AI-Enabled Threat Detection in Blockchain-Based Systems
DOI:
https://doi.org/10.63345/sjaibt.v1.i2.101Keywords:
Blockchain, Artificial Intelligence, Threat Detection, Cybersecurity, Anomaly Detection, Deep Learning, Federated Learning, Consensus SecurityAbstract
Blockchain technology has emerged as a foundational layer for decentralized, tamper-resistant, and trustless systems, widely deployed in financial services, supply chain networks, healthcare, and digital governance. However, the growth of blockchain ecosystems has been paralleled by increasingly sophisticated cyber threats that challenge network integrity, privacy, and security. Traditional security approaches are limited in scalability and adaptability, especially against dynamic adversarial tactics such as zero-day exploits, collusion-based fraud, Sybil attacks, or adversarial consensus manipulation. Artificial Intelligence (AI), particularly machine learning (ML) and deep learning (DL), presents a promising paradigm for augmenting blockchain security. By leveraging predictive analytics, anomaly detection, federated learning, and graph neural networks, AI can identify hidden patterns, anticipate threats, and enable proactive defense mechanisms in real time.
This manuscript presents a comprehensive study of AI-enabled threat detection mechanisms in blockchain-based systems. It begins with a detailed overview of blockchain vulnerabilities, followed by a literature review covering state-of-the-art AI-driven detection strategies. The proposed methodology emphasizes hybrid approaches that combine supervised, unsupervised, and reinforcement learning models with blockchain’s inherent consensus and auditability. A statistical simulation experiment is conducted to evaluate threat detection rates, false positives, latency, and scalability in a blockchain test network infused with AI-based monitoring agents. Results demonstrate that AI-enhanced systems improve detection accuracy by 35–60%, reduce latency by 40%, and significantly outperform rule-based models in identifying novel attack patterns.
The study concludes that AI-enabled threat detection provides a crucial pathway for resilient, adaptive, and self-learning blockchain ecosystems. Yet, challenges persist in data privacy, adversarial AI attacks, resource efficiency, and ethical governance. This work contributes to both academic discourse and industrial practice, offering recommendations for designing next-generation blockchain architectures secured by intelligent threat detection frameworks.
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