Blockchain is a revolutionary technology that enables users to communicate in a trust-less manner. It revolutionizes the modes of business between organizations without the need for a trusted third party. It is a distributed ledger technology based on a decentralized peer-to-peer (P2P) network. It enables users to store data globally on thousands of computers in an immutable format and empowers users to deploy small pieces of programs known as smart contracts. The blockchain-based smart contract enables auto enforcement of the agreed terms between two untrusted parties. There are several security vulnerabilities in Ethereum blockchain-based smart contracts, due to which sometimes it does not behave as intended. Because a smart contract can hold millions of dollars as cryptocurrency, so these security vulnerabilities can lead to disastrous losses. In this paper, a systematic review of the security vulnerabilities in the Ethereum blockchain is presented. The main objective is to discuss Ethereum smart contract security vulnerabilities, detection tools, real life attacks and preventive mechanisms. Comparisons are drawn among the Ethereum smart contract analysis tools by considering various features. From the extensive depth review, various issues associated with the Ethereum blockchain-based smart contract are highlighted. Finally, various future directions are also discussed in the field of the Ethereum blockchain-based smart contract that can help the researchers to set the directions for future research in this domain.

Optimization is a buzzword, whenever researchers think of engineering problems. This paper presents a new metaheuristic named dingo optimizer (DOX) which is motivated by the behavior of dingo (Canis familiaris dingo). The overall concept is to develop this method involving the collaborative and social behavior of dingoes. The developed algorithm is based on the hunting behavior of dingoes that includes exploration, encircling, and exploitation. All the above prey hunting steps are modeled mathematically and are implemented in the simulator to test the performance of the proposed algorithm. Comparative analyses are drawn among the proposed approach and grey wolf optimizer (GWO) and particle swarm optimizer (PSO). Some of the well-known test functions are used for the comparative study of this work. The results reveal that the dingo optimizer performed significantly better than other nature-inspired algorithms.

Blockchain technology and its applications are gaining popularity day by day. It is a ground-breaking technology that allows users to communicate without the need of a trusted middleman. A smart contract (self-executable code) is deployed on the blockchain and auto executes due to a triggering condition. In a no-trust contracting environment, smart contracts can establish trust among parties. Terms and conditions embedded in smart contracts will be imposed immediately when specified criteria have been fulfilled. Due to this, the malicious assailants have a special interest in smart contracts. Blockchains are immutable means if some transaction is deployed or recorded on the blockchain, it becomes unalterable. Thus, smart contracts must be analyzed to ensure zero security vulnerabilities or flaws before deploying the same on the blockchain because a single vulnerability can lead to the loss of millions. For analyzing the security vulnerabilities of smart contracts, various analysis tools have been developed to create safe and secure smart contracts. This paper presents a systematic review on Ethereum smart contracts analysis tools. Initially, these tools are categorized into static and dynamic analysis tools. Thereafter, different sources code analysis techniques are studied such as taint analysis, symbolic execution, and fuzzing techniques. In total, 86 security analysis tools developed for Ethereum blockchain smart contract are analyzed regardless of tool type and analysis approach. Finally, the paper highlights some challenges and future recommendations in the field of Ethereum smart contracts.

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Data is the key to measuring educational effectiveness promptly. But data and education are trapped and siloed across centralized systems, causing information discrepancies and inaccuracies. This has caused countless delayed opportunities, academic credential disagreements, and never-ending confusion around learning potency. This lack of transparency, despite having its fair share of usefulness, has also been quite burdensome. To alleviate this issue, our team has developed a blockchain protocol that verifies professional certifications that have been earned both locally and through another well-established online educational portal. This system allows accuracy, reliability, and immutability that has never been implemented. This foundation of clear, verified data will then be used further to power blockchain-based applications. The result is that our attempt at a versatile, holistic, and decentralized view of educational performance ensures the best e-learning outcome for students and teachers alike.