Federated Learning in Cybersecurity: Applications, Challenges, and Future Directions
Abstract: Federated learning is an innovative decentralized machine learning technique that offers significant potential for enhancing cybersecurity. By enabling multiple entities to collaboratively train models without sharing raw data, federated learning preserves data privacy and security while leveraging the collective intelligence of diverse datasets. This paper explores the core principles of federated learning, its applications in threat detection, intrusion detection systems (IDS), and malware detection. It also addresses the technical challenges related to data privacy, communication overhead, and model accuracy, providing solutions to overcome these hurdles. Furthermore, the paper discusses future trends and research opportunities, including the integration of federated learning with emerging technologies like blockchain. Through case studies and real-world examples, we demonstrate the effectiveness of federated learning in improving cybersecurity measures. The paper concludes by emphasizing the importance of ongoing research and collaboration to fully realize the potential of federated learning in safeguarding digital infrastructures.
Keywords: Federated Learning, Cybersecurity, Threat Detection, Intrusion Detection Systems, Malware Detection, Data Privacy, Secure Aggregation, Communication Overhead, Model Accuracy, Blockchain Integration
How to Cite?: Yamini Kannan, "Federated Learning in Cybersecurity: Applications, Challenges, and Future Directions", Volume 13 Issue 7, July 2024, International Journal of Science and Research (IJSR), Pages: 617-622, https://www.ijsr.net/getabstract.php?paperid=MR24706174710, DOI: https://dx.doi.org/10.21275/MR24706174710