Ada-boosted locally enhanced probabilistic neural network for IoT intrusion detection

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

5 Citations (Scopus)

Abstract

This paper proposes an intelligent and compact Probabilistic Neural Network which integrates locally enhanced semi-parametric base classifiers with AdaBoosting for intrusion detection system in IoT environment. The proposed model is to provide an improved intrusion detection at an affordable computational complexity. The proposed model is applied to the benchmark data sets for experiment and shows comparable intrusion detection performance at a reduced computational cost for real time use.

Original languageEnglish
Title of host publicationComplex, Intelligent, and Software Intensive Systems - Proceedings of the 12th International Conference on Complex, Intelligent, and Software Intensive Systems CISIS-2018
EditorsLeonard Barolli, Makoto Ikeda, Makoto Takizawa, Nadeem Javaid
PublisherSpringer Verlag
Pages583-589
Number of pages7
ISBN (Print)9783319936581
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event12th International Conference on Complex, Intelligent, and Software Intensive Systems, CISIS 2018 - Matsue, Japan
Duration: 4 Jul 20186 Jul 2018

Publication series

NameAdvances in Intelligent Systems and Computing
Volume772
ISSN (Print)2194-5357

Conference

Conference12th International Conference on Complex, Intelligent, and Software Intensive Systems, CISIS 2018
Country/TerritoryJapan
CityMatsue
Period4/07/186/07/18

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