Secure data exchange in Industrial Internet of Things

Sukiasyan, Anna; Badikyan, Hasmik; Pedrosa, Tiago; Leitão, Paulo

doi:10.1016/j.neucom.2021.07.101

Cited by 9 publications

(4 citation statements)

References 13 publications

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“…The widespread use of Industrial Internet of Things(IIoT) has enabled large-scale, reliable, and secure industrial environments, but existing deployments often fall short in meeting security standards and have limited resources leading to security breaches and potential critical outcomes. To address the concerns, a secure architecture is proposed [19] that combines blockchain-based solutions with IIoT environments, utilizing a structured asynchronous blockchain Directed Acyclic Graph(DAG) for improved security and transaction efficiency. The proposed solution incorporates detailed use cases, sequence diagrams, and a treat model created using the STRIDE approach for comprehensive security testing.…”

Section: Literature Surveymentioning

confidence: 99%

“…The input of softmax classifier of neural network is defined as follows, 𝐼 = {(𝑢 (1) , 𝑣 (1) ), … . , (𝑢 (𝑛) , 𝑣 (𝑛) ), 𝑣 (𝑛) ∈ {1,2, … , 𝑘} (19) For every input 𝑢 we used classifier for estimating the probability of posterior 𝑝(𝑣 = 𝑖/𝑢), it is the probability that 𝑢 is the sample input of 𝑖. The hidden layer activation function is defined as follows,…”

Section: (Ii) Global Model Generationmentioning

confidence: 99%

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Twin chain: A Blockchain based Federated Learning Intrusion Detection System usingOptimized Backpropagation based Neural Network for Edge Assisted IoT Networks

Narayanan

Paul

2023

Preprint

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Ensuring security and privacy in IoT environments is a critical concern due to the prevalence of intrusions. Federated learning (FL) has emerged as a prominent technology for intrusion detection without compromising data privacy. This study proposes a novel model called BlockFL-IDS (Blockchain-based Federated Learning for Intrusion Detection System) that combines blockchain and deep learning approaches for effective intrusion detection. The BlockFL-IDS model consists of three key processes: efficient client selection, secure channel selection, and federated learning-based IDS. To streamline the complexity of federated learning, we employ Auction game theory to select efficient clients based on metrics such as trust, energy, bandwidth, and network conditions. Furthermore, we employ the Base Criterion Method (BCM), a multicriteria decision-making algorithm, for secure channel selection. BCM evaluates multiple criteria, including noise, path loss, channel quality, stability, trust, and fading, resulting in improved accuracy and reduced data loss in intrusion detection. For federated learning, we utilize the Optimized Back Propagation-based Deep Belief Network (OB-DBN), enabling the generation of both local and global models. The edge server generates local models, extracting packet-based features from client data for intrusion detection. Cloud servers aggregate these local models to create global models stored in a circular-based regression tree structure to enhance scalability and reduce retrieval time. The proposed OB-DBN algorithm calculates backpropagation error, facilitating loss reduction and weight updates. To evaluate the performance of the BlockFL-IDS model, we implement it using the NS-3.26 network simulator and assess its effectiveness using various performance metrics. Through our research, we aim to address security and privacy concerns in IoT environments, providing an innovative solution that enhances intrusion detection while preserving data privacy.

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Section: Literature Surveymentioning

confidence: 99%

Section: (Ii) Global Model Generationmentioning

confidence: 99%

Twin chain: A Blockchain based Federated Learning Intrusion Detection System usingOptimized Backpropagation based Neural Network for Edge Assisted IoT Networks

Narayanan

Paul

2023

Preprint

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“…The American pharmaceutical giant PharMerica disclosed that unknown actors accessed its system in March and extracted the personal data of 5.8 million individuals [10]. A ransomware hacker group named BlackCat threatened to leak 80 GB of confidential data they claimed to have stolen from Reddit servers in February [11]. According to a 2022 report by Waterfall Security Solutions, there were 57 cyber-attack incidents related to operational technology, impacting over 150 industrial operations [12].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Security and Flexibility in the Industrial Internet of Things: Blockchain-Based Data Sharing and Privacy Protection

Tong,

Yang,

et al. 2024

Sensors

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To address the complexities, inflexibility, and security concerns in traditional data sharing models of the Industrial Internet of Things (IIoT), we propose a blockchain-based data sharing and privacy protection (BBDSPP) scheme for IIoT. Initially, we characterize and assign values to attributes, and employ a weighted threshold secret sharing scheme to refine the data sharing approach. This enables flexible combinations of permissions, ensuring the adaptability of data sharing. Subsequently, based on non-interactive zero-knowledge proof technology, we design a lightweight identity proof protocol using attribute values. This protocol pre-verifies the identity of data accessors, ensuring that only legitimate terminal members can access data within the system, while also protecting the privacy of the members. Finally, we utilize the InterPlanetary File System (IPFS) to store encrypted shared resources, effectively addressing the issue of low storage efficiency in traditional blockchain systems. Theoretical analysis and testing of the computational overhead of our scheme demonstrate that, while ensuring performance, our scheme has the smallest total computational load compared to the other five schemes. Experimental results indicate that our scheme effectively addresses the shortcomings of existing solutions in areas such as identity authentication, privacy protection, and flexible combination of permissions, demonstrating a good performance and strong feasibility.

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“…The primary research method employed entails a rigorous exploration of peer-reviewed scholarly articles. Of particular significance is the seminal work [14], which serves as a pivotal reference point for comprehending the intricacies of secure data management within DAGs. This foundational source is instrumental in unraveling the complex web of data security considerations in the context of DAGs.…”

Section: Introductionmentioning

confidence: 99%

Development of Dag Blockchain Model

Tokhmetov,

Lee,

Tanchenko

2024

sjaitu

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In this study the authors present an innovative approach to resolving scalability and efficiency challenges in blockchain technology through the integration of Directed Acyclic Graphs (DAGs). This approach helps to overcome the limitations of traditional blockchain systems, particularly in transaction processing. The classic blockchain has some problems as slow transaction processing and poor scalability. The authors offer Directed Acyclic Graph (DAG) as a scalable and energy-efficient alternative. The paper outlines the development of a DAG-based blockchain model, utilizing Python and Flask alongside the Ed25519 cryptographic curve. It conducts a comparative analysis of DAG with traditional consensus mechanisms like Proof of Work and Proof of Stake, underscoring the efficiency and security benefits of employment of DAG. The research methodology includes an extensive literature review and the construction of a practical model to demonstrate DAG's applicability in blockchain networks. Particularly notable is the exploration of DAG's potential in Internet of Things (IoT) ecosystems, addressing critical issues such as energy inefficiency and network communication challenges in existing consensus algorithms. The authors calculated the performance of the model and compared it with similar models on several evaluation criteria. The simulation results of our proposed model show an improvement in performance and security by minimizing end-to-end delay, time cost, energy consumption, and throughput. The model eliminates the limitations of classic blockchain systems, such as high latency and low scalability. It structures transactions and blocks as a DAG, which provides fast validation and high scalability without compromising security. The research demonstrates the transformative implications of DAG for advancing blockchain technology.

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Secure data exchange in Industrial Internet of Things

Cited by 9 publications

References 13 publications

Twin chain: A Blockchain based Federated Learning Intrusion Detection System usingOptimized Backpropagation based Neural Network for Edge Assisted IoT Networks

Twin chain: A Blockchain based Federated Learning Intrusion Detection System usingOptimized Backpropagation based Neural Network for Edge Assisted IoT Networks

Enhancing Security and Flexibility in the Industrial Internet of Things: Blockchain-Based Data Sharing and Privacy Protection

Development of Dag Blockchain Model

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