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Submitted
20 April 2025
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10 June 2025
Published
30 June 2025
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Abstract

Rapid digital ecosystem growth has made cybersecurity a major issue nowadays. As gadgets, cloud platforms, and critical infrastructures become more interconnected, fraudsters may exploit weaknesses with unparalleled sophistication. Advanced threats including ransomware, deepfake-driven phishing, supply-chain breaches, and AI-powered assaults are beyond firewalls and intrusion detection systems. This paper presents a hybrid cybersecurity system that uses AI, blockchain, and Zero Trust to anticipate, prevent, and mitigate intrusions in real time. Our system uses machine learning to identify anomalies and decentralized, blockchain-based trust management to safeguard data and authentication. A proactive strategy improves detection accuracy, decreases false positives, and builds resistance to emerging threats. Trials utilizing benchmark intrusion detection datasets show that the framework outperforms standard systems. Its use in high-risk industries including banking, healthcare, and industrial IoT is shown by the results. For a safer digital future, our study develops adaptable, intelligent, and scalable cyber protection methods.

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Cybersecurity Artificial intelligence Blockchain Zero trust Anomaly detection Threat prediction

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Copyright (c) 2025 Muna A. Radhi, Majd S. Ahmed, Ethar Abdul Wahhab Hachim, Zeyad Farooq Lutfi (Author)

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M. A. . Radhi, M. S. . Ahmed, E. Abdul Wahhab Hachim, and Z. Farooq Lutfi, “Emerging Trends and AI-Driven Defense Mechanisms in Cybersecurity: A Novel Framework for Threat Prediction and Prevention”, Cybersys. J, vol. 2, no. 1, pp. 10–21, Jun. 2025, doi: 10.57238/csj.2025.1002.
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[1]
M. A. . Radhi, M. S. . Ahmed, E. Abdul Wahhab Hachim, and Z. Farooq Lutfi, “Emerging Trends and AI-Driven Defense Mechanisms in Cybersecurity: A Novel Framework for Threat Prediction and Prevention”, Cybersys. J, vol. 2, no. 1, pp. 10–21, Jun. 2025, doi: 10.57238/csj.2025.1002.
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References

  1. M. A. Aleisa, “Blockchain-Enabled Zero Trust Architecture for Privacy-Preserving Cybersecurity in IoT Environments,” IEEE Access, vol. 13, pp. 18660–18676, 2025, doi: 10.1109/ACCESS.2025.3529309.
  2. S. Al-Otaibi, S. Ayouni, N. Sarwar, A. Irshad, and F. Ullah, “AI-driven security framework for medical sensor networks: enhancing privacy and trust in smart healthcare systems,” Cluster Computing, vol. 28, no. 6, art. no. 408, 2025, doi: 10.1007/s10586-024-05049-3.
  3. D. Schönle and C. Reich, Evaluation of AI Attack Mitigation: From Citrix Bleed to Self-Evolving Malware: Modernising Aerospace Cyber Defence with AI, SSRN, 2025.
  4. A. A. Laghari, A. A. Khan, A. Ksibi, F. Hajjej, N. Kryvinska, A. Almadhor, et al., “A novel and secure artificial intelligence enabled zero trust intrusion detection in industrial internet of things architecture,” Scientific Reports, vol. 15, no. 1, art. no. 26843, 2025.
  5. C. Zhang, D. Jia, L. Wang, W. Wang, F. Liu, and A. Yang, “Comparative research on network intrusion detection methods based on machine learning,” Computers & Security, vol. 121, art. no. 102861, 2022. doi: 10.1016/j.cose.2022.102861.
  6. K. Zakhmi, A. Ushmani, M. R. Mohanty, S. Agrawal, A. Banduni, and S. R. Kakatum, “Evolving Zero Trust Architectures for AI-Driven Cyber Threats in Healthcare and Other High-Risk Data Environments: A Systematic Review,” Cureus, vol. 17, no. 6, e15532, 2025, doi: 10.7759/cureus.85446.
  7. M. H. Bashaa, W. S. Bhaya, and N. H. K. Al-aaraji, “Integration of Zero Trust Architecture and Machine Learning for Improving the Security of Software Defined Networking: A Review,” Journal of Intelligent Informatics, Networking, and Cybersecurity, vol. 1, no. 1, art. no. 1, 2025. [Online]. Available: https://jiinc.uobabylon.edu.iq/journal/vol1/iss1/1
  8. M. A. Azad, S. Abdullah, J. Arshad, H. Lallie, and Y. H. Ahmed, “Verify and trust: A multidimensional survey of zero-trust security in the age of IoT,” Internet of Things, vol. 27, art. no. 101227, 2024. doi: 10.1016/j.iot.2024.101227.
  9. A. Singh, V. Pareek, and A. Sharma, “Blockchain-Enabled Zero Trust Framework for Securing FinTech Ecosystems Against Insider Threats and Cyber Attacks,” arXiv preprint, arXiv:2507.19976, 2025.
  10. L. Alevizos, “Automated cybersecurity compliance and threat response using AI, blockchain and smart contracts,” International Journal of Information Technology, vol. 17, no. 2, pp. 767–781, 2025.
  11. L. Alevizos, V. T. Ta, and M. H. Eiza, “Augmenting zero trust architecture to endpoints using blockchain: A state-of-the-art review,” Security and Privacy, vol. 5, no. 1, 2022, doi: 10.1002/spy2.333.
  12. M. Hasan, "Enhancing Enterprise Security with Zero Trust Architecture," arXiv preprint arXiv:2410.18291, 2024, doi: 10.48550/arXiv.2410.18291.
  13. P. Steichen, “Artificial Intelligence and Cybersecurity: Navigating a Double-Edged Relationship,” in The Routledge Handbook of Artificial Intelligence and International Relations, London: Routledge, 2025, pp. 223–242, doi: 10.4324/9781003518495-22.
  14. S. A. Syed, “Adversarial AI and cybersecurity: Defending against AI-powered cyber threats,” Iconic Research And Engineering Journals, vol. 8, no. 9, pp. 1030–1041, 2025.
  15. F. Mohammad, S. Al-Ahmadi, and J. Al-Muhtadi, “OMD-RAS: Optimizing Malware Detection through Comprehensive Approach to Real-Time and Adaptive Security,” Computers, Materials & Continua, vol. 84, no. 3, pp. 5995–6014, 2025, doi: 10.32604/cmc.2025.063046
  16. I. Kolawole, “Leveraging Cloud-based AI and Zero Trust Architecture to Enhance U.S. Cybersecurity and Counteract Foreign Threats,” World Journal of Advanced Research and Reviews, vol. 25, no. 3, pp. 006–025, 2025, doi: 10.30574/wjarr.2025.25.3.0635
  17. A. A. Laghari, A. A. Khan, A. Ksibi, F. Hajjej, N. Kryvinska, A. Almadhor, et al., “A novel and secure artificial intelligence enabled zero trust intrusion detection in industrial internet of things architecture,” Scientific Reports, vol. 15, no. 1, p. 26843, 2025, doi: 10.1038/s41598-025-11738-9.[18] A. Paya and A. Gómez, “Enhancing software-defined perimeters with integrated identity solutions and threat detection for robust zero trust security,” International Journal of Information Security, vol. 24, no. 4, pp. 1–20, 2025, doi: 10.1007/s10207-025-01099-9.
  18. I. Kolawole, “Leveraging Cloud-based AI and Zero Trust Architecture to Enhance U.S. Cybersecurity and Counteract Foreign Threats,” World Journal of Advanced Research and Reviews, vol. 25, no. 3, pp. 006–025, 2025, doi: 10.30574/wjarr.2025.25.3.0635.
  19. A. M. Abdelmagid and R. Diaz, “Zero Trust Architecture as a Risk Countermeasure in Small–Medium Enterprises and Advanced Technology Systems,” Risk Analysis, 2025, doi: 10.1111/risa.70026.
  20. A. Poirrier, L. Cailleux, and T. H. Clausen, “Is Trust Misplaced? A Zero-Trust Survey,” Proceedings of the IEEE, vol. 113, no. 1, pp. 5–39, Jan. 2025, doi: 10.1109/jproc.2025.3555131.
  21. A. Singh, V. Pareek, and A. Sharma, “Blockchain-Enabled Zero Trust Framework for Securing FinTech Ecosystems Against Insider Threats and Cyber Attacks,” arXiv preprint arXiv:2507.19976, 2025, doi: 10.48550/arXiv.2507.19976.
  22. L. Alevizos, “Automated cybersecurity compliance and threat response using AI, blockchain and smart contracts,” International Journal of Information Technology, vol. 17, no. 2, pp. 767–781, 2025, doi: 10.1007/s41870-024-02324-9.
  23. O. S. Adanigbo, B. I. Adekunle, E. Ogbuefi, O. T. Odofin, O. A. Agboola, and D. Kisina, “Implementing Zero Trust Security in Multi-Cloud Microservices Platforms: A Review and Architectural Framework,” Ecosystems, vol. 13, p. 14, 2025.
  24. F. A. Idialu, “Leveraging Zero Trust Architectures and Blockchain Protocols to Prevent Credential Stuffing and Lateral Fraud Attacks in Enterprise Systems,” International Journal of Computer Applications in Technology, vol. 14, no. 8, 2025.
  25. H. Tyagi, P. K. Goel, A. Gautam, A. Agarwal, and S. Samant, “AI and Blockchain Synergy: Enhancing Security in Industrial Automation,” in AI-Enhanced Cybersecurity for Industrial Automation, H. M. Pandey and P. K. Goel, Eds., Hershey, PA: IGI Global Scientific Publishing, 2025, pp. 231–258, doi:10.4018/979-8-3373-3241-3.ch012.
  26. V. K. Kokku, “Toward Secure IoT Infrastructure: Integrating Zero Trust, Federated Learning, and Dynamic Trust Management Models,” Research and Reviews: Advancement in Cyber Security, vol. 2, no. 2, 2025.
  27. N. K. Birru, “Zero trust at scale: Security architecture for distributed enterprises,” World Journal of Advanced Research and Reviews, vol. 26, no. 2, pp. 3027–3036, 2025, doi:10.30574/wjarr.2025.26.2.1939.
  28. T. Clement, C. Gbaja, and H. Onayemi, “Adversarial Machine Learning: Defense Mechanisms Against Poisoning Attacks in Cybersecurity Models,” International Journal Of Engineering And Computer Science, vol. 13, no. 6, pp. 27286–27308, Jun. 2025, doi:10.18535/ijecs/v14i06.5156.
  29. K. T. Pedersen, L. Pepke, T. Stærmose, M. Papaioannou, G. Choudhary, and N. Dragoni, “Deepfake-Driven Social Engineering: Threats, Detection Techniques, and Defensive Strategies in Corporate Environments,” Journal of Cybersecurity and Privacy, vol. 5, no. 2, Art. no. 18, 2025, doi:10.3390/jcp5020018.
  30. N. Mohamed, H. Taherdoost, and M. Madanchian, “Enhancing Spear Phishing Defense with AI: A Comprehensive Review and Future Directions,” EAI Endorsed Transactions on Scalable Information Systems, vol. 12, no. 1, 2025, doi:10.4108/eetsis.6109.
  31. L. Hafiz and T. Hidayat, “Unveiling the Cybercrime Ecosystem: Impact of Ransomware-as-a-Service (RaaS) in Indonesia,” International Journal of Science Education and Cultural Studies, vol. 4, no. 1, pp. 11–21, 2025.
  32. T. Olorunlana and H. Mohammed, “Analysis of the Colonial Pipeline Cybersecurity Incident,” unpublished, 2025.
  33. L. Williams et al., “Research directions in software supply chain security,” ACM Transactions on Software Engineering and Methodology, vol. 34, no. 5, pp. 1–38, 2025.
  34. J. Girhotra and A. Byrisetty, “Securing Cloud-Native Applications (CNAs): A Case Study of Practices in a large IT Company,” M.Sc. thesis, Faculty of Computing, Blekinge Institute of Technology, Karlskrona, Sweden, 2025.
  35. G. Singh and S. Sharma, “A comprehensive review on the Internet of Things in precision agriculture,” Multimedia Tools and Applications, vol. 84, no. 17, pp. 18123–18198, 2025, doi: 10.1007/s11042-024-19656-0.
  36. T. Al-Shurbaji, M. Anbar, S. Manickam, I. H. Hasbullah, N. Alfriehate, B. A. Alabsi, et al., “Deep learning-based intrusion detection system for detecting IoT botnet attacks: A review,” IEEE Access, 2025.
  37. T. Zhukabayeva, L. Zholshiyeva, N. Karabayev, S. Khan, and N. Alnazzawi, “Cybersecurity Solutions for Industrial Internet of Things–Edge Computing Integration: Challenges, Threats, and Future Directions,” Sensors, vol. 25, no. 1, p. 213, 2025, doi: 10.3390/s25010213.
  38. C. K. Gitonga, “The impact of quantum computing on cryptographic systems: Urgency of quantum-resistant algorithms and practical applications in cryptography,” European Journal of Information Technologies and Computer Science, vol. 5, no. 1, pp. 1–10, 2025.
  39. E. D. Demir, B. Bilgin, and M. C. Onbasli, “Performance analysis and industry deployment of post-quantum cryptography algorithms,” arXiv preprint arXiv:2503.12952, 2025, doi: 10.48550/arXiv.2503.12952.
  40. A. Akbar, “Analyzing the Harvest Now, Decrypt Later Threat and Post-Quantum Cryptography Solutions: A Systematic Literature Review,” Migration, vol. 2, p. 10, 2025.
  41. K. C. Mizrak, “Secure Remote Work: HR’s Role in Managing Cyber Risks in Hybrid Work Environments,” in Utilizing Cybersecurity to Foster Business Innovation and Resiliency, Hershey, PA: IGI Global, 2025, pp. 169–188, doi: 10.4018/979-8-3693-6417-8.ch008.
  42. S. B. Narayan, “What is One Effective Way Organizations Can Reduce the Risk of Insider Threats Without Disrupting Productivity,” Journal of Engineering and Artificial Intelligence, vol. 1, no. 2, pp. 1–3, 2025.
  43. K. Muthusamy, “AI-Powered Threat Detection in Cybersecurity Infrastructures,” International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 6, no. 1, pp. 23–30, 2025.
  44. A. Mishra, “AI-powered cybersecurity framework for secure data transmission in IoT network,” International Journal of Advances in Engineering and Management, vol. 7, no. 3, pp. 5–13, 2025, doi: 10.35629/5252-07030513.
  45. N. R. Choudhury, S. Paul, and S. Ghosh, “Comparative analysis of traditional vs. AI-driven network security,” in AI for Large Scale Communication Networks, Hershey, PA: IGI Global, 2025, pp. 107–128, doi: 10.4018/979-8-3693-6552-6.ch006.
  46. H. Kamal and M. Mashaly, “Enhanced hybrid deep learning models-based anomaly detection method for two-stage binary and multi-class classification of attacks in intrusion detection systems,” Algorithms, vol. 18, no. 2, Art. no. 69, 2025, doi: 10.3390/a18020069.
  47. E. M. Timofte, M. Dimian, A. Graur, A. D. Potorac, D. Balan, I. Croitoru, et al., “Federated learning for cybersecurity: A privacy-preserving approach,” Applied Sciences, vol. 15, no. 12, Art. no. 6878, 2025, doi: 10.3390/app15126878.
  48. A. Tariq, T. Qayyum, S. Alrabaee, and M. A. Serhani, “Blockchain and distributed ledger technologies for cyberthreat intelligence sharing,” arXiv preprint arXiv:2504.02537, 2025, doi: 10.48550/arXiv.2504.02537.
  49. A. Gupta and K. Lakhwani, “Enhancing blockchain quality-of-service: A comparative analysis and novel smart contract mechanism,” Discover Applied Sciences, vol. 7, Art. no. 807, 2025, doi: 10.1007/s42452-025-07395-2.
  50. W. L. Rebouças Filho, “The role of zero trust architecture in modern cybersecurity: Integration with IAM and emerging technologies,” Brazilian Journal of Development, vol. 11, no. 1, Art. no. e76836, 2025.
  51. F. Stodt, C. Reich, and F. Theoleyre, “Beyond static security: A context-aware and real-time dynamic zero trust architecture for IIoT access control,” IEEE Internet of Things Journal, 2025, doi: 10.1109/JIOT.2025.3579028.
  52. M. Shafi, A. H. Lashkari, and A. H. Roudsari, “Toward generating a large scale intrusion detection dataset and intruders behavioral profiling using network and transportation layers traffic flow analyzer (NTLFlowLyzer),” Journal of Network and Systems Management, vol. 33, art. no. 44, 2025, doi: 10.1007/s10922-025-09917-0.
  53. I. H. Putro, “Evaluating the performance of machine learning classifiers for network intrusion detection: A comparative study using the UNSW-NB15 dataset,” Teknika, vol. 14, no. 2, pp. 330–338, 2025.
  54. B. Martin, T. D. Bennett, P. E. DeWitt, S. Russell, and L. N. Sanchez-Pinto, “Use of the area under the precision-recall curve to evaluate prediction models of rare critical illness events,” Pediatric Critical Care Medicine, vol. 26, no. 6, pp. e855–e859, 2025, doi: 10.1097/PCC.0000000000003752.
  55. A. A. Mohamed, A. Al-Saleh, S. K. Sharma, and G. G. Tejani, “Zero-day exploits detection with adaptive WavePCA-Autoencoder adaptive hybrid exploit detection network (AHEDNet),” Scientific Reports, vol. 15, art. no. 4036, 2025, doi: 10.1038/s41598-025-87615-2.
  56. S. Michaelides, S. Lenz, T. Vogt, and M. Henze, “Secure integration of 5G in industrial networks: State of the art, challenges and opportunities,” Future Generation Computer Systems, vol. 166, art. no. 107645, 2025, doi: 10.1016/j.future.2024.107645.
  57. J. Yedalla, “Quantum-safe cryptography: Navigating the future of cybersecurity in the post-quantum era,” International Journal of Science and Research (IJSR), vol. 14, no. 2, pp. 249–253, 2025.
  58. G. G. Shayea, M. H. M. Zabil, M. A. Habeeb, Y. L. Khaleel, and A. S. Albahri, “Strategies for protection against adversarial attacks in AI models: An in-depth review,” Journal of Intelligent Systems, vol. 34, no. 1, art. no. 20240277, 2025, doi: 10.1515/jisys-2024-0277.
  59. V. K. Pandey, D. Sahu, S. Prakash, R. S. Rathore, P. Dixit, and I. Hunko, “A lightweight framework to secure IoT devices with limited resources in cloud environments,” Scientific Reports, vol. 15, art. no. 9885, 2025, doi: 10.1038/s41598-025-09885-0.
  60. M. Khayat, E. Barka, M. A. Serhani, F. Sallabi, K. Shuaib, and H. M. Khater, “Reinforcement learning with deep features: A dynamic approach for intrusion detection in IoT networks,” IEEE Access, 2025.