Smart Contract Security Beyond Detection

TL;DR

This paper explores advanced research directions in smart contract security, emphasizing semantic reasoning, automated repair, adversarial robustness, and real-time exploit detection to enhance security measures.

Contribution

It presents a comprehensive research narrative connecting foundational models, formal guarantees, adversarial learning, and real-time detection for smart contract security.

Findings

Analyzes current limitations of smart contract security analyzers

Proposes a scalable real-time malicious transaction detection system

Connects recent studies to a unified research framework

Abstract

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.