This paper proposes and evaluates a novel real-time cybersecurity framework integrating artificial intelligence (AI) and blockchain technology to enhance the detection and auditability of cyber threats. Traditional cybersecurity approaches often lack transparency and robustness in logging and verifying AI-generated decisions, hindering forensic investigations and regulatory compliance. To address these challenges, we developed an integrated solution combining a convolutional neural network (CNN)-based anomaly detection module with a permissioned Ethereum blockchain to securely log and immutably store AI-generated alerts and relevant metadata. The proposed system employs smart contracts to automatically validate AI alerts and ensure data integrity and transparency, significantly enhancing auditability and forensic analysis capabilities. To rigorously test and validate our solution, we conducted comprehensive experiments using the CICIDS2017 dataset and evaluated the system’s detection accuracy, precision, recall, and real-time responsiveness. Additionally, we performed penetration testing and security assessments to verify system resilience against common cybersecurity threats. Results demonstrate that our AI-blockchain integrated solution achieves superior detection performance while ensuring real-time logging, transparency, and auditability. The integration significantly strengthens system robustness, reduces false positives, and provides clear benefits for cybersecurity management, especially in regulated environments. This paper concludes by outlining potential avenues for future research, particularly extending blockchain scalability, privacy enhancements, and optimizing performance for high-throughput cybersecurity applications.

The Internet of Things (IoT) has recently attracted much interest from researchers due to its diverse IoT applications. However, IoT systems encounter additional security and privacy threats. Developing an efficient IoT system is challenging because of its sophisticated network topology. Effective access control is required to ensure user privacy in the Internet of Things. Traditional access control methods are inappropriate for IoT systems because most conventional access control approaches are designed for centralized systems. This paper proposes a decentralized access control framework based on smart contracts with three parts: initialization, an access control protocol, and an inspection. Smart contracts are used in the proposed framework to store access control policies safely on the blockchain. The framework also penalizes users for attempting unauthorized access to the IoT resources. The smart contract was developed using Remix and deployed on the Ropsten Ethereum testnet. We analyze the performance of the smart contract-based access policies based on the gas consumption of blockchain transactions. Further, we analyze the system’s security, usability, scalability, and interoperability performance.

Recent advances in artificial intelligence, edge computing, and big data have enabled extensive reasoning capabilities at the edge of the network. Edge servers are now capable of extracting meaningful intelligence from IoT nodes, which can benefit a very diverse set of IoT applications, including smart carrier and distribution networks (power, people, water, and food), smart agriculture and manufacturing, and healthcare and maintenance. Unfortunately, as the infrastructures become more intelligent, they also become more vulnerable to disruption due to cyberattacks and information leakage. Furthermore, the rich data gathering and analytics involved in driving the intelligent management substantially raise the stakes in terms of privacy violation of the people and organizations that it serves.

Generative artificial intelligence (AI) and persistent empirical gaps are reshaping the cyber threat landscape faster than Zero-Trust Architecture (ZTA) research can respond. We reviewed 10 recent ZTA surveys and 136 primary studies (2022–2024) and found that 98% provided only partial or no real-world validation, leaving several core controls largely untested. Our critique, therefore, proceeds on two axes: first, mainstream ZTA research is empirically under-powered and operationally unproven; second, generative-AI attacks exploit these very weaknesses, accelerating policy bypass and detection failure. To expose this compounding risk, we contribute the Cyber Fraud Kill Chain (CFKC), a seven-stage attacker model (target identification, preparation, engagement, deception, execution, monetization, and cover-up) that maps specific generative techniques to NIST SP 800-207 components they erode. The CFKC highlights how synthetic identities, context manipulation and adversarial telemetry drive up false-negative rates, extend dwell time, and sidestep audit trails, thereby undermining the Zero-Trust principles of verify explicitly and assume breach. Existing guidance offers no systematic countermeasures for AI-scaled attacks, and that compliance regimes struggle to audit content that AI can mutate on demand. Finally, we outline research directions for adaptive, evidence-driven ZTA, and we argue that incremental extensions of current ZTA that are insufficient; only a generative-AI-aware redesign will sustain defensive parity in the coming threat cycle.