Federated learning for cyber security

Ekaterina Khramtsova, Christian Hammerschmidt, Sofian Lagraa, Radu State

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

2 Citations (Scopus)

Abstract

Managed security service providers increasingly rely on machine-learning methods to exceed traditional, signature-based threat detection and classification methods. As machine-learning often improves with more data available, smaller organizations and clients find themselves at a disadvantage: Without the ability to share their data and others willing to collaborate, their machine-learned threat detection will perform worse than the same model in a larger organization. We show that Federated Learning, i.e. collaborative learning without data sharing, successfully helps to overcome this problem. Our experiments focus on a common task in cyber security, the detection of unwanted URLs in network traffic seen by security-as-a-service providers. Our experiments show that i) Smaller participants benefit from larger participants ii) Participants seeing different types of malicious traffic can generalize better to unseen types of attacks, increasing performance by 8% to 15% on average, and up to 27% in the extreme case. iii) Participating in Federated training never harms the performance of the locally trained model. In our experiment modeling a security-as-a service setting, Federated Learning increased detection up to 30% for some participants in the scheme. This clearly shows that Federated Learning is a viable approach to address issues of data sharing in common cyber security settings.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 40th International Conference on Distributed Computing Systems, ICDCS 2020
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1316-1321
Number of pages6
ISBN (Electronic)9781728170022
DOIs
Publication statusPublished - 2020
Event40th IEEE International Conference on Distributed Computing Systems, ICDCS 2020 - Singapore, Singapore
Duration: 29 Nov 20201 Dec 2020

Publication series

NameProceedings - International Conference on Distributed Computing Systems
Volume2020-November

Conference

Conference40th IEEE International Conference on Distributed Computing Systems, ICDCS 2020
Country/TerritorySingapore
CitySingapore
Period29/11/201/12/20

Keywords

  • cyber-security
  • Federated-learning
  • Machine-learning

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