SACM - Australia
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9648
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Item Restricted An AI-Driven, Secure, and Trustworthy Ranking System for Blockchain-Based Wallets(University of Technology Sydney, 2024-07-08) Almadani, Mwaheb; Farookh HussainThe significance of blockchain security has gained considerable interest as blockchain technologies grow in popularity. The spectacular rise in cryptocurrency values has also increased the adoption of blockchain-based wallets(BW/BWs). This tendency emphasizes the need for comprehensive security measures to protect digital assets, maintain transaction integrity and preserve trust in the blockchain networks. The most critical concern surrounding blockchain-based wallets is managing users' private keys, which are essential for authorizing transactions and accessing the digital cryptocurrencies stored in the blockchain network. In recent years, malicious actors have increased efforts to compromise these private keys and take control of the BW's digital assets. Therefore, ensuring the security of private keys through rigorous security protocols is paramount to defend against unauthorized access and potential financial losses. This thesis aims to investigate the integration of hard security, such as authentication techniques and access controls, and soft security measures, such as trust models and ranking systems, in the context of BWs. By incorporating tangible physical defenses (hard security) with intangible procedural strategies (soft security), we present a comprehensive framework for enhancing BW solution security and trustworthiness. This is essential for the widespread adoption and use of blockchain technology in financial transactions and digital asset management. This thesis proposes a secure, intelligent, and trustworthy approach for BW solutions that incorporates 2FA and MFA as hard security measures and an AI-driven ranking system as soft security measures. We have developed a BW website (BWW) with four authentication mechanisms, including different factors such as TOTP and biometrics through facial recognition, allowing BW users to choose their preferred level of security. The BWW remarkably improves the security of BW solutions by defending them against various threats, including sophisticated cyber-attacks, unauthorized access and human-caused weaknesses. Moreover, We introduce a trust-based ranking system (TBW-RAnk) for BW solutions that transparently ranks the BW solutions according to several objective and trusted criteria. TBW-RAnk is built using three AI models, namely the random forest classifier (RFC), the support vector classifier (SVC) and deep neural network (DNN). It has two modes: general and customized for a comprehensive and accurate assessment and recommendation for BW users. Consequently, BW users can make informed decisions and increase their security within the blockchain ecosystem. The proposed approach enhances the security and trustworthiness of BWs and increases their acceptance in the market.41 0