AI Cyber-Chain: Combining AI and Blockchain for Improved Cybersecurity

Abstract

Cybersecurity threats are growing in scale and complexity, posing serious risks to digital infrastructure, data privacy, and economic stability worldwide. Old-style security systems often encounter challenges against advanced and coordinated cyber-attacks, particularly when they rely on centralized architectures that create single points of failure. To tackle this challenge, we introduce AICyber-Chain, a novel AI-integrated model with blockchain technology to enhance threat detection, secure data sharing, and provide tamper-proof auditability. In our approach, AI-driven algorithms analyze vast streams of network data to detect anomalies and evolving threats in real time, while blockchain ensures the integrity, transparency, and decentralization of security events and data flows. Smart contracts are employed to automate adaptive security responses, enabling systems to dynamically isolate risks and enforce security policies without human delay. Experiments using Ethereum’s test network demonstrate that AICyber-Chain achieves 1.8× faster authentication and reduces gas consumption by up to 25% compared to centralized methods. Furthermore, the system shows superior resilience against DDoS attacks, scalability in enterprise environments, and financial incentives that encourage data and security rule sharing across participants. These findings underscore how combining AI and blockchain can substantially improve cybersecurity, offering a scalable, transparent, and intelligent defense framework for future digital ecosystems.

Introduction

Cybersecurity has become a critical issue as cyber threats grow in complexity and scale, impacting sensitive data, critical infrastructure, and trust in digital systems. Traditional defenses rely on centralized, rule-based systems that are vulnerable to sophisticated attacks and single points of failure. Artificial intelligence (AI) improves threat detection but faces challenges in data security, trust, and centralization.

To address these issues, the study proposes AICyber-Chain, a novel cybersecurity framework that integrates AI-driven threat detection with blockchain’s decentralized, immutable ledger. By recording security events as blockchain transactions, the system ensures tamper-proof logs and uses smart contracts to automate rapid responses, reducing human delay. Early tests show improved authentication speed, reduced resource costs, and enhanced resilience against attacks like DDoS.

The literature review highlights previous research combining AI and blockchain in secure data sharing, IoT security, adaptive encryption, federated learning, explainable AI, and supply chain security, which informed the design of AICyber-Chain.

Methodology:

• Data Network Construction: Security data (logs, user activity, etc.) is modeled as a graph to capture interactions.

• AI Threat Detection: Uses supervised, unsupervised, and reinforcement learning to detect known and novel threats in real time.

• Blockchain Integrity: Security events are stored immutably on a decentralized ledger to prevent tampering.

• Smart Contracts: Automate security policy enforcement (e.g., isolate compromised nodes, trigger multi-factor authentication).

• Collaborative Sharing: Incentivizes data and rule sharing among participants, fostering collective defense.

• Training & Optimization: AI models are trained on historical data with continuous updates.

• Multi-Level Security Analysis: Detects threats at entity, transaction, and group levels to capture both isolated and coordinated attacks.

• Scalability: Designed to support enterprise/cloud environments with real-time monitoring and efficient throughput.

Discussion:

AICyber-Chain’s combination of AI, blockchain, and smart contracts offers a decentralized, adaptive, and automated cybersecurity approach that outperforms traditional centralized systems. It enhances detection, response speed, data integrity, and collaborative defense, making it robust against evolving cyber threats.

Challenges remain in scalability, AI interpretability, and privacy balancing, requiring further research to refine consensus mechanisms, explainable AI integration, and cryptographic safeguards.

Conclusion

The escalating scale and the heightened sophistication of cyber threads demand more intelligent, decentralized, and trustworthy solutions—AI Cyber-Chain represents a significant step in this direction. By combining the adaptability of artificial intelligence with the transparency and immutability of blockchain technology, the framework addresses critical shortcomings of conventional cybersecurity systems. Its layered approach—encompassing AI-driven anomaly detection, blockchain-based data integrity, and smart contract–enabled automated responses—offers a more resilient and proactive defense against modern cyberattacks. This work has highlighted how AI Cyber-Chain advances beyond traditional models by ensuring tamper-proof event logging, collaborative intelligence sharing, and automated mitigation of threats in real time. Experimental results on blockchain test networks confirm improvements in authentication efficiency, resource optimization, and resilience against distributed attacks. Moreover, the system demonstrates that incentivized collaboration can help overcome the fragmentation of threat intelligence across organizations. Nevertheless, important challenges remain. Issues of scalability, privacy preservation, and explainability of AI decisions continue to limit deployment at enterprise scale. Resolving these challenges will require innovations in lightweight consensus algorithms, integration of privacy-aware techniques such as federated learning, and the adoption of explainable AI models. Looking ahead, cooperation between researchers, industry practitioners, and policymakers will be essential to translate frameworks like AI Cyber-Chain into operationally viable security infrastructures. Ultimately, AI Cyber-Chain is not merely a technological advancement—it signals a paradigm shift toward transparent, collaborative, and intelligent cybersecurity ecosystems capable of meeting the demands of a rapidly evolving digital world.

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Copyright

Copyright © 2025 Vyshnavi M, Pankaj Yadav, Satyam Kumar , Saurav Shanker, Shubh Chadha. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.