Redefining Cybersecurity with Artificial Intelligence: Innovations in Data Networking Defense
Keywords:
Artificial Intelligence (AI), Cybersecurity, Threat Detection, Scalability, Network Resilience
Abstract
The integration of Artificial Intelligence (AI) in cybersecurity has emerged as a transformative approach to addressing the growing complexity of cyber threats in data networking environments. This study explores the impact of AI-powered systems on threat detection, response times, and network resilience. Through a mixed-method analysis of case studies from industries such as healthcare, finance, and IoT, the research reveals that AI systems outperform traditional cybersecurity methods in both detection accuracy (95% vs. 80%) and response times (15 seconds vs. 25 seconds). However, scalability remains a critical challenge, with AI systems showing efficiency declines in large, complex networks.
The study employs a qualitative and exploratory research design, analyzing literature, case studies, and descriptive statistics. Key findings underscore the effectiveness of AI in automating threat detection and mitigation, while highlighting limitations such as data quality dependency and scalability issues. Statistical tests, including t-tests and ANOVA, further validate the superiority of AI systems.
This research has significant implications for practice, advocating for tailored AI solutions in diverse industries, regulatory frameworks for ethical AI use, and hybrid models combining AI with human expertise. Limitations include a focus on short-term outcomes and limited industry representation, which open pathways for future research.
The findings emphasize the transformative potential of AI in redefining cybersecurity, providing organizations with enhanced tools to safeguard their data networks. By addressing existing challenges and fostering interdisciplinary collaboration, AI can become an indispensable asset in the fight against evolving cyber threats.
References
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Bhatti, I., Waqar, M., & Khan, A. H. (2024). The Role of AI-Driven Automation in Smart Cities: Enhancing Urban Living through Intelligent System. Multidisciplinary Journal of Instruction (MDJI), 7(1), 101-114.
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Farhan, M., Rafiq, H., Rafi, H., Ali, R., & Jahan, S. (2019). Neuroprotective role of quercetin against neurotoxicity induced by lead acetate in male rats. Int. J. Biol. Biotech., 16(2), 291-298.
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Farooq Mohi-U-din, S., Tariq, M., & Tariq, A. (2024). Deep Dive into Health: Harnessing AI and Deep Learning for Brain and Heart Care. International Journal of Advanced Engineering Technologies and Innovations, 1(4), 248-267.
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Hussain, H. K., Tariq, A., & Gill, A. Y. (2023). Role of AI in Cardiovascular Health Care; a Brief Overview. Journal of World Science, 2(4), 794-802.
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Khan, A. H., Zainab, H., Khan, R., & Hussain, H. K. (2024). Implications of AI on Cardiovascular Patients’ Routine Monitoring and Telemedicine. BULLET: Jurnal Multidisiplin Ilmu, 3(5), 621-637.
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Khan, M. I., Arif, A., & Khan, A. R. A. (2024). AI's Revolutionary Role in Cyber Defense and Social Engineering. International Journal of Multidisciplinary Sciences and Arts, 3(4), 57-66.
Khan, M. I., Arif, A., & Khan, A. R. A. (2024). The Most Recent Advances and Uses of AI in Cybersecurity. BULLET: Jurnal Multidisiplin Ilmu, 3(4), 566-578.
Khan, R., Zainab, H., Khan, A. H., & Hussain, H. K. (2024). Advances in Predictive Modeling: The Role of Artificial Intelligence in Monitoring Blood Lactate Levels Post-Cardiac Surgery. International Journal of Multidisciplinary Sciences and Arts, 3(4), 140-151.
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Lodhi, S. K., Gill, A. Y., & Hussain, I. (2024). 3D Printing Techniques: Transforming Manufacturing with Precision and Sustainability. International Journal of Multidisciplinary Sciences and Arts, 3(3), 129-138.
Lodhi, S. K., Hussain, H. K., & Gill, A. Y. (2024). Renewable Energy Technologies: Present Patterns and Upcoming Paths in Ecological Power Production. Global Journal of Universal Studies, 1(1), 108-131.
Lodhi, S. K., Hussain, I., & Gill, A. Y. (2024). Artificial intelligence: Pioneering the future of sustainable cutting tools in smart manufacturing. BIN: Bulletin of Informatics, 2(1), 147-162.
Mahmood, T., Asif, M., & Raza, Z. H. (2024). Smart Forestry: The Role of AI and Bioengineering in Revolutionizing Timber Production and Biodiversity Protection. Revista de Inteligencia Artificial en Medicina, 15(1), 1176-1202.
Rafi, A. H., Chowdhury, A. A. A., Sultana, A., & Noman, A. A. (2024). Unveiling the Role of Artificial Intelligence and Stock Market Growth in Achieving Carbon Neutrality in the United States: An ARDL Model Analysis. arXiv preprint arXiv:2412.16166.
Rafi, A. H., Sultana, A., Chowdhury, A. A. A., & Tariq, M. (2024). Artificial Intelligence for Early Diagnosis and Personalized Treatment in Gynecology. International Journal of Advanced Engineering Technologies and Innovations, 2(1), 286-306.
Rafi, H. (2024). Peer Review of “Establishment of a Novel Fetal Ovine Heart Cell Line by Spontaneous Cell Fusion: Experimental Study”. JMIRx Bio, 2(1), e63336.
Rafi, H., & Farhan, M. (2015). Dapoxetine: An Innovative Approach in Therapeutic Management in Animal Model of Depression. Pakistan Journal of Pharmaceutical Sciences, 2(1), 15-22.
Rafi, H., Ahmad, F., Anis, J., Khan, R., Rafiq, H., & Farhan, M. (2020). Comparative effectiveness of agmatine and choline treatment in rats with cognitive impairment induced by AlCl3 and forced swim stress. Current Clinical Pharmacology, 15(3), 251-264.
Rafi, H., Rafiq, H., & Farhan, M. (2021). Antagonization of monoamine reuptake transporters by agmatine improves anxiolytic and locomotive behaviors commensurate with fluoxetine and methylphenidate. Beni-Suef University Journal of Basic and Applied Sciences, 10, 1-14.
Rafi, H., Rafiq, H., & Farhan, M. (2021). Inhibition of NMDA receptors by agmatine is followed by GABA/glutamate balance in benzodiazepine withdrawal syndrome. Beni-Suef University Journal of Basic and Applied Sciences, 10, 1-13.
Rafi, H., Rafiq, H., & Farhan, M. (2023). Agmatine alleviates brain oxidative stress induced by sodium azide.
Rafi, H., Rafiq, H., & Farhan, M. (2024). Pharmacological profile of agmatine: An in-depth overview. Neuropeptides, 102429.
Rafi, H., Rafiq, H., Hanif, I., Rizwan, R., & Farhan, M. (2018). Chronic agmatine treatment modulates behavioral deficits induced by chronic unpredictable stress in wistar rats. Journal of Pharmaceutical and Biological Sciences, 6(3), 80.
Rafi, H., Rafiq, H., Khan, R., Ahmad, F., Anis, J., & Farhan, M. (2019). Neuroethological study of ALCL3 and chronic forced swim stress induced memory and cognitive deficits in albino rats. The Journal of Neurobehavioral Sciences, 6(2), 149-158.
Rafiq, H., Farhan, M., Rafi, H., Rehman, S., Arshad, M., & Shakeel, S. (2022). Inhibition of drug induced Parkinsonism by chronic supplementation of quercetin in haloperidol-treated wistars. Pak J Pharm Sci, 35, 1655-1662.
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Published
2025-01-02
How to Cite
Dandamudi, S. R. P., Sajja, J., & Khanna, A. (2025). Redefining Cybersecurity with Artificial Intelligence: Innovations in Data Networking Defense. European Journal of Science, Innovation and Technology, 4(6), 54-66. Retrieved from https://ejsit-journal.com/index.php/ejsit/article/view/577
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Copyright (c) 2024 Sai Ratna Prasad Dandamudi, Jaideep Sajja, Amit Khanna
This work is licensed under a Creative Commons Attribution 4.0 International License.
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