A Comprehensive Survey of Federated Learning for Enhanced Privacy in the Intrusion Detection System
Subject Areas : Machine learning
Dattatray Raghunath Kale
1
*
,
Swati Shirke-Deshmukh
2
,
Amolkumar Jadhav
3
,
Shrihari Khatawkar
4
,
Sunny Mohite
5
,
Sarang Patil
6
,
Madhav Salunkhe
7
,
Rahul Sonkamble
8
1 - Computer Science & Engineering –Artificial Intelligence & Machine Learning Department, Pimpri Chinchwad University, Pune, Maharashtra, India
2 - Department of Computer Science & Engineering, Pimpri Chinchwad University, Pune, Maharashtra, India
3 - Computer Science and Engineering D.Y.Patil College of Engineering and Technology, Kolhapur Maharashtra, India
4 - Computer Science and Engineering, Annasaheb Dange College of Engineering and Technology, Ashta India
5 - D Y Patil College of Engineering and Technology, Kolhapur, India
6 - Amity School of Engineering & Technology, Amity University Mumbai
7 - Annasaheb Dange College of Engineering and Technology Ashta
8 - Department of Computer Science & Engineering, Pimpri Chinchwad University, Pune, Maharashtra, India
Keywords: Federated Learning, Intrusion Detection, Data Privacy, Cyber security,
Abstract :
This comprehensive survey uses Federated Learning (FL) as a lens to examine how intrusion detection privacy is changing. The increasing number of interconnected systems and the growing risk of cyber intrusions make it critical to strike a balance between efficient detection and protecting personal privacy. Decentralized machine learning paradigms like federated learning present a viable way to handle this difficult balance. The analysis systematically looks at the improvements in privacy-preserving intrusion detection that FL approaches have brought about. It clarifies FL's fundamental ideas and highlights its ability to train models cooperatively among dispersed nodes while protecting sensitive data. The study explores the various uses of FL in relation to security monitoring systems, emphasizing significant innovations and contributions. In addition, the review offers a detailed examination of how FL integrates with current intrusion detection techniques, illuminating the synergies that result in increased efficiency and accuracy. It explains how this paradigm shift allays worries about centralized storage of data by handling the complex relationship between FL and privacy. This paper ends by summarizing the cutting-edge research in Federated Learning for improved privacy in intrusion detection. It is an essential tool for academics, industry professionals, and decision-makers who want to understand the complex dynamics of FL as a strong defense against cyberattacks that are always changing and compromising personal information. FL in relation to security monitoring systems
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