Artificial Intelligence-Driven Approaches for Enhancing Cybersecurity in Modern Digital Systems
Keywords:
Artificial Intelligence, Cybersecurity, Machine Learning, Intrusion Detection Systems, Threat Intelligence, Digital SecurityAbstract
The rapid digitalization of services, industries, and governance systems has significantly increased reliance on interconnected networks, cloud infrastructures, and data-driven platforms. While this transformation has enhanced efficiency and innovation, it has simultaneously expanded the threat landscape of cybersecurity. Traditional rule-based and signature-based security mechanisms are increasingly inadequate in detecting sophisticated cyberattacks such as zero-day exploits, advanced persistent threats (APTs), ransomware, and insider attacks. In this context, Artificial Intelligence (AI) has emerged as a transformative solution capable of enhancing cybersecurity through automation, predictive analytics, and intelligent threat detection. This research paper examines AI-driven approaches for strengthening cybersecurity in modern digital systems. It explores the application of machine learning, deep learning, neural networks, natural language processing, and reinforcement learning in detecting, preventing, and responding to cyber threats. The paper highlights how AI systems analyse large volumes of structured and unstructured data to identify anomalous behaviour, recognize attack patterns, and adapt to evolving threat environments in real time. Key domains such as intrusion detection systems, malware analysis, phishing detection, identity management, and automated incident response are critically discussed. Furthermore, the study addresses challenges associated with AI-based cybersecurity solutions, including data quality issues, model bias, explainability, adversarial machine learning attacks, and ethical concerns related to privacy and surveillance. The paper also emphasizes the importance of integrating human expertise with AI systems to ensure reliability, transparency, and accountability. The findings suggest that AI-driven cybersecurity frameworks significantly enhance threat detection accuracy, reduce response time, and improve overall system resilience. However, effective implementation requires robust data governance, continuous model training, interdisciplinary collaboration, and regulatory oversight. The study concludes that Artificial Intelligence is not merely a supportive tool but a strategic necessity for securing modern digital ecosystems against increasingly complex cyber threats.
References
Abu-Nimeh, S., Nappa, D., Wang, X., & Nair, S. (2007). A comparison of machine learning techniques for phishing detection. Proceedings of the Anti-Phishing Working Groups 2nd Annual eCrime Researchers Summit, 60–69. https://doi.org/10.1145/1299015.1299021
Bahnsen, A. C., Bohorquez, E. C., Villegas, S., Vargas, J., & Gonzalez, F. A. (2015). Classifying phishing URLs using recurrent neural networks. 2015 APWG Symposium on Electronic Crime Research (eCrime), 1–8. https://doi.org/10.1109/ECRIME.2015.7120208
Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., … Amodei, D. (2018). The malicious use of artificial intelligence: Forecasting, prevention, and mitigation. arXiv preprint arXiv:1802.07228.
Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153–1176. https://doi.org/10.1109/COMST.2015.2494502
Goodfellow, I., Shlens, J., & Szegedy, C. (2014). Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572.
Saxe, J., & Berlin, K. (2015). Deep neural network-based malware detection using two-dimensional binary program features. 2015 10th International Conference on Malicious and Unwanted Software (MALWARE), 11–20. https://doi.org/10.1109/MALWARE.2015.7413680
Sommer, R., & Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. 2010 IEEE Symposium on Security and Privacy, 305–316. https://doi.org/10.1109/SP.2010.25
Yin, C., Zhu, Y., Fei, J., & He, X. (2017). A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access, 5, 21954–21961. https://doi.org/10.1109/ACCESS.2017.2762418
Zhang, Y., Chen, X., Guo, D., Song, M., Teng, Y., & Wang, X. (2020). PCCN: Parallel cross convolutional neural network for malicious code detection. Journal of Network and Computer Applications, 166, 102685. https://doi.org/10.1016/j.jnca.2020.102685
Sharma, A., & Sahay, S. K. (2016). An effective approach for classification of advanced persistent threats. International Journal of Computer Applications, 151(3), 35–41.
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