Security Challenges Facing IoT Layers and its Protective Measures

Rao, Tariq Aziz; Ehsan-ul-Haq,

doi:10.5120/ijca2018916607

Cited by 37 publications

(20 citation statements)

References 9 publications

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“…IoT is mainly composed of five layers [ 47 , 48 ]: “(1) Perception or physical layer, (2) Network or communication layer, (3) Middleware or processing layer, (4) Application layer, and (5) Business layer”. In some situations.…”

Section: Introductionmentioning

confidence: 99%

“…Supply chain systems are very essential systems for numerous industries and organizations, mainly the various businesses reproduction and profitability directly depending on the effectiveness of the supply chain system used [ 47 , 48 ]. A supply chain system encompassing several users/entities and operations; however, a number of participating entities and their performing operations depend on the type and size of organizational infrastructure.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intuitive Development to Examine Collaborative IoT Supply Chain System Underlying Privacy and Security Levels and Perspective Powering through Proactive Blockchain

Shahzad

Zhang

Gherbi

2020

Sensors

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Undoubtedly, the supply chain management (SCM) system is an important part of many organizations worldwide; over time, the technologies used to manage a supply chain ecosystem have, therefore, a great impact on businesses’ effectiveness. Among others, numerous developments have been made that targeted to have robust supply chain systems to efficiently manage the growing demands of various supplies, considering the underlying requirements and main challenges such as scalability, specifically privacy and security, of various business networks. Internet of things (IoT) comes with a solution to manage a complex, scalable supply chain system, but to provide and attain enough security during information exchange, along with keeping the privacy of its users, is the great inherent challenge of IoT. To fulfill these limitations, this study designs and models a scaled IoT-based supply chain (IoT-SC) system, comprising several operations and participants, and deploys mechanisms to leverage the security, mainly confidentially, integrity, authentication (CIA), and a digital signature scheme to leverage potentially secured non-repudiation security service for the worst-case scenario, and to leverage privacy to keep users sensitive personal and location information protected against adversarial entities to the IoT-SC system. Indeed, a scaled IoT-SC system certainly opens new challenges to manage privacy and security while communicating. Therefore, in the IoT-SC system, each transaction writes from edge computing nodes to the IoT-SC controller is thoroughly examined to ensure the proposed solutions in bi-directional communication, and their robustness against adversarial behaviors. Future research works, employing blockchain and its integrations, are detailed as paces to accelerate the privacy and security of the IoT-SC system, for example, migrating IoT-centric computing to an immutable, decentralized platform.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intuitive Development to Examine Collaborative IoT Supply Chain System Underlying Privacy and Security Levels and Perspective Powering through Proactive Blockchain

Shahzad

Zhang

Gherbi

2020

Sensors

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“…Typically, IoT has three layers, four -layers, and five layers of architecture [31,32,33]. Although, there is no standard model of the IoT architecture.…”

Section: Attacks Against Iot Structurementioning

confidence: 99%

Anomaly-based Intrusion Detection System for IoT Application

Bhavsar

Roy

Kelly

et al. 2023

Preprint

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Internet-of-Things (IoT) connects various physical objects through the Internet and it has a wide application, such as in transportation, military, healthcare, agriculture, and many more. Those applications are increasingly popular because they address real-time problems. In contrast, the use of transmission and communication protocols have raised serious security concerns for IoT devices, and traditional methods such as signature and rule-based methods are inefficient for securing these devices. Hence, identifying network traffic behavior and mitigating cyber attacks are important in IoT to provide guaranteed network security. Therefore, we develop an Intrusion Detection System (IDS) based on a deep learning model called Pearson-Correlation Coefficient - Convolutional Neural Networks (PCC-CNN) to detect network anomalies. The PCC-CNN model combines the important features obtained from the linear-based extractions followed by the Convolutional Neural Network. It performs a Binary classification for Anomaly detection and also a Multiclass classification for various types of attacks. The model is evaluated on three publicly available datasets: NSL-KDD, CICIDS-2017, and IOTID20. We first train and test five different (Logistic Regression, Linear Discriminant Analysis, K Nearest Neighbour, Classification and Regression Tree,\& Support Vector Machine) PCC-based Machine Learning models to evaluate the model performance. We achieve the best similar accuracy from the KNN and CART model of 98\%, 99\%, and 98\%, respectively, on the three datasets. On the other hand, we achieve a promising performance with a better detection accuracy of 99.89\% and with a low misclassification rate of 0.001 with our proposed PCC-CNN model. The integrated model is promising, with a misclassification rate (or False alarm rate) of 0.02, 0.02, and 0.00 with Binary and Multiclass intrusion detection classifiers. Finally, we compare and discuss our PCC-CNN model in comparison to five traditional PCC-ML models. Our proposed Deep Learning (DL)-based IDS outperforms traditional methods.

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“…The basic rule of BIM in buildings is to give or provide data collected from any source that specifies the circumstances of these structures or original data. In any situation, valuable data might be utilized to evaluate the remodeling operation [102]. Furthermore, Table 3 summarises some recent research on integrating IoT-BIM.…”

Section: Iot-bim Operational Processmentioning

confidence: 99%