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A novel online incremental learning intrusion prevention system

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Attack vectors are continuously evolving in order to evade Intrusion Detection systems. Internet of Things (IoT) environments, while beneficial for the IT ecosystem, suffer from inherent hardware limitations, which restrict their ability to implement comprehensive security measures and increase their exposure to vulnerability attacks. This paper proposes a novel Network Intrusion Prevention System that utilises a Self-Organizing Incremental Neural Network along with a Support Vector Machine. Due to its structure, the proposed system provides a security solution that does not rely on signatures or rules and is capable to mitigate known and unknown attacks in real-time with high accuracy. Based on our experimental results with the NSL KDD dataset, the proposed framework can achieve on-line updated incremental learning, making it suitable for efficient and scalable industrial applications.

Original languageEnglish
Title of host publication2019 10th IFIP International Conference on New Technologies, Mobility and Security, NTMS 2019 - Proceedings and Workshop
PublisherIEEE
Number of pages6
ISBN (Electronic)978-1-7281-1542-9, 978-1-7281-1541-2
ISBN (Print)978-1-7281-1543-6
DOIs
Publication statusPublished - 15 Jul 2019
Event10th IFIP International Conference on New Technologies, Mobility and Security - Canary Islands, Spain
Duration: 24 Jun 201926 Jun 2019

Publication series

Name2019 10th IFIP International Conference on New Technologies, Mobility and Security, NTMS 2019 - Proceedings and Workshop
PublisherIEEE
ISSN (Print)2157-4952

Conference

Conference10th IFIP International Conference on New Technologies, Mobility and Security
Abbreviated titleNTMS 2019
CountrySpain
CityCanary Islands
Period24/06/1926/06/19

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ID: 15934675