Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey

Musa, Abubakar Ahmad; Hussaini, Adamu; Liao, Weixian; Fan, Lei; Yu, Wei

doi:10.3390/fi15060199

Cited by 9 publications

(6 citation statements)

References 89 publications

(98 reference statements)

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“…The layer processes the data obtained from the perception layer through numerous machine learning, deep-learning algorithms, and data processing elements to generate new insight and, in some cases, make projections and provide useful warnings of impending hazards and situations. Various types of technologies of the processing layer include wired, wireless, and satellite technologies, as well as cloud and other third-party computational systems [46].…”

Section: Data-processing Layermentioning

confidence: 99%

“…The FTA framework has been used extensively across various safety-critical domains for safety analysis. In the IoT domain, the FTA framework was used in the safety analysis of smart homes [71], smart grid system [77], smart aquaculture [78] and CPS, in general [46,79]. Although the studies of IoT safety design evaluation using FTA are in progress, the manual process of the approach still needs to be improved.…”

Section: Top Eventmentioning

confidence: 99%

“…Although separate analyses of the safety and security properties could suffice in other fields, the case differs in cyber-physical systems (CPS). The peculiarity of CPS, for which the IoT is at the centre stage, demands a high consideration of the safety and cyber-security properties to develop dependable systems [46]. In the IoT environment, safety and security requirements are becoming increasingly interwoven, and the systems are increasingly given autonomous, adaptive, and evolving features [16].…”

Section: Introductionmentioning

confidence: 99%

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An Overview of Safety and Security Analysis Frameworks for the Internet of Things

et al. 2023

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The rapid progress of the Internet of Things (IoT) has continued to offer humanity numerous benefits, including many security and safety-critical applications. However, unlocking the full potential of IoT applications, especially in high-consequence domains, requires the assurance that IoT devices will not constitute risk hazards to the users or the environment. To design safe, secure, and reliable IoT systems, numerous frameworks have been proposed to analyse the safety and security, among other properties. This paper reviews some of the prominent classical and model-based system engineering (MBSE) approaches for IoT systems’ safety and security analysis. The review established that most analysis frameworks are based on classical manual approaches, which independently evaluate the two properties. The manual frameworks tend to inherit the natural limitations of informal system modelling, such as human error, a cumbersome processes, time consumption, and a lack of support for reusability. Model-based approaches have been incorporated into the safety and security analysis process to simplify the analysis process and improve the system design’s efficiency and manageability. Conversely, the existing MBSE safety and security analysis approaches in the IoT environment are still in their infancy. The limited number of proposed MBSE approaches have only considered limited and simple scenarios, which are yet to adequately evaluate the complex interactions between the two properties in the IoT domain. The findings of this survey are that the existing methods have not adequately addressed the analysis of safety/security interdependencies, detailed cyber security quantification analysis, and the unified treatment of safety and security properties. The existing classical and MBSE frameworks’ limitations obviously create gaps for a meaningful assessment of IoT dependability. To address some of the gaps, we proposed a possible research direction for developing a novel MBSE approach for the IoT domain’s safety and security coanalysis framework.

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Section: Data-processing Layermentioning

confidence: 99%

Section: Top Eventmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Overview of Safety and Security Analysis Frameworks for the Internet of Things

et al. 2023

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“…For example, these techniques can learn the operating pattern of the network from its data to predict traffic and performance degradation, detect anomalies and intrusions, and optimize energy efficiency. However, despite DNN's potential, striking a balance between their complexity and the complexity of the emerging O-RAN-based IoT system to satisfy the real-time performance requirement of their operating environment remains open for further research [41,46,47].…”

Section: Artificial Intelligence and Machine Learning (Ai/ml)mentioning

confidence: 99%

Open Radio Access Networks for Smart IoT Systems: State of Art and Future Directions

Musa,

Hussaini,

Qian

et al. 2023

Future Internet

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The Internet of Things (IoT) constitutes a vast network comprising various components such as physical devices, vehicles, buildings, and other items equipped with sensors, actuators, and software. These components are interconnected, facilitating the collection and exchange of copious data across networked communications. IoT empowers extensive monitoring and control over a myriad of objects, enabling them to gather and disseminate data that bolster applications, thereby enhancing the system’s capacity for informed decision making, environmental surveillance, and autonomous inter-object interaction, all without the need for direct human involvement. These systems have achieved seamless connectivity requirements using the next-generation wireless network infrastructures (5G, 6G, etc.), while their diverse reliability and quality of service (QoS) requirements across various domains require more efficient solutions. Open RAN (O-RAN), i.e., open radio open access network (RAN), promotes flexibility and intelligence in the next-generation RAN. This article reviews the applications of O-RAN in supporting the next-generation smart world IoT systems by conducting a thorough survey. We propose a generic problem space, which consists of (i) IoT Systems: transportation, industry, healthcare, and energy; (ii) targets: reliable communication, real-time analytics, fault tolerance, interoperability, and integration; and (iii) artificial intelligence and machine learning (AI/ML): reinforcement learning (RL), deep neural networks (DNNs), etc. Furthermore, we outline future research directions concerning robust and scalable solutions, interoperability and standardization, privacy, and security. We present a taxonomy to unveil the security threats to emerge from the O-RAN-assisted IoT systems and the feasible directions to move this research forward.

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“…Motivation. Time-to-event (TTE) analysis [14,59], also known as survival analysis, is of great importance in CPS dependability, as CPSs are characterized by the interaction of computational and physical processes, often facing uncertainty, and the reliability of the systems is of paramount importance. TTE analysis allows for modelling and predicting the time until certain events occur, such as predicting the passenger waiting time in an elevator system and predicting time-to-collision in an ADS.…”

Section: Paper IVmentioning

confidence: 99%

Digital Twin-based Anomaly Detection in Cyber-physical Systems

Ali

Yue

2021

2021 14th IEEE Conference on Software Testing, Verification and Validation (ICST)

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Cyber-Physical Systems (CPS) are susceptible to various anomalies during their operations. Thus, it is important to detect such anomalies. Detecting such anomalies is challenging since it is uncertain when and where anomalies can happen. To this end, we present a novel approach called Anomaly deTection with digiTAl twIN (ATTAIN), which continuously and automatically builds a digital twin with live data obtained from a CPS for anomaly detection. ATTAIN builds a Timed Automaton Machine (TAM) as the digital representation of the CPS, and implements a Generative Adversarial Network (GAN) to detect anomalies. GAN uses a GCN-LSTM-based module as a generator, which can capture temporal and spatial characteristics of the input data and learn to produce realistic unlabeled adversarial samples. TAM labels these adversarial samples, which are then fed into a discriminator along with real labeled samples. After training, the discriminator is capable of distinguishing anomalous data from normal data with a high F1 score. To evaluate our approach, we used three publicly available datasets collected from three CPS testbeds. Evaluation results show that ATTAIN improved the performance of two state-of-art anomaly detection methods by 2.413%, 8.487%, and 5.438% on average on the three datasets, respectively. Moreover, ATTAIN achieved on average 8.39% increase in the anomaly detection capability with digital twins as compared with an approach of not using digital twins.

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Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey

Cited by 9 publications

References 89 publications

An Overview of Safety and Security Analysis Frameworks for the Internet of Things

An Overview of Safety and Security Analysis Frameworks for the Internet of Things

Open Radio Access Networks for Smart IoT Systems: State of Art and Future Directions

Digital Twin-based Anomaly Detection in Cyber-physical Systems

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