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@article{196658,
        author = {Lavkush and Bhole Nath and Chandra Shekhar Nishad and Vikas Kannaujiya and Prayag Raj},
        title = {AI-Driven Vulnerability Detection in Blockchain Smart Contracts},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {3646-3649},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196658},
        abstract = {Blockchain technology has transformed digital transactions by enabling decentralized and transparent systems. Smart contracts, which are self-executing programs running on blockchain networks, automate agreements without intermediaries. However, smart contracts often contain vulnerabilities that can lead to severe financial losses and security breaches. Traditional vulnerability detection methods are often slow and limited in scalability.
Artificial Intelligence (AI), particularly machine learning and deep learning techniques, has emerged as a promising solution for detecting vulnerabilities in smart contracts. This paper explores the role of AI in identifying vulnerabilities in blockchain smart contracts, discusses common security issues, analyzes AI- based detection techniques, and evaluates their advantages and challenges.},
        keywords = {Artificial Intelligence, Blockchain Security, Smart Contracts, Vulnerability Detection, Machine Learning.},
        month = {April},
        }