A deep learning approach for IP hijack detection based on ASN embedding

Tal Shapira, Yuval Shavitt

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

IP hijack detection is an important security challenge. In this paper we introduce a novel approach for BGP hijack detection using deep learning. Similar to natural language processing (NLP) models, we show that by using BGP route announcements as sentences, we can embed each AS number (ASN) to a vector that represents its latent characteristics. In order to solve this supervised learning problem, we use these vectors as an input to a recurrent neural network and achieve an excellent result: an accuracy of 99.99% for BGP hijack detection with 0.00% false alarm. We test our method on 48 past hijack events between the years 2008 and 2018 and detect 32 of them, and in particular, all the events within two years from our training data.

Original languageEnglish
Title of host publicationNetAI 2020 - Proceedings of the 2020 Workshop on Network Meets AI and ML
PublisherAssociation for Computing Machinery
Pages35-41
Number of pages7
ISBN (Electronic)9781450380430
DOIs
StatePublished - 14 Aug 2020
Event2020 ACM Workshop on Network Meets AI and ML, NetAI 2020 - Virtual, Online, United States
Duration: 14 Aug 2020 → …

Publication series

NameNetAI 2020 - Proceedings of the 2020 Workshop on Network Meets AI and ML

Conference

Conference2020 ACM Workshop on Network Meets AI and ML, NetAI 2020
Country/TerritoryUnited States
CityVirtual, Online
Period14/08/20 → …

Funding

FundersFunder number
Tel Aviv University

    Keywords

    • AS embedding
    • BGP
    • BGP hijacking
    • Deep Learning
    • Routing
    • Security

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