Abstract
The Industrial Internet of Things (IIoT) platform consists of purpose-driven communication controllers, enterprise-grade modems (routers and gateways), and edge computing systems that require integrated software and sensing capability in mission-critical environments. Extensible purpose-built industrial supervisory control and data acquisition networks are prone to numerous cybersecurity threats. In this paper, the historical databased qualitative threat assessment was part of the comprehensive risk breakdown (i.e., to quantify assessment and remediation) of the practicing industry (i.e., systems that rely on robotics, big data & analytics). Furthermore, a risk and operability (HAZOP & convolution neural-network) evaluation was proved to be the paramount study for autonomous vulnerability assessment. Through autonomous network management, continuous software monitoring, data-driven device insights, and integrated content filtering, the proposed endpoint protection scheme shows significant improvement in preventing data breaches, denial of service (DoS), and malware detection. A distinctive computational methodology to determine the cyber risk for industrial structures with IoT-explicit control factors has been programmed and elucidated in the perspective of IIoT systems. Firmware driven emulation (integrated and optimized) outcome aided to reduce breach ratio, better incident detection, and enhanced protection of confidential data.
Highlights
Mission-critical industrial IoT (IIoT) has become increasingly a leading system paradigm in the IIoT realm whose failure can result in significant economic and operational costs
Well-orchestrated and selfpropagating advanced persistent threats (APT) are hard to detect due to adversaries’ adopted concealment techniques, e.g., register reassignment subroutine recording, instruction subroutines, code transportation, and code integration
Factors such as (a) lack of security-aware data, (b) misuse of insider device privileges, (c) unable to aggregate and filter cybersecurity-aware alerts, (d) inability to blacklist unauthorized event/access, (e) poorly configured interconnected IIoT infrastructure, (f) improper utilization sensitive information by legitimate or adversary controlled applications, (g) lack of realization regarding flash crowd or denial of service (DoS) in context of data and application can result in a devastating impact on efficient industrial automation endeavor
Summary
Mission-critical industrial IoT (IIoT) has become increasingly a leading system paradigm in the IIoT realm whose failure can result in significant economic and operational costs. Industrial IoT enabled cloud is currently protected by deploying techniques, e.g., ‘controlled access privileges’, ‘restricting unnecessary micro-processing services’, and limiting virtualized stack Factors such as (a) lack of security-aware data, (b) misuse of insider device privileges, (c) unable to aggregate and filter cybersecurity-aware alerts, (d) inability to blacklist unauthorized event/access, (e) poorly configured interconnected IIoT infrastructure, (f) improper utilization sensitive information by legitimate or adversary controlled applications, (g) lack of realization regarding flash crowd or denial of service (DoS) in context of data and application can result in a devastating impact on efficient industrial automation endeavor. INDUSTRY DRIVEN MODULAR NODE FORMULATION IIoT architecture can be divided into three sub-units (a) things (i.e., Wind River Linux, microcontrollers and microprocessor), (b) gateway devices (network), and (c) cloud (servers, storage, API’s management) Considering this setting, the security layer is dependent on (1) control layer, (2) data layer, and (3) communication and connectivity layer. We have registered / setup protocol stack (i.e., security layer, storage layer, preprocessing layer and monitoring layer) with the purpose of cross-layer optimization that was utilized by all the related nodes
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