@inproceedings{4eb2abc76eca4d3ab0925dd96d5b195f,
title = "A machine learning approach for intrusion detection in smart cities",
abstract = "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.",
keywords = "Deep learning, Distributed Denial of Service, Intrusion detection, Smart city, Smart water plant",
author = "Asmaa Elsaeidy and Munasinghe, {Kumudu S.} and Dharmendra Sharma and Abbas Jamalipour",
year = "2019",
month = sep,
day = "22",
doi = "10.1109/VTCFall.2019.8891281",
language = "English",
isbn = "9781728112213",
series = "IEEE Vehicular Technology Conference",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
pages = "1--5",
editor = "Li-Chun Wang and Murat Uysal",
booktitle = "2019 IEEE 90th Vehicular Technology Conference, VTC 2019 Fall - Proceedings",
address = "United States",
note = "90th IEEE Vehicular Technology Conference, VTC 2019 ; Conference date: 22-09-2019 Through 25-09-2019",
}