AI-Driven Cybersecurity Threat Detection Framework for Next-Generation Network Environments
DOI:
https://doi.org/10.70063/techcompinnovations.v3i1.184Keywords:
Artificial Intelligence, Cybersecurity, Machine Learning, Threat Detection, Network SecurityAbstract
This study explores the role of artificial intelligence in strengthening cybersecurity threat detection frameworks for next-generation network environments. The rapid expansion of cloud computing, Internet of Things ecosystems, and distributed digital infrastructures has significantly increased cybersecurity risks and operational vulnerabilities. Traditional cybersecurity systems often struggle to detect sophisticated and evolving threats due to their dependence on static detection mechanisms. Using a qualitative research approach and content analysis method, this study examines recent developments in artificial intelligence, machine learning algorithms, and intelligent cybersecurity frameworks. The findings indicate that AI-driven cybersecurity systems improve real-time threat detection, anomaly identification, automated monitoring, and predictive security analysis. Machine learning technologies such as Random Forest, Support Vector Machine, and deep learning models demonstrate strong potential for enhancing intrusion detection accuracy and reducing false positive rates. The study also identifies critical challenges related to ethical governance, privacy protection, computational complexity, and adversarial attacks in AI-based cybersecurity systems
Downloads
References
Abrahams, T. O., Ewuga, S. K., Dawodu, S. O., Adegbite, A. O., & Hassan, A. O. (2024). A Review of Cybersecurity Strategies in Modern Organizations: Examining The Evolution And Effectiveness of Cybersecurity Measures for Data Protection [Review of A Review of Cybersecurity Strategies in Modern Organizations: Examining The Evolution and Effectiveness of Cybersecurity Measures for Data Protection]. Computer Science & IT Research Journal, 5(1), 1–25. Fair East Publishers. https://doi.org/10.51594/csitrj.v5i1.699
Achuthan, K., Ramanathan, S., Srinivas, S., & Raman, R. (2024). Advancing cybersecurity and privacy with artificial intelligence: current trends and future research directions. Frontiers in Big Data, 7, 1497535–1497535. https://doi.org/10.3389/fdata.2024.1497535
Adegbite, A. O., Akinwolemiwa, D. I., Uwaoma, P. U., Kaggwa, S., Akindote, O. J., & Dawodu, S. O. (2023). Review of Cybersecurity Strategies in Protecting National Infrastructure: Perspectives from The USA. Computer Science & IT Research Journal, 4(3), 200–219. https://doi.org/10.51594/csitrj.v4i3.658
Adelani, F. A., Okafor, E. S., Jacks, B. S., & Ajala, O. A. (2024). Theoretical Frameworks for The Role of AI and Machine Learning in Water Cybersecurity: Insights From African And U.S. Applications. Computer Science & IT Research Journal, 5(3), 681–692. https://doi.org/10.51594/csitrj.v5i3.928
Adewusi, A. O., Okoli, U. I., Olorunsogo, T., Adaga, E. M., Daraojimba, D. O., & Chimezie, O. (2024). Artificial intelligence in cybersecurity: Protecting national infrastructure: A USA review [Review of Artificial intelligence in cybersecurity: Protecting national infrastructure: A USA review]. World Journal of Advanced Research and Reviews, 21(1), 2263–2275. GSC Online Press. https://doi.org/10.30574/wjarr.2024.21.1.0313
Aggarwal, D., Sharma, D., & Saxena, A. (2023). Role of AI in cyber security through Anomaly detection and Predictive analysis. Journal of Informatics Education and Research. https://doi.org/10.52783/jier.v3i2.314
Ahmadi, S. (2025). Adaptive Cybersecurity: Dynamically Retrainable Firewalls for Real-Time Network Protection. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2501.09033
Akshay. (2026). Security and Privacy Issues in cloud computing. In Zenodo (CERN European Organization for Nuclear Research). European Organization for Nuclear Research. https://doi.org/10.5281/zenodo.19082869
Aladiyan, A. (2025). Digital Safeguards: Unravelling the Complex Interplay Between Emerging Threats and Proactive Cyber Defence Strategies. Journal of Internet Services and Information Security, 15(1), 348–360. https://doi.org/10.58346/jisis.2025.i1.022
Alam, A., Umer, A., Ullah, I., & Alsayat, A. (2026). AI-enabled cybersecurity framework for future 5G wireless infrastructures. Scientific Reports, 16(1), 7055–7055. https://doi.org/10.1038/s41598-026-37444-8
Alawadhi, R., Aalmohamed, H., Alhashemi, S., & Alkhazaleh, H. A. (2024). Application of Big Data in Cybersecurity. 1–6. https://doi.org/10.1109/icspis63676.2024.10812589
Al–Hussaini, K., & Al-Swidi, A. A. (2024). A New Architecture for Decentralized Cybersecurity Mesh (DCSM) Using Blockchain Technology for Distributed Networks. 1–8. https://doi.org/10.1109/iceti63946.2024.10777231
Ali, B., Shah, S. I., Sajid, L., Talpur, M. R. H., Javed, M. U., & Warsi, M. U. (2026). Design of Intelligent Cyber Defense Frameworks Using Artificial Intelligence for Proactive Threat Detection, Prediction, and Automated Response. Global Research Journal of Natural Science and Technology. https://doi.org/10.53762/grjnst.04.01.01
Ali, M. L., Thakur, K., Schmeelk, S., DeBello, J. E., & Dragos, D. (2025). Deep Learning vs. Machine Learning for Intrusion Detection in Computer Networks: A Comparative Study. Applied Sciences, 15(4), 1903–1903. https://doi.org/10.3390/app15041903
ALmojel, F., & Mishra, S. (2024). Advancing Hospital Cybersecurity Through IoT-Enabled Neural Network for Human Behavior Analysis and Anomaly Detection. International Journal of Advanced Computer Science and Applications, 15(5). https://doi.org/10.14569/ijacsa.2024.0150506
Alsaleh, A. (2023). A balanced communication-avoiding support vector machine decision tree method for smart intrusion detection systems. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-36304-z
Alshammari, M. M., & Al‐Mamary, Y. H. (2025). User acceptance of AI-powered training: extending the technology acceptance model (TAM). Future Business Journal, 11(1). https://doi.org/10.1186/s43093-025-00665-w
Amandeep, A. (2025). Cybersentinel AI: An Intelligent Cybersecurity Framework Using Artificial Intelligence. International Journal for Research in Applied Science and Engineering Technology, 13(6), 2679–2686. https://doi.org/10.22214/ijraset.2025.72761
Arif, H., Kumar, A., Fahad, M., & Hussain, H. K. (2024). Future Horizons: AI-Enhanced Threat Detection in Cloud Environments: Unveiling Opportunities for Research. International Journal of Multidisciplinary Sciences and Arts, 2(2), 242–251. https://doi.org/10.47709/ijmdsa.v2i2.3452
Arslan, R., Özseven, M., & Aydın, M. M. (2025). Transforming European Cybersecurity: AI-Powered Threat Analysis, Quantum Age, Blockchain/Crypto Risks, and Regulatory Strategies. International Journal of Engineering & Applied Sciences, 17(2), 81–93. https://doi.org/10.24107/ijeas.1619600
Aslam, M. M., Tufail, A., Gul, H., Irshad, M. N., & Namoun, A. (2025). Artificial intelligence for secure and sustainable industrial control systems - A Survey of challenges and solutions. Artificial Intelligence Review, 58(11). https://doi.org/10.1007/s10462-025-11320-9
Atadoga, A., Sodiya, E. O., Umoga, U. J., & Amoo, O. O. (2024). A comprehensive review of machine learning’s role in enhancing network security and threat detection [Review of A comprehensive review of machine learning’s role in enhancing network security and threat detection]. World Journal of Advanced Research and Reviews, 21(2), 877–886. GSC Online Press. https://doi.org/10.30574/wjarr.2024.21.2.0501
Azad, M. A., Abdullah, S., Arshad, J., Lallie, H. S., & Ahmed, Y. (2024). Verify and trust: A multidimensional survey of zero-trust security in the age of IoT. Internet of Things, 27, 101227–101227. https://doi.org/10.1016/j.iot.2024.101227
Banyal, A. (2024). Synergies and Challenges: Exploring the Intersection of Artificial Intelligence and Cybersecurity. International Journal for Research in Applied Science and Engineering Technology, 12(4), 1091–1095. https://doi.org/10.22214/ijraset.2024.60021
Boretti, A. (2024). Technical, economic, and societal risks in the progress of artificial intelligence driven quantum technologies. Discover Artificial Intelligence, 4(1). https://doi.org/10.1007/s44163-024-00171-y
Burrell, D. N., & McAndrew, I. (2023). Addressing Bio-Cybersecurity Workforce Employee Shortages in Biotechnology and Health Science Sectors in the U.S. Scientific Bulletin, 28(2), 127–141. https://doi.org/10.2478/bsaft-2023-0014
Chimezie, O., Akagha, O. V., Dawodu, S. O., Anyanwu, A., Onwusinkwue, S., & Ahmad, I. A. I. (2024). Comprehensive Review on Cybersecurity: Modern Threats and Advanced Defense Strategies. Computer Science & IT Research Journal, 5(2), 293–310. https://doi.org/10.51594/csitrj.v5i2.758
Chinnasamy, R., Subramanian, M., & Sengupta, N. (2024). Empowering Intrusion Detection Systems: A Synergistic Hybrid Approach with Optimization and Deep Learning Techniques for Network Security. The International Arab Journal of Information Technology, 22(1). https://doi.org/10.34028/iajit/22/1/6
Chisty, N. M. A., Baddam, P. R., & Amin, R. (2022). Strategic Approaches to Safeguarding the Digital Future: Insights into Next-Generation Cybersecurity. Engineering International, 10(2), 69–84. https://doi.org/10.18034/ei.v10i2.689
Chukwunweike, J. N., Adeniran, A. A., & Obasuyi, O. (2024). Advanced modelling and recurrent analysis in network security: Scrutiny of data and fault resolution. World Journal of Advanced Research and Reviews, 23(2), 2373–2390. https://doi.org/10.30574/wjarr.2024.23.2.2582
Cunha, J., Ferreira, P., Barbero, E. M. C., Oliveira, P., Nicolau, M. J., Núñez, I., Sousa, X. R., & Serôdio, C. (2024). Enhancing Network Slicing Security: Machine Learning, Software-Defined Networking, and Network Functions Virtualization-Driven Strategies. Future Internet, 16(7), 226–226. https://doi.org/10.3390/fi16070226
Dash, B., & Ullah, S. (2024). Quantum-safe: Cybersecurity in the age of Quantum-Powered AI. World Journal of Advanced Research and Reviews, 21(1), 1555–1563. https://doi.org/10.30574/wjarr.2024.21.1.2640
Dawodu, S. O., Omotosho, A., Akindote, O. J., Adegbite, A. O., & Ewuga, S. K. (2023). Cybersecurity Risk Assessment in Banking: Methodologies and Best Practices. Computer Science & IT Research Journal, 4(3), 220–243. https://doi.org/10.51594/csitrj.v4i3.659
Erigha, E. D., Obuse, E., Ayanbode, N., Cadet, E., & Etim, E. D. (2025). Self-Learning autonomous cyber defense agents in AI-empowered security operations. Computer Science & IT Research Journal, 6(8), 475–505. https://doi.org/10.51594/csitrj.v6i8.2011
Familoni, B. T. (2024). Cybersecurity Challenges in The Age of AI: Theoretical Approaches and Practical Solutions. Computer Science & IT Research Journal, 5(3), 703–724. https://doi.org/10.51594/csitrj.v5i3.930
Ferrag, M. A., Friha, O., Kantarcı, B., Tihanyi, N., Cordeiro, L. C., Debbah, M., Hamouda, D., Al-Hawawreh, M., & Choo, K. R. (2023). Edge Learning for 6G-Enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses. IEEE Communications Surveys & Tutorials, 25(4), 2654–2713. https://doi.org/10.1109/comst.2023.3317242
Fofana, R., Bah, F. A., & Tripathi, K. (2025). An Approach to Identify the Security of IoT Devices: Challenges and Solution. International Journal of Advanced Research in Science Communication and Technology, 452–459. https://doi.org/10.48175/ijarsct-29959
Folorunso, A., Adewumi, T., Adewa, A., Okonkwo, R., & Olawumi, T. N. (2024). Impact of AI on cybersecurity and security compliance. Global Journal of Engineering and Technology Advances, 21(1), 167–184. https://doi.org/10.30574/gjeta.2024.21.1.0193
George, A., George, A. S. H., Baskar, T., & Pandey, D. (2021). XDR: The Evolution of Endpoint Security Solutions - Superior Extensibility and Analytics to Satisfy the Organizational Needs of the Future. International Journal of Advanced Research in Science Communication and Technology, 493–501. https://doi.org/10.48175/ijarsct-1888
Gerodimos, A., Μαγλαράς, Λ., Ferrag, M. A., Ayres, N., & Kantzavelou, I. (2023). IoT: Communication protocols and security threats. Internet of Things and Cyber-Physical Systems, 3, 1–13. https://doi.org/10.1016/j.iotcps.2022.12.003
Godavarthi, D., Rekapalli, V. C. S., Mohanty, S., Jaswanth, J. V. S. D. V., Polisetty, D., Dash, B. B., & Moreira, F. (2025). Federated quantum-inspired anomaly detection using collaborative neural clients. Frontiers in Artificial Intelligence, 8. https://doi.org/10.3389/frai.2025.1648609
Guembe, B., Misra, S., Azeta, A., & López-Baldominos, I. (2025). Bibliometric analysis of artificial intelligence cyberattack detection models. Artificial Intelligence Review, 58(6). https://doi.org/10.1007/s10462-025-11167-0
Hasan, K., Hossain, Md. F., Amin, A., Sutradhar, Y., Jeny, I. J., & Mahmud, S. (2025). Enhancing Proactive Cyber Defense: A Theoretical Framework for AI-Driven Predictive Cyber Threat Intelligence. Journal of Technologies Information and Communication, 5(1), 33122–33122. https://doi.org/10.55267/rtic/16176
Hashim, W., & Hussein, N. A.-H. K. (2024). Securing Cloud Computing Environments: An Analysis of Multi-Tenancy Vulnerabilities and Countermeasures. Shifra., 2024, 8–16. https://doi.org/10.70470/shifra/2024/002
Hassan, O. F., Fatai, F. O., Aderibigbe, O., Akinde, A. O., Onasanya, T. D., Sanusi, M. A., & Odukoya, O. (2024). Enhancing Cybersecurity through Cloud Computing Solutions in the United States. Intelligent Information Management, 16(4), 176–193. https://doi.org/10.4236/iim.2024.164011
Homaei, M., Mogollón-Gutiérrez, Ó., Núñez, J. C. S., Ávila, M., & Caro, A. (2024). A review of digital twins and their application in cybersecurity based on artificial intelligence [Review of A review of digital twins and their application in cybersecurity based on artificial intelligence]. Artificial Intelligence Review, 57(8). Springer Science+Business Media. https://doi.org/10.1007/s10462-024-10805-3
Hwaitat, A. K. A., Fakhouri, H. N., Alawida, M., Atoum, M. S., Abu-Salih, B., Salah, I., Al-Sharaeh, S., & Alassaf, N. (2024). Overview of Mobile Attack Detection and Prevention Techniques Using Machine Learning. International Journal of Interactive Mobile Technologies (iJIM), 18(10), 125–157. https://doi.org/10.3991/ijim.v18i10.46485
Ibiyemi, M. O., & Olutimehin, D. O. (2024). Cybersecurity in supply chains: Addressing emerging threats with strategic measures. International Journal of Management & Entrepreneurship Research, 6(6), 2024–2047. https://doi.org/10.51594/ijmer.v6i6.1241
Ibokette, A. I., Ogundare, T. O., Anyebe, A. P., Alao, F. O.-O., Odeh, I. I., & Okafor, F. C. (2024). Mitigating Maritime Cybersecurity Risks Using AI-Based Intrusion Detection Systems and Network Automation During Extreme Environmental Conditions. International Journal of Scientific Research and Modern Technology., 3(10), 65–91. https://doi.org/10.38124/ijsrmt.v3i10.73
Jain, P. (2026). The Evolving Nexus of Cyber Defense: Architectural Paradigms, Emerging Threats, and the Imperative of Quantum Resilience. In Zenodo (CERN European Organization for Nuclear Research). European Organization for Nuclear Research. https://doi.org/10.5281/zenodo.18335129
Kadel, R., Shrestha, H. K., Shrestha, A., Sharma, P., Shrestha, N., Bashyal, J., & Shrestha, S. (2023). Emergence of AI in Cyber Security. International Research Journal of Modernization in Engineering Technology and Science. https://doi.org/10.56726/irjmets32643
Kara, S. A., İlkbahar, F., & Gündüz, M. Z. (2025). AI-Powered Vulnerability Detection and Adaptive Defense Strategies in Cybersecurity. DergiPark (Istanbul University). https://dergipark.org.tr/en/pub/saucis/issue/94717/1711704
Katiyar, N., Tripathi, Mr. S., Kumar, Mr. P., Verma, M., Sahu, A. K., & Saxena, S. (2024). AI and Cyber-Security: Enhancing threat detection and response with machine learning. https://doi.org/10.53555/kuey.v30i4.2377
Khanday, S. A., Fatima, H., & Rakesh, N. (2024). A Novel Data Preprocessing Model for Lightweight Sensory IoT Intrusion Detection. International Journal of Mathematical Engineering and Management Sciences, 9(1), 188–204. https://doi.org/10.33889/ijmems.2024.9.1.010
Kulothungan, V. (2025). Securing the AI Frontier: Urgent Ethical and Regulatory Imperatives for AI-Driven Cybersecurity. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2501.10467
Kumar, A., & Thing, V. L. L. (2023). Malicious Lateral Movement in 5G Core With Network Slicing And Its Detection. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2312.01681
Lateș, I., & Boja, C. (2025). Automating Attack and Defense Strategies in Cybersecurity. Informatica Economica, 29, 5–15. https://doi.org/10.24818/issn14531305/29.1.2025.01
Lekkala, S., Avula, R., & Gurijala, P. (2024). Big Data and AI/ML in Threat Detection: A New Era of Cybersecurity. Journal of Artificial Intelligence and Big Data, 2(1), 32–48. https://doi.org/10.31586/jaibd.2022.1125
Liu, C., Tan, R., Wu, Y., Feng, Y., Jin, Z., Zhang, F., Liu, Y., & Liu, Q. (2024). Dissecting zero trust: research landscape and its implementation in IoT. Cybersecurity, 7(1). https://doi.org/10.1186/s42400-024-00212-0
Luidold, C., & Jungbauer, C. (2024). Cybersecurity policy framework requirements for the establishment of highly interoperable and interconnected health data spaces. Frontiers in Medicine, 11. https://doi.org/10.3389/fmed.2024.1379852
Mahdi, A. J. (2024). Machine learning applications of network security enhancement: review. Computer Science & IT Research Journal, 5(10), 2283–2300. https://doi.org/10.51594/csitrj.v5i10.1635
Malatji, M. (2025). A cybersecurity AI agent selection and decision support framework. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2510.01751
Manjramkar, M. A., & Jondhale, K. C. (2023). Cyber Security Using Machine Learning Techniques. In Advances in computer science research (pp. 680–701). Atlantis Press. https://doi.org/10.2991/978-94-6463-136-4_59
Manoharan, A., & Sarker, M. (2024). Revolutionizing Cybersecurity: Unleashing the Power of Artificial Intelligence and Machine Learning for Next-Generation Threat Detection. International Research Journal of Modernization in Engineering Technology and Science. https://doi.org/10.56726/irjmets32644
Meduri, K., Nadella, G. S., & Gonaygunta, H. (2024). Enhancing Cybersecurity with Artificial Intelligence: Predictive Techniques and Challenges in the Age of IoT. International Journal of Science and Engineering Applications. https://doi.org/10.7753/ijsea1304.1007
Mohale, V. Z., & Obagbuwa, I. C. (2025). A systematic review on the integration of explainable artificial intelligence in intrusion detection systems to enhancing transparency and interpretability in cybersecurity [Review of A systematic review on the integration of explainable artificial intelligence in intrusion detection systems to enhancing transparency and interpretability in cybersecurity]. Frontiers in Artificial Intelligence, 8. Frontiers Media. https://doi.org/10.3389/frai.2025.1526221
Mohamed, N. (2025). Cutting-edge advances in AI and ML for cybersecurity: a comprehensive review of emerging trends and future directions. Cogent Business & Management, 12(1). https://doi.org/10.1080/23311975.2025.2518496
Naik, B., Mehta, A., Yagnik, H., & Shah, M. (2021). The impacts of artificial intelligence techniques in augmentation of cybersecurity: a comprehensive review [Review of The impacts of artificial intelligence techniques in augmentation of cybersecurity: a comprehensive review]. Complex & Intelligent Systems, 8(2), 1763–1780. Springer Science+Business Media. https://doi.org/10.1007/s40747-021-00494-8
Nikam, R. L. (2024). AI Driven Innovations in Cyber Security. International Journal for Research in Applied Science and Engineering Technology, 12(4), 829–836. https://doi.org/10.22214/ijraset.2024.59803
Okafor, E. S., Akinrinola, O., Usman, F. O., Amoo, O. O., & Ochuba, N. A. (2024). Cybersecurity Analytics in Protecting Satellite Telecommunications Networks: A Conceptual Development of Current Trends, Challenges, and Strategic Responses. International Journal of Applied Research in Social Sciences, 6(3), 254–266. https://doi.org/10.51594/ijarss.v6i3.854
Olabanji, S. O., Oladoyinbo, O. B., Asonze, C. U., Oladoyinbo, T. O., Ajayi, S. A., & Olaniyi, O. O. (2024). Effect of Adopting AI to Explore Big Data on Personally Identifiable Information (PII) for Financial and Economic Data Transformation. Asian Journal of Economics Business and Accounting, 24(4), 106–125. https://doi.org/10.9734/ajeba/2024/v24i41268
Paliwal, G., Moh, M., Ritika, Sharma, P., Shrimal, V. M., & Kaur, G. (2025). AI-Based Effective Intrusion Detection System. 1–6. https://doi.org/10.1109/icdiss68238.2025.11320591.
Papadaki, M. (2026). AI-Driven Intrusion Detection Systems for Next-Generation Networks Design, Optimization, and Evaluation of Adaptive Machine Learning-Based Security Frameworks. In Zenodo (CERN European Organization for Nuclear Research). European Organization for Nuclear Research. https://doi.org/10.5281/zenodo.19186873
Paul, S., Choudhury, N. R., Pandit, B., & Dawn, A. (2024). Integration of AI and Quantum Computing in Cybersecurity. In Advances in mechatronics and mechanical engineering (AMME) book series (pp. 287–308). CRC Press. https://doi.org/10.4018/979-8-3693-7076-6.ch014
Potel, R. (2022). AI-Driven Security Graphs for Real-Time Breach Containment in Hybrid Cloud Environments. International Journal of AI BigData Computational and Management Studies, 3, 123–131. https://doi.org/10.63282/3050-9416.ijaibdcms-v3i4p113
Quincozes, V. E., Quincozes, S. E., Kazienko, J. F., Gama, S. D., Cheikhrouhou, O., & Koubâa, A. (2024). A survey on IoT application layer protocols, security challenges, and the role of explainable AI in IoT (XAIoT). International Journal of Information Security, 23(3), 1975–2002. https://doi.org/10.1007/s10207-024-00828-w
Radanliev, P. (2024). Artificial intelligence and quantum cryptography. Journal of Analytical Science & Technology, 15(1). https://doi.org/10.1186/s40543-024-00416-6
Raj, A. (2025). The Role of Artificial Intelligence in Cyber Security. International Journal for Research in Applied Science and Engineering Technology, 13(5), 6621–6639. https://doi.org/10.22214/ijraset.2025.71773
Raja, D. R. K., Abas, Z. A., Akula, C. S., Kumar, Y. D., Kumar, G. H., & Eswari, V. (2024). Artificial intelligence powered internet of vehicles: securing connected vehicles in 6G. Indonesian Journal of Electrical Engineering and Computer Science, 35(1), 213–213. https://doi.org/10.11591/ijeecs.v35.i1.pp213-221
Reddy, R. V., & Reddy, E. C. (2024). Intelligent Cyber Defense: Exploring the Role of AI in Safeguarding Digital Assets. International Journal for Research in Applied Science and Engineering Technology, 12(11), 75–79. https://doi.org/10.22214/ijraset.2024.61410
Rizvi, M. (2023). Enhancing cybersecurity: The power of artificial intelligence in threat detection and prevention. International Journal of Advanced Engineering Research and Science, 10(5), 55–60. https://doi.org/10.22161/ijaers.105.8
Saied, M., Guirguis, S. K., & Madbouly, M. M. (2023). A Comparative Study of Using Boosting-Based Machine Learning Algorithms for IoT Network Intrusion Detection. International Journal of Computational Intelligence Systems, 16(1). https://doi.org/10.1007/s44196-023-00355-x
Sarker, I. H. (2022). Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects. Annals of Data Science, 10(6), 1473–1498. https://doi.org/10.1007/s40745-022-00444-2
Scrivano, A. (2025). The Impact of AI on Cybersecurity: Threats and Solutions. Research Square (Research Square). https://doi.org/10.21203/rs.3.rs-7550421/v1
Sefati, S. S., Arasteh, B., Halunga, S., & Fratu, O. (2025). A comprehensive survey of cybersecurity techniques based on quality of service (QoS) on the Internet of Things (IoT). Cluster Computing, 28(12). https://doi.org/10.1007/s10586-025-05449-z
Shah, S. (2026). AI Techniques for Cybersecurity Threat Detection: An Overview. International Journal of Artificial Intelligence Data Science and Machine Learning, 7, 327–331. https://doi.org/10.63282/3050-9262.ijaidsml-v7i1p152
Shahana, A., Hasan, R., Farabi, S. F., Akter, J., Mahmud, Md. A. A., Johora, F. T., & Suzer, G. (2024). AI-Driven Cybersecurity: Balancing Advancements and Safeguards. Journal of Computer Science and Technology Studies, 6(2), 76–85. https://doi.org/10.32996/jcsts.2024.6.2.9
Sharmila, B., & Nagapadma, R. (2023). Quantized autoencoder (QAE) intrusion detection system for anomaly detection in resource-constrained IoT devices using RT-IoT2022 dataset. Cybersecurity, 6(1). https://doi.org/10.1186/s42400-023-00178-5
Shoetan, P. O., Amoo, O. O., Okafor, E. S., & Olorunfemi, O. L. (2024). Synthesizing AI’s Impact on Cybersecurity in Telecommunications: A Conceptual Framework. Computer Science & IT Research Journal, 5(3), 594–605. https://doi.org/10.51594/csitrj.v5i3.908
Singh, S., & Kumar, D. (2024). Enhancing Cyber Security Using Quantum Computing and Artificial Intelligence: A Review [Review of Enhancing Cyber Security Using Quantum Computing and Artificial Intelligence: A Review]. International Journal of Advanced Research in Science Communication and Technology, 4–11. Shivkrupa Publication’s. https://doi.org/10.48175/ijarsct-18902
Singh, S. V. (2025). The Role of Artificial Intelligence in Enhancing Cybersecurity Systems. International Journal for Multidisciplinary Research, 7(6). https://doi.org/10.36948/ijfmr.2025.v07i06.61033
Sonko, S., Ibekwe, K. I., Ilojianya, V. I., Etukudoh, E. A., & Fabuyide, A. (2024). Quantum Cryptography and U.S. Digital Security: A Comprehensive Review: Investigating the Potential of Quantum Technologies in Creating Unbreakable Encryption And Their Future in National Security. Computer Science & IT Research Journal, 5(2), 390–414. https://doi.org/10.51594/csitrj.v5i2.790
Sontan, A. D., & Samuel, S. V. (2024). The intersection of Artificial Intelligence and cybersecurity: Challenges and opportunities. World Journal of Advanced Research and Reviews, 21(2), 1720–1736. https://doi.org/10.30574/wjarr.2024.21.2.0607
Tiwari, A. (2024). Securing the Cloud: Overcoming Challenges and Implementing Solutions for Effective Cloud Computing Security. International Journal for Research in Applied Science and Engineering Technology, 12(5), 5100–5111. https://doi.org/10.22214/ijraset.2024.62772
Udeh, E. O., Amajuoyi, P., Adeusi, K. B., & Scott, A. O. (2024). The integration of artificial intelligence in cybersecurity measures for sustainable finance platforms: An analysis. Computer Science & IT Research Journal, 5(6), 1221–1246. https://doi.org/10.51594/csitrj.v5i6.1195
Vannthoeurn, S., & Chadarin, C. (2025). Using AI for Real-Time Threat Detection and Anomaly Identification. Journal of Data Analysis and Critical Management, 1(2), 26–33. https://doi.org/10.64235/2r41sr73
Venkadesh, P. (2025). Aegis AI - Intelligent Cyber Resilience. Interantional Journal of Scientific Research in Engineering and Management, 9(3), 1–9. https://doi.org/10.55041/ijsrem42978
Yogi, M. K. (2023). A Comprehensive Study of Zero-Day Attacks. Journal of Information Technology and Digital World, 5(3), 253–273. https://doi.org/10.36548/jitdw.2023.3.003
Zaka, Engr. R., Uddin, S., Hayat, M. M., Murtaza, A., Haider, S., & Beejal, C. lal. (2025). AI-Driven Cybersecurity for IoT–Cloud Ecosystems. Physical Education Health and Social Sciences, 3(3), 63–76. https://doi.org/10.63163/jpehss.v3i3.633
