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BLOCKCHAIN-BASED SECURE SENSING DATA PROCESSING AND LOGGING

Aldyaflah, Izdehar Mahmoud
http://orcid.org/0009-0006-4572-7977
http://rave.ohiolink.edu/etdc/view?acc_num=csu1718889789399438

Abstract Details

2024, Doctor of Engineering, Cleveland State University, Washkewicz College of Engineering.
This dissertation research investigated how to use the blockchain technology to secure sensor data processing and logging. The research was done in three phases. First, to ensure the legitimate of the sensor to log data into Blockchain, sensor identifcation and authentication mechanism is used where only the defned sensors sensing data are accepted. Second, to minimize the throughput demand on large public blockchain such as Bitcoin and Ethereum and the fnancial cost of using blockchain services, only a small amount of raw sensing data are placed on the blockchain through an aggregation process, where a group of raw sensing data is converted to one condensed data time. A Merkle tree based mechanism is used to protect the security of the of-chain data (raw sensing data) with the condensed Data placed on the blockchain. The system was tested with the IOTA Shimmer test network, and the Ethereum test network. The second phase focuses on developing an Ethereum smart contract to manage access control for storing and retrieving condensed data on the blockchain. The smart contract introduces three levels of authorization (read, write, and admin) to regulate data access securely. Gas consumption optimization is achieved through a tag-based secure data-store mechanism embedded in the smart contract design. In the fnal phase, a deep learning model using Convolution Neural Networks (CNN) is introduced to detect vulnerabilities in smart contracts. Four input techniques—Word2Vec, FastText, Bag of Words (BoW), and TF-IDF—are compared for their efectiveness in identifying six types of vulnerabilities. TF-IDF emerges as the most efcient input technique, consistently achieving high detection rates (90% to 100%) across all vulnerability types. In particular, TF-IDF excels in detecting the Reentrancy vulnerability, achieving performance metrics of 96% to 97%. Word2Vec and FastText performed comparably with slight changes, however BoW consistently dropped behind, attaining the lowest performance across all vulnerability types, ranging from 72% to 78%. The accuracy of multiclass experiment among the four input types ranging from 84.5% to 96%. Also, the model in this research compared to other models on two separate vulnerabilities namely are Reentrancy and Timestamp. According to the comparison results, this model with TF-IDF has obtained the maximum performance ranging from 96% to 97% in detecting the Reentrancy vulnerability on all performance metrics
Wenbing Zhao (Advisor)
Timothy V Arndt (Committee Member)
Hongkai Yu (Committee Member)
Lili Dong (Committee Member)
Sun S. Chung (Committee Member)
185 p.
Computer Engineering; Computer Science
Blockchain, smart contract, Ethereum, wireless sensors, smart contract vulnerabilities, detect vulnerabilities, deep learning, convolution neural networks, CNN.

Recommended Citations

Citations

  • Aldyaflah, I. M. (2024). BLOCKCHAIN-BASED SECURE SENSING DATA PROCESSING AND LOGGING [Doctoral dissertation, Cleveland State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=csu1718889789399438

    APA Style (7th edition)

  • Aldyaflah, Izdehar. BLOCKCHAIN-BASED SECURE SENSING DATA PROCESSING AND LOGGING. 2024. Cleveland State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=csu1718889789399438.

    MLA Style (8th edition)

  • Aldyaflah, Izdehar. "BLOCKCHAIN-BASED SECURE SENSING DATA PROCESSING AND LOGGING." Doctoral dissertation, Cleveland State University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=csu1718889789399438

    Chicago Manual of Style (17th edition)

csu1718889789399438
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© 2024, some rights reserved.Creative Commons License BLOCKCHAIN-BASED SECURE SENSING DATA PROCESSING AND LOGGING by Izdehar Mahmoud Aldyaflah is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Cleveland State University and OhioLINK.