## 2 IIoT Security Challenges and Concerns  
The discussion of IIoT can be traced back to the connection between the physical world and ubiquitous \"things\" via the Internet during the early 1990s [28]. While IIoT was still in its infancy growth stage, these definitions' scope is framed by different business interests and industry application scenarios [29; 30; 31; 32; 33]. For example, IETF and IEEE definitions are bounded by sensing technologies such as RFID and sensors [29; 30], whilst the W3C expound the IoT with the Word Wide Web ecosystems [31]. IoT's vision is to enable the connection of any \"things\" anytime. In most industry cases, we concluded that these \"things\" are associated with three fundamental characteristics: heterogeneity, unique identities and connectivity.  
Along with the growth of IIoT for supporting industries, IIoT security and privacy issues have become more challenging. These security challenges inherit the conventional systems issues such as the advanced persistent threat (APT) and are further exacerbated by the complexity of the newer IIoT associated characteristics such as high heterogeneity, large scale of \"things\", and cyber-physical systems. Table 2 further summarises the difference between conventional systems and IIoT security concerns.  
The high heterogeneity of \"things\" on a large scale implicates the interoperability issues of cross-network communications, cyber-physical systems and IIoT enabled-technologies integration. The intricate maze of interoperability issues arises when: (i) heterogeneous devices and sensor nodes are identified with different naming and addressing schemes; (ii) exploit different data structures and formats; and (iii) communicate through different security protocols with varying requirements of the network (e.g., reliability, communication cost, latency and bandwidth) and integrated to provide a plethora of service applications. The question of whether these conventional security mechanisms and defence systems can be further integrated and standardized universally in resolving IIoT security complexities remains unanswered.  
When there is a large scale of \"things\" (e.g., sensors in the aviation industry that consistently capture engine and aircraft health information during a flight) or diverse \"things\" in smart factories and manufacturing (e.g., sensors, edge devices, and smart grid) that collaborate to generate and exchange data continuously, these generated data from cyber-physical systems always come in big data flavour [17]. The data come in high volume and wide variety (e.g., structured, unstructured, quasi-structured, and semi-structured data), which need to be processed at a high velocity or analyzed nearly real-time, resulting in conventional data processing mechanisms being complicated or too expensive to scale and handle them efficiently.  
As conventional data processing systems mainly were built-in houses, centralized management, and typically worked within the organization boundaries with a finite number of connected devices and users; therefore, security and privacy issues were not a concern. However, security protection and defences mechanisms are significantly different in the era of IIoT. Collected sensors data are locally processed and analyzed by IIoT gateway or automation system before sending to a centralized cloud platform for remote monitoring and post-analysis. The scalability of the existing security mechanisms to authenticate, fine-grained access control on massive IIoT resources has drawn the industry and researcher's attention to move forward into a decentralized approach. Subsequently, more lightweight and highly efficient encryption schemes have been proposed recently to protect the tiniest \"things\" of IIoT, such as edge devices, sensor nodes and WSNs.