Reliability Analysis in Cyber-Physical System Using Deep Learning for Smart Cities Industrial IoT Network Node

Rajawat, Anand Singh; Bedi, Pradeep; Goyal, S. B.; Shaw, Rabindra Nath; Ghosh, Ankush

doi:10.1007/978-981-16-7498-3_10

Cited by 23 publications

(5 citation statements)

References 28 publications

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“…Machine learning can also help in detecting real-time malicious and suspicious patterns, making security analytics a crucial solution for cyber security in the near future. According to [13], deep learning can detect cyber-attacks and should be considered in building IoT devices and smart cities. The paper represented by [5] talks about a new intrusion detection system that can detect five different types of threats in a network, including Exploit, DOS, Probe, Generic, and Normal.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Intrusion Detection Systems Performance with UNSW-NB15 Data Analysis

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Idrissi,

Mahmoud

et al. 2024

Algorithms

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The rapid proliferation of new technologies such as Internet of Things (IoT), cloud computing, virtualization, and smart devices has led to a massive annual production of over 400 zettabytes of network traffic data. As a result, it is crucial for companies to implement robust cybersecurity measures to safeguard sensitive data from intrusion, which can lead to significant financial losses. Existing intrusion detection systems (IDS) require further enhancements to reduce false positives as well as enhance overall accuracy. To minimize security risks, data analytics and machine learning can be utilized to create data-driven recommendations and decisions based on the input data. This study focuses on developing machine learning models that can identify cyber-attacks and enhance IDS system performance. This paper employed logistic regression, support vector machine, decision tree, and random forest algorithms on the UNSW-NB15 network traffic dataset, utilizing in-depth exploratory data analysis, and feature selection using correlation analysis and random sampling to compare model accuracy and effectiveness. The performance and confusion matrix results indicate that the Random Forest model is the best option for identifying cyber-attacks, with a remarkable F1 score of 97.80%, accuracy of 98.63%, and low false alarm rate of 1.36%, and thus should be considered to improve IDS system security.

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Section: Related Workmentioning

confidence: 99%

Enhanced Intrusion Detection Systems Performance with UNSW-NB15 Data Analysis

More,

Idrissi,

Mahmoud

et al. 2024

Algorithms

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“…The parts are connected in a sequence. We need to segment the sensing process into two components to analyze the reliability of sensing: data collection and processing [6][7][8][9][10]. Figure 2 depicts the modules of a sensing.…”

Section: Figure 1 Rbd Of Primary Iot Systemsmentioning

confidence: 99%

Parametric Evaluation Techniques for Reliability of Internet of Things (IoT)

Singh¹,

Singh²,

Barak³

et al. 2023

IJCMEM

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Reliability analysis aims to assess the dependability and performance of a system over time, considering factors such as device failures, communication issues, and data integrity. By integrating taxonomy and reliability analysis, researchers can systematically analyze and enhance the reliability of IoT systems by addressing specific components and categories identified in the taxonomy. IoT based on its reliable applications is classified among various categories such as smart home devices, smart watches, electric vehicles with IoT capabilities, industrial, healthcare, agriculture, soil monitoring sensors, smart city infrastructure, environmental monitoring devices, and smart energy management devices. The need for a consistent taxonomy was one of the problems recognized and examined in this paper even though IoT management technology is developing. To examine the reliability analysis of IoT systems, we created a brand-new taxonomy for IoT gadgets that places a strong focus on administration. We have released a completely new IoT reliability RBD model analysis at different gateways and Round-Trip Time is to be analyzed. It features an aircraft for dependability. Our goal is to help administrators of networks, as well as designers, put processes in place that will analyze and enhance the reliability of IoT systems. To implement techniques to increase the IoT system's reliability, we may utilize the management capabilities to keep an eye on the devices. ChatGPT can act as the conversational interface for ISABELA. By integrating ChatGPT with ISABELA's functionality, users can have natural language conversations with ISABELA, making the interaction more intuitive and user-friendly. A use case study of ISABELA has enhanced the position of the CHATGPT system in the IoT system which increase the efficiency and reliability of IoT designs and networks.

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“…Though the creation of smart cities (as well as smart companies) envisages the increase in the level of economic processes' automatisation, humans are not replaced with machines, but remain in the smart city (Leite 2022;van der Wouden 2022). Due to this, the smart city is a complex socio-economic cyber-physical system (Ahmad et al 2022;Duygan et al 2022;Rajawat et al 2022). When studying it, it is necessary to pay special attention to the social urban environment (Bokhari and Myeong 2022;Singh and Dwivedi 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

The Risks of Smart Cities and the Perspectives of Their Management Based on Corporate Social Responsibility in the Interests of Sustainable Development

Morozova

Yatsechko

2022

Risks

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Purpose: Bring to light the risks of smart cities and the perspectives of their management. It has been discovered that smart cities are created and developed under the impact of not only technological factors but also social factors. The connection between smart cities and quality of life is systemic (direct and reverse)—the quality of life also specifies the creation and development of smart cities. The impact of the COVID-19 pandemic on the development of smart cities is almost null (smart cities do not depend on the implementation of SDG 3). This paper’s originality lies in the description of a new angle of studying smart cities—from the position of risks, and in the determination of the current level of these risks and the dynamics of their change during systematisation and description of the wide international experience of creation and development of smart cities. This paper’s uniqueness lies in the development of a new approach to managing the creation and development of smart cities, which is based on corporate social responsibility, thus specifying and ensuring the involvement and important role of the subjects of entrepreneurship in this process. It is proved that the contribution of smart cities to the implementation of the SDGs is much wider and goes beyond the limits of SDG 9—it also extends to SDG 1 and SDGs 11–13.

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Reliability Analysis in Cyber-Physical System Using Deep Learning for Smart Cities Industrial IoT Network Node

Cited by 23 publications

References 28 publications

Enhanced Intrusion Detection Systems Performance with UNSW-NB15 Data Analysis

Enhanced Intrusion Detection Systems Performance with UNSW-NB15 Data Analysis

Parametric Evaluation Techniques for Reliability of Internet of Things (IoT)

The Risks of Smart Cities and the Perspectives of Their Management Based on Corporate Social Responsibility in the Interests of Sustainable Development

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