Enhancing Privacy-Preserving Intrusion Detection through Federated Learning

Ammar Alazab, Ansam Khraisat, Sarabjot Singh, Tony Jan

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Detecting anomalies, intrusions, and security threats in the network (including Internet of Things) traffic necessitates the processing of large volumes of sensitive data, which raises concerns about privacy and security. Federated learning, a distributed machine learning approach, enables multiple parties to collaboratively train a shared model while preserving data decentralization and privacy. In a federated learning environment, instead of training and evaluating the model on a single machine, each client learns a local model with the same structure but is trained on different local datasets. These local models are then communicated to an aggregation server that employs federated averaging to aggregate them and produce an optimized global model. This approach offers significant benefits for developing efficient and effective intrusion detection system (IDS) solutions. In this research, we investigated the effectiveness of federated learning for IDSs and compared it with that of traditional deep learning models. Our findings demonstrate that federated learning, by utilizing random client selection, achieved higher accuracy and lower loss compared to deep learning, particularly in scenarios emphasizing data privacy and security. Our experiments highlight the capability of federated learning to create global models without sharing sensitive data, thereby mitigating the risks associated with data breaches or leakage. The results suggest that federated averaging in federated learning has the potential to revolutionize the development of IDS solutions, thus making them more secure, efficient, and effective.
Original languageEnglish
JournalElectronics (Switzerland)
Volume12
Issue number16
Publication statusPublished - 8 Aug 2023

Keywords

  • federated learning
  • data privacy
  • communication network security
  • anomaly detection
  • intrusion detection system

Fingerprint

Dive into the research topics of 'Enhancing Privacy-Preserving Intrusion Detection through Federated Learning'. Together they form a unique fingerprint.

Cite this