A machine learning approach for intrusion detection in smart cities

Asmaa Elsaeidy, Kumudu S. Munasinghe, Dharmendra Sharma, Abbas Jamalipour

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

10 Citations (Scopus)


Over the recent years smart cities have been emerged as promising paradigm for a transition toward providing effective and real time smart services. Despite the great potential it brings to citizens' life, security and privacy issues still need to be addressed. Due to technology advances, large amount of data is produced, where machine learning methods are applied to learn meaningful patterns. In this paper a machine learning-based framework is proposed for detecting distributed Denial of Service (DDoS) attacks in smart cities. The proposed framework applies restricted Boltzmann machines to learn high-level features from raw data and on top of these learned features, a feed forward neural network model is trained for attack detection. The performance of the proposed framework is verified using a smart city dataset collected from a smart water plant. The results show the effectiveness of the proposed framework in detecting DDoS attacks.

Original languageEnglish
Title of host publication2019 IEEE 90th Vehicular Technology Conference, VTC 2019 Fall - Proceedings
EditorsLi-Chun Wang, Murat Uysal
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781728112206
ISBN (Print)9781728112213
Publication statusPublished - 22 Sept 2019
Event90th IEEE Vehicular Technology Conference - Honolulu, United States
Duration: 22 Sept 201925 Sept 2019

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Conference90th IEEE Vehicular Technology Conference
Abbreviated titleVTC 2019
Country/TerritoryUnited States


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