Mutation testing is a technique to assess the effectiveness of test suites by introducing artificial faults into programs. Although mutation testing plugins are available for many platforms and languages, none is currently available for Remix-IDE, the most widely used Integrated Development Environment for the entire contract development journey, used by users of all knowledge levels, and serves as a learning lab for teaching and experimenting with Ethereum. The quality and security of smart contracts are crucial in blockchain systems, as even minor issues can result in substantial financial losses. This paper proposes MuSe, a mutation testing plugin for the Remix-IDE. MuSe includes traditional, Solidity-specific, and security-oriented mutation operators. Its integration into the Remix-IDE eliminates the need for additional setup and lowers the entry barrier. As a result, developers and researchers can immediately leverage mutation testing to assess the effectiveness of their test suites and identify potential issues in smart contracts. We provide a demo video showing MuSe: https://www.youtube.com/watch?v=MIFk9exTDu0 and its repository: https://github.com/GerardoIuliano/MuSe-Remix-Plugin.

The security of smart contracts is critical in blockchain systems, where even minor vulnerabilities can lead to substantial financial losses. Researchers proposed several vulnerability detection tools evaluated using existing benchmarks. However, most benchmarks are outdated and focus on a narrow set of vulnerabilities. This work evaluates whether mutation seeding can effectively inject vulnerabilities into Solidity-based smart contracts and whether state-of-the-art static analysis tools can detect the injected flaws. We aim to automatically inject vulnerabilities into smart contracts to generate large and wide benchmarks. We propose MuSe, a tool to generate vulnerable smart contracts by leveraging pattern-based mutation operators to inject six vulnerability types into real-world smart contracts. We analyzed these vulnerable smart contracts using Slither, a static analysis tool, to determine its capacity to identify them and assess their validity. The results show that each vulnerability has a different injection rate. Not all smart contracts can exhibit some vulnerabilities because they lack the prerequisites for injection. Furthermore, static analysis tools fail to detect all vulnerabilities injected using pattern-based mutations, underscoring the need for enhancements in static analyzers and demonstrating that benchmarks generated by mutation seeding tools can improve the evaluation of detection tools.

IoT devices generate and exchange large amounts of data daily, creating significant security and privacy challenges. Security testing, particularly using Machine Learning (ML), helps identify and classify potential malicious network traffic. Previous research has shown how ML can aid in designing security tests for IoT attacks. This ongoing paper introduces a search-based approach using Genetic Algorithms (GAs) to evolve detection rules and detect intrusion attacks. We build on existing GA methods for intrusion detection and compare them with leading ML models. We propose 17 detection rules and demonstrate that while GAs do not fully replace ML, they perform well with ample attack examples and enhance the usability and implementation of deterministic test cases by security testers.