Smart Contract Security Beyond Detection
For educators and students in smart contract security, this provides a structured framework for capstone projects, but it is an incremental synthesis of existing research directions.
This paper develops a capstone-oriented research narrative for smart contract security, moving beyond detection to include semantic reasoning, automated repair, adversarial robustness, and real-time exploit detection, and connects these to recent studies to help students formulate technically grounded capstone projects.
Smart contract security has progressed from vulnerability detection toward a broader research agenda that includes semantic reasoning, automated repair, adversarial robustness, and real-time exploit detection. This paper develops a capstone-oriented research narrative around four directions: foundation-model-based smart contract semantics and vulnerability reasoning [1], automated smart contract repair with formal guarantees [2], adversarial learning for robust malicious contract and transaction detection [3], and real-time transaction-level exploit detection at blockchain scale [4]. We connect these directions to two recent studies that characterize the current frontier: a diagnostic analysis of where smart contract security analyzers fall short [5] and a scalable real-time system for malicious Ethereum transaction detection [6]. The resulting framework is intended to help students formulate capstone projects that are technically grounded, empirically measurable, and aligned with contemporary smart contract security research.