ARTIFICIAL INTELLIGENCE IN CYBERSECURITY: ADVANCING THREAT DETECTION, RESPONSE, AND PRIVACY PRESERVATION IN THE DIGITAL ERA

Sanjay Patel; Viral Patel; Ashvin Prajapati; Nrupesh Shah; Nitin Raval

doi:10.29121/shodhkosh.v4.i2.2023.6245

Authors

Sanjay Patel Assistant Professor, Department of Computer Engineering, Government Engineering College, Sector-28, Gandhinagar
Viral Patel Assistant Professor, Department of Computer Engineering, Government Engineering College, Sector-28, Gandhinagar
Ashvin Prajapati Assistant Professor, Department of Computer Engineering, Government Engineering College, Sector-28, Gandhinagar
Nrupesh Shah Assistant Professor, Department of Computer Engineering, Government Engineering College, Sector-28, Gandhinagar
Nitin Raval Assistant Professor, Department of Computer Engineering, Government Engineering College, Sector-28, Gandhinagar

DOI:

https://doi.org/10.29121/shodhkosh.v4.i2.2023.6245

Keywords:

Artificial Intelligence, Cybersecurity, Threat Detection, Incident Response, Privacy Preservation, Adversarial Ai, Federated Learning

Abstract [English]

The exponential growth of digital technologies has amplified both opportunities and risks in the cybersecurity domain. With the rising frequency and sophistication of cyberattacks, traditional rule-based and signature-driven defense mechanisms have become inadequate in addressing real-time threats. Artificial Intelligence (AI) has emerged as a transformative approach for enhancing cybersecurity by enabling adaptive, predictive, and automated security solutions. This paper provides a comprehensive review of the integration of AI in cybersecurity, focusing on its role in advancing threat detection, accelerating incident response, and ensuring privacy preservation in the digital era. We explore how machine learning, deep learning, and natural language processing are applied in intrusion detection systems, malware classification, phishing detection, and fraud prevention. In addition, the paper highlights AI-driven innovations in automated response systems, adaptive firewalls, and intelligent Security Operations Centers (SOCs). While AI introduces remarkable capabilities, it also brings ethical challenges, including data privacy concerns, model explainability, adversarial attacks, and biases in training data. The review examines recent case studies and implementations of AI in critical infrastructures, healthcare, and finance, emphasizing their successes and limitations. Furthermore, we discuss emerging paradigms such as federated learning, blockchain-AI integration, and quantum-resilient AI models for future-proof cybersecurity. By synthesizing current literature and industry practices, this paper provides insights into how AI can effectively transform cybersecurity landscapes while addressing inherent challenges. The findings underline the importance of balancing technological advancement with responsible AI governance to ensure secure, transparent, and privacy-preserving digital ecosystems.

References

Ponemon Institute. Cost of a Data Breach Report 2023. IBM Security, 2023.

Sommer, R., & Paxson, V. “Outside the Closed World: On Using Machine Learning for Network Intrusion Detection.” IEEE Symposium on Security and Privacy, 2010. DOI: https://doi.org/10.1109/SP.2010.25

Buczak, A. L., & Guven, E. “A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection.” IEEE Communications Surveys & Tutorials, 2016. DOI: https://doi.org/10.1109/COMST.2015.2494502

Pitropakis, N. et al. “A taxonomy and survey of attacks against machine learning.” Computer Science Review, 2020. DOI: https://doi.org/10.1016/j.cosrev.2019.100199

Shaukat, K. et al. “Cyber threat detection using machine learning techniques: A review.” Computers & Security, 2020. DOI: https://doi.org/10.1109/ICCWS48432.2020.9292388

Chio, C., & Freeman, D. Machine Learning and Security. O’Reilly Media, 2018.

[Rigaki, M., & Garcia, S. “Bringing a GAN to a Knife-Fight: Adapting Malware Communication to Avoid Detection.” IEEE Security & Privacy Workshops, 2018. DOI: https://doi.org/10.1109/SPW.2018.00019

Berman, D. et al. “A survey of deep learning methods for cyber security.” Information Fusion, 2019. DOI: https://doi.org/10.3390/info10040122

Wang, T. et al. “Adversarial Attacks and Defenses in Images, Graphs and Text: A Review.” International Journal of Automation and Computing, 2020.

Abeshu, A., & Chilamkurti, N. “Deep Learning for Cyber Security Intrusion Detection: Approaches, Datasets, and Comparative Study.” Journal of Information Security and Applications, 2018.

Fernandes, E. et al. “Security implications of IoT: A survey.” ACM Computing Surveys, 2017.

Zhang, Y. et al. “Privacy-preserving federated learning for healthcare: A survey.” IEEE Transactions on Neural Networks and Learning Systems, 2021.