An Advanced Cyber Security Model Using Federated Machine Learning Approach for Intrusion Detection in Networks

Abstract

The intelligent cyber security model for intrusion detection with federated machine learning is based on distributed learning protocols for processing data and training models while preserving data security and privacy. Data owners can use the federated machine learning architecture to create a standard intrusion detection system by transferring their data without revealing private information. Taking a global approach to fraud management, models based on predictive analysis and anomaly detection are developed using a federated learning model. By leveraging unsupervised machine learning algorithms, the system can find new and unconventional ways fraudsters make a move by recognizing intricate relationships within them. In addition, the system can develop an adaptive intrusion detection solution with current new profile downloads and model training. This model is a handy and effective mechanism culminating in distributed architectures and proper data processing protocols to develop radical improvements in security systems to counter cyberattacks. Also, the model seeks to enhance cyber security systems since federated learning combines the strength that comes with advances in data analysis techniques, which helps in the detection and response to cyberattacks.

Received: 3 July 2024 | Revised: 29 September 2024 | Accepted: 19 October 2024

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The Network Intrusion Detection data that supports the findings of this study is openly available at https://www.kaggle.com/ datasets/sampadab17/network-intrusion-detection.

Author Contribution Statement

Mahantesh Laddi: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Shridhar Allagi: Validation, Resources, Writing – review & editing, Supervision, Project administration. Rashmi Rachh: Supervision. Kuldeep Sambrekar: Project administration. Shrikant Athanikar: Visualization.