; however, the integration of complex smart manufacturing technologies massively increases the scope for attack from adversaries aiming at industrial espionage and sabotage. The potential outcome of these attacks ranges from economic damage and lost production, through injury and loss of life, to catastrophic nation-wide effects. In this paper, we discuss the security of existing industrial and manufacturing systems, existing vulnerabilities, potential future cyber-attacks, the weaknesses of existing measures, the levels of awareness and preparedness for future security challenges, and why security must play a key role underpinning the development of future smart manufacturing systems.

Human-Machine Interaction and Human Resource Management Perspective for Collaborative Robotics Implementation and Adoption

“…Information Systems (IS) research on technology adoption related to organizational and individual behavior [1] has been highly developed in the recent decade. It concurs with Industry 4.0 (I4.0), where digitalization within organizations is growing at an important rate with smarter [2], more autonomous, and even self-conscious systems [3]. In Canada, 900,000 jobs in the manufacturing industry could be automated or robotized in the future, which represents 61% of the entire Canadian manufacturing industry [4].…”

Artificial intelligence and internet of things for sustainable development – emerging technological and social opportunities and threats

“…Such solutions require adequate business models to regulate revenues distribution to cover involved subjects costs, security mechanism to protect data against unauthorized access and malicious actors, anonymizing algorithms for strong analyses, standards for acquiring interoperability, algorithms and storage solutions for big data operations, interaction models for increasing usability, and efforts to make users involved AI and IoT systems deployment processes [13]. We extracted a set of key requirements to enable more reliable systems: security (regulations, standards and guidelines deployment, end-to-end security mechanisms, intrusion detection systems, post incident management, data encryption, configurable authorization control, users security skills training), data (data fusion, enforced formal data sharing frameworks, cloudcomputing, nonrigid SQL databases, automated collection and analyses) flexibility (open APIs, device and sensors manageability, constant development through networking and device agnosticism) [3,7,22,52].…”

“…Security and privacy of data may be threaten as sending data without any encryption by sensors due to their limited computational power, outsourced storage and computation [10]. That is why AI and IoT-based solutions may be vulnerable to cyber attacks, including: installing malware, physical, time delay, data tampering, denial of service (DoS), false data injection, side channel, covert-channel, eavesdropping, replay, spoofing, zero day-attacks, and those against AI analytics [52,53]. Attacks may be performed individual criminals or organized networks of criminal entities, terrorist groups, rival organizations, hacker hobbyists, patriotic hackers often driven by a political ideal, or even insiders possessing access to the organization's assets (current and former employees, business partners, service providers, vendors, guests) [52].…”

“…That is why AI and IoT-based solutions may be vulnerable to cyber attacks, including: installing malware, physical, time delay, data tampering, denial of service (DoS), false data injection, side channel, covert-channel, eavesdropping, replay, spoofing, zero day-attacks, and those against AI analytics [52,53]. Attacks may be performed individual criminals or organized networks of criminal entities, terrorist groups, rival organizations, hacker hobbyists, patriotic hackers often driven by a political ideal, or even insiders possessing access to the organization's assets (current and former employees, business partners, service providers, vendors, guests) [52]. Important threat raised also on social background as employees using AI and IoT applications, may argue that these technologies cause a conflict of interest with them, feel unsecure about future employment and refuse to use them [51].…”

A Fuzzy WASPAS-Based Approach to Determine Critical Information Infrastructures of EU Sustainable Development

“…They often lead to numerous and significant losses, the essential disruption of production, and the destruction of the environment, etc. [11][12][13][14].…”