IdenMultiSig: Identity-Based Decentralized Multi-Signature in Internet of Things

Liu, Han; Han, Dezhi; Cui, Mingming; Li, Kuan‐Ching; Souri, Alireza; Shojafar, Mohammad

doi:10.1109/tcss.2022.3232173

Cited by 19 publications

(5 citation statements)

References 33 publications

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“…In this paper, an intrusion detection model (RESNETCCN) is proposed that fuses traffic detection requirements. In our future work, we will introduce more new ideas such as blockchain cryptography [8], [18], [9], [19], [16], alliance chain [36], [7], [20], visual Q&A [5], [28], transformer [21], panoramic image [17], reinforcement learning [3], internet of things [23], [24], shared data [6] in our model.We will continue to explore network intrusion detection methods in more areas such as unsupervised and semi-supervised [2] areas for network anomalous traffic data detection. In addition, we also try to introduce new evaluation metrics and establish systematic evaluation methods of intrusion detection.…”

Section: Discussionmentioning

confidence: 99%

RESNETCNN: An abnormal network traffic flows detection model

Han

Cui

et al. 2023

ComSIS

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Intrusion detection is an important means to protect system security by detecting intrusions or intrusion attempts on the system through operational behaviors, security logs, and data audit. However, existing intrusion detection systems suffer from incomplete data feature extraction and low classification accuracy, which affects the intrusion detection effect. To this end, this paper proposes an intrusion detection model that fuses residual network(RESNET) and parallel cross-convolutional neural network, called RESNETCCN. RESNETCNN can efficiently learn various data stream features through the fusion of deep learning and convolutional neural network (CNN), which improves the detection accuracy of abnormal data streams in unbalanced data streams, moreover, the oversampling method into the data preprocessing, to extract multiple types of unbalanced data stream features at the same time, effectively solving the problems of incomplete data feature extraction and low classification accuracy of unbalanced data streams. Finally, three improved versions of RESNETCNN networks are designed to meet the requirements of different traffic data processing, and the highest detection accuracy reaches 99.98% on the CICIDS 2017 dataset and 99.90% on the ISCXIDS 2012 dataset.

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Section: Discussionmentioning

confidence: 99%

RESNETCNN: An abnormal network traffic flows detection model

Han

Cui

et al. 2023

ComSIS

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“…The decentralization of the blockchain ensures that smart contracts can be executed transparently and securely, and also makes the execution of smart contracts independent of any central authority or server. Smart contracts can also be used to flexibly embed various data and digital assets, thus achieving secure and decentralized on-chain information exchange and management [ 3 ].…”

Section: Related Workmentioning

confidence: 99%

“…Blockchain is a widely applied distributed database based on cryptography, with advantages such as immutability and decentralization [ 1 , 2 ]. Smart contracts are self-executing codes or protocols deployed on the blockchain [ 3 ]. Access control technology is one of the core techniques in information security.…”

Section: Introductionmentioning

confidence: 99%

A Blockchain-Based Trustworthy Model Evaluation Framework for Deep Learning and Its Application in Moving Object Segmentation

Jiang

et al. 2023

Sensors

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Model evaluation is critical in deep learning. However, the traditional model evaluation approach is susceptible to issues of untrustworthiness, including insecure data and model sharing, insecure model training, incorrect model evaluation, centralized model evaluation, and evaluation results that can be tampered easily. To minimize these untrustworthiness issues, this paper proposes a blockchain-based model evaluation framework. The framework consists of an access control layer, a storage layer, a model training layer, and a model evaluation layer. The access control layer facilitates secure resource sharing. To achieve fine-grained and flexible access control, an attribute-based access control model combining the idea of a role-based access control model is adopted. A smart contract is designed to manage the access control policies stored in the blockchain ledger. The storage layer ensures efficient and secure storage of resources. Resource files are stored in the IPFS, with the encrypted results of their index addresses recorded in the blockchain ledger. Another smart contract is designed to achieve decentralized and efficient management of resource records. The model training layer performs training on users’ servers, and, to ensure security, the training data must have records in the blockchain. The model evaluation layer utilizes the recorded data to evaluate the recorded models. A method in the smart contract of the storage layer is designed to enable evaluation, with scores automatically uploaded as a resource attribute. The proposed framework is applied to deep learning-based motion object segmentation, demonstrating its key functionalities. Furthermore, we validated the storage strategy adopted by the framework, and the trustworthiness of the framework is also analyzed.

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“…In the field of data storage, event logs are used to record important events that occur in the storage system, such as accesses, modifications, deletions, etc., for the purpose of auditing. Integrity monitoring is employed to prevent and detect data tampering, verifying the integrity of data through techniques like digital signatures [13]. Real-time monitoring involves ongoing checks on the storage system to promptly identify anomalies.…”

Section: Fundamental Technologies For Building Model Service Systems ...mentioning

confidence: 99%

“…To further enhance the security and efficiency of the system, integrating blockchain with other technologies has become a recent research focus. Common technologies include data encryption [11,12], auditing and monitoring [13,14], access control [15], and the InterPlanetary File System (IPFS) [16]. How to design and integrate specific techniques to create a secure, efficient, and highly applicable system that supports sustainable model services for deep learning is an issue worthy of in-depth research.…”

Section: Introductionmentioning

confidence: 99%

Toward Sustainable Model Services for Deep Learning: A Sub-Network-Based Solution Integrating Blockchain with IPFS and a Use Case in Intelligent Transportation

Jiang,

Li,

et al. 2023

Sustainability

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In the era of deep learning as a service, ensuring that model services are sustainable is a key challenge. To achieve sustainability, the model services, including but not limited to storage and inference, must maintain model security while preserving system efficiency, and be applicable to all deep models. To address these issues, we propose a sub-network-based model storage and inference solution that integrates blockchain and IPFS, which includes a highly distributed storage method, a tamper-proof checking method, a double-attribute-based permission management method, and an automatic inference method. We also design a smart contract to deploy these methods in the blockchain. The storage method divides a deep model into intra-sub-network and inter-sub-network information. Sub-network files are stored in the IPFS, while their records in the blockchain are designed as a chained structure based on their encrypted address. Connections between sub-networks are represented as attributes of their records. This method enhances model security and improves storage and computational efficiency of the blockchain. The tamper-proof checking method is designed based on the chained structure of sub-network records and includes on-chain checking and IPFS-based checking stages. It efficiently and dynamically monitors model correctness. The permission management method restricts user permission based on the user role and the expiration time, further reducing the risk of model attacks and controlling system efficiency. The automatic inference method is designed based on the idea of preceding sub-network encrypted address lookup. It can distribute trusted off-chain computing resources to perform sub-network inference and use the IPFS to store model inputs and sub-network outputs, further alleviating the on-chain storage burden and computational load. This solution is not restricted to model architectures and division methods, or sub-network recording orders, making it highly applicable. In experiments and analyses, we present a use case in intelligent transportation and analyze the security, applicability, and system efficiency of the proposed solution, particularly focusing on the on-chain efficiency. The experimental results indicate that the proposed solution can balance security and system efficiency by controlling the number of sub-networks, thus it is a step towards sustainable model services for deep learning.

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IdenMultiSig: Identity-Based Decentralized Multi-Signature in Internet of Things

Cited by 19 publications

References 33 publications

RESNETCNN: An abnormal network traffic flows detection model

RESNETCNN: An abnormal network traffic flows detection model

A Blockchain-Based Trustworthy Model Evaluation Framework for Deep Learning and Its Application in Moving Object Segmentation

Toward Sustainable Model Services for Deep Learning: A Sub-Network-Based Solution Integrating Blockchain with IPFS and a Use Case in Intelligent Transportation

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