Global Journal of Computer Science and Technology, D: Neural & Artificial Intelligence, Volume 23 Issue 2

The attackers took advantage of this vulnerability by manipulating Tellor oracles to provide incorrect values for ALBT tokens. This allowed them to borrow significant amounts of BEUR stablecoins from the Bonq DAO platform at an exceptionally low cost. Subsequently, the hackers drained the pool of ALBT tokens used as collateral for BEUR loans. This action caused the value of the ALBT token to plummet, further reducing the cost of their borrowing. While the attackers ultimately repaid the BEUR loans, they retained the ALBT tokens. This incident resulted in a loss of approximately $120 million for the Bonq DAO platform. The Bonq DAO vulnerability posed a substantial challenge for the project. Nevertheless, the team has taken measures to enhance platform security, including diversifying the use of different divination services and implementing new security measures v. Euler Finance Hack Euler Finance is a decentralized finance (DeFi) platform that facilitates cryptocurrency borrowing and lending through the use of smart contracts, streamlining the lending process. However, on March 13, 2023, Euler Finance experienced a security breach resulting in the loss of approximately $196.9 million worth of cryptocurrencies. The attackers exploited vulnerabilities within Euler Finance's smart contracts related to revenue management. In Euler Finance, a 'call' is a notification requiring borrowers to add additional collateral to their loans. Failure to do so can result in the lender freezing the borrower's position. Hackers capitalized on this vulnerability by sending a large number of 'calls' to the Euler Finance smart contract, causing it to enter a state where it could no longer process any further calls. This effectively granted hackers access to the Euler Finance platform. The Euler Finance hack represented a significant setback for the project. However, the team managed to recover the majority of the stolen funds. iii. A rtificial I ntelligence for S mart C ontract S ecurity Artificial Intelligence (AI) plays a pivotal role in enhancing smart contract security by providing advanced tools and techniques to identify vulnerabilities, detect anomalies, and mitigate risks. This integration of smart contracts with AI has the potential to revolutionize various industries and domains, spanning from finance and healthcare to logistics and energy. By harnessing the combined power of smart contracts and AI, developers can create applications that are more efficient, secure, and autonomous, enabling innovative business models and services. For instance, AI can enhance the adaptability of smart contracts by incorporating logic, neural graphs, and neural networks². This fusion of technologies has the potential to significantly reduce the manpower required to manage both contracts and the entire contracting process, adding substantial value to organizations. AI offers a wide array of applications within the realm of smart contracts. It can be directly integrated into smart contract code or utilized to validate and ensure contract integrity. Furthermore, the combination of AI techniques with deep learning concepts, such as Tensor, holds promise for advancing blockchain-based smart contracts. Additionally, cognitive computing, a subset of AI, aims to emulate human thought processes within computing infrastructure. a) AI for Testing and Evaluation of Smart Contracts AI can play a crucial role in testing smart contracts through various methods, encompassing performance testing, vulnerability detection, and correctness evaluation. By harnessing AI as a utility service for blockchain, the performance of blockchain- based smart contracts can be significantly enhanced, marking a substantial contribution of AI to the field of blockchain technology. In a study by Marwala et al. [39], the utilization of AI for verifying smart contracts was discussed. The authors highlighted the potential advantages of applying AI to blockchain-based smart contracts, which include heightened security and scalability. Furthermore, they emphasized the feasibility of employing AI-based formal verification techniques to assess the correctness of smart contracts. b) Federated Learning Federated learning is an innovative approach to collaborative and decentralized learning, aligning well with the decentralization capabilities of blockchain technology. In this approach, training data remains secure and private, making it particularly valuable in scenarios involving sensitive information, such as healthcare data. By combining federated learning with blockchain, various functionalities, including data access control and enhanced privacy preservation, can be achieved. In a study by Lu et al. [40], a novel privacy preservation mechanism for industrial IoT was proposed, leveraging a combination of federated learning and blockchain. They integrated federated learning into the consensus process, resulting in improved computing resource consumption and operational efficiency. However, challenges persist, particularly in addressing resource constraints within computing infrastructure, necessitating a deeper exploration of data privacy requirements. In another work by Kang et al. [41], a federated learning system based on a consortium blockchain was presented. The authors introduced an incentive mechanism based on contract theory to evaluate workers with a high reputation for reliable training, thus © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue II Version I 63 ( )D Year 2023 Strengthening Smart Contracts: An AI-Driven Security Exploration