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Cyber attacks detection from smart city applications using artificial neural network

conference contribution
posted on 25.06.2021, 00:17 authored by MD Mamunur RashidMD Mamunur Rashid, Joarder Kamruzzaman, Tasadduq ImamTasadduq Imam, Shahriar Kaisar, Md Jahangir Alam
Recently, the widespread deployment of the Internet of Things (IoT) applications has contributed to the development of smart cities, which utilise smart applications to maximize operational efficiency, and thereby the quality of services and the wellbeing of people. In this paper, we propose an attack and anomaly detection technique based on machine learning algorithms to mitigate IoT cybersecurity threats in a smart city. Notably, while there are many different machine learning (ML) algorithms, including computationally expensive deep learning network, we opted for using artificial neural network (ANN) since an ANN can provide a simple and computationally faster architecture as needed for smart city operations. A widely used performance metrics, namely, accuracy, precision, recall, and F1 score are utilized to evaluate the model. Experiment results with the recent attack dataset demonstrate that the proposed technique can effectively identify the cyber attacks and outperform results reported in an existing work

History

Start Page

1

End Page

6

Number of Pages

6

Start Date

16/12/2020

Finish Date

18/12/2020

ISBN-13

9781665419741

Location

Gold Coast, Qld., Australia

Publisher

IEEE

Place of Publication

Piscataway, NJ

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Federation University; Asia Pacific International College; RMIT;

Era Eligible

Yes

Name of Conference

IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE 2020)

Parent Title

2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)