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Chen, Hanlin, Hu, Ming, Yan, Hui, Yu, Ping.  2019.  Research on Industrial Internet of Things Security Architecture and Protection Strategy. 2019 International Conference on Virtual Reality and Intelligent Systems (ICVRIS). :365–368.
Industrial Internet of Things (IIoT) is a fusion of industrial automation systems and IoT systems. It features comprehensive sensing, interconnected transmission, intelligent processing, self-organization and self-maintenance. Its applications span intelligent transportation, smart factories, and intelligence. Many areas such as power grid and intelligent environment detection. With the widespread application of IIoT technology, the cyber security threats to industrial IoT systems are increasing day by day, and information security issues have become a major challenge in the development process. In order to protect the industrial IoT system from network attacks, this paper aims to study the industrial IoT information security protection technology, and the typical architecture of industrial Internet of things system, and analyzes the network security threats faced by industrial Internet of things system according to the different levels of the architecture, and designs the security protection strategies applied to different levels of structures based on the specific means of network attack.
White, Ruffin, Caiazza, Gianluca, Jiang, Chenxu, Ou, Xinyue, Yang, Zhiyue, Cortesi, Agostino, Christensen, Henrik.  2019.  Network Reconnaissance and Vulnerability Excavation of Secure DDS Systems. 2019 IEEE European Symposium on Security and Privacy Workshops (EuroS PW). :57–66.

Data Distribution Service (DDS) is a realtime peer-to-peer protocol that serves as a scalable middleware between distributed networked systems found in many Industrial IoT domains such as automotive, medical, energy, and defense. Since the initial ratification of the standard, specifications have introduced a Security Model and Service Plugin Interface (SPI) architecture, facilitating authenticated encryption and data centric access control while preserving interoperable data exchange. However, as Secure DDS v1.1, the default plugin specifications presently exchanges digitally signed capability lists of both participants in the clear during the crypto handshake for permission attestation; thus breaching confidentiality of the context of the connection. In this work, we present an attacker model that makes use of network reconnaissance afforded by this leaked context in conjunction with formal verification and model checking to arbitrarily reason about the underlying topology and reachability of information flow, enabling targeted attacks such as selective denial of service, adversarial partitioning of the data bus, or vulnerability excavation of vendor implementations.

Oyekanlu, E..  2018.  Distributed Osmotic Computing Approach to Implementation of Explainable Predictive Deep Learning at Industrial IoT Network Edges with Real-Time Adaptive Wavelet Graphs. 2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE). :179–188.
Challenges associated with developing analytics solutions at the edge of large scale Industrial Internet of Things (IIoT) networks close to where data is being generated in most cases involves developing analytics solutions from ground up. However, this approach increases IoT development costs and system complexities, delay time to market, and ultimately lowers competitive advantages associated with delivering next-generation IoT designs. To overcome these challenges, existing, widely available, hardware can be utilized to successfully participate in distributed edge computing for IIoT systems. In this paper, an osmotic computing approach is used to illustrate how distributed osmotic computing and existing low-cost hardware may be utilized to solve complex, compute-intensive Explainable Artificial Intelligence (XAI) deep learning problem from the edge, through the fog, to the network cloud layer of IIoT systems. At the edge layer, the C28x digital signal processor (DSP), an existing low-cost, embedded, real-time DSP that has very wide deployment and integration in several IoT industries is used as a case study for constructing real-time graph-based Coiflet wavelets that could be used for several analytic applications including deep learning pre-processing applications at the edge and fog layers of IIoT networks. Our implementation is the first known application of the fixed-point C28x DSP to construct Coiflet wavelets. Coiflet Wavelets are constructed in the form of an osmotic microservice, using embedded low-level machine language to program the C28x at the network edge. With the graph-based approach, it is shown that an entire Coiflet wavelet distribution could be generated from only one wavelet stored in the C28x based edge device, and this could lead to significant savings in memory at the edge of IoT networks. Pearson correlation coefficient is used to select an edge generated Coiflet wavelet and the selected wavelet is used at the fog layer for pre-processing and denoising IIoT data to improve data quality for fog layer based deep learning application. Parameters for implementing deep learning at the fog layer using LSTM networks have been determined in the cloud. For XAI, communication network noise is shown to have significant impact on results of predictive deep learning at IIoT network fog layer.