A4 Article in conference proceedings
IoT -based adversarial attack's effect on cloud data platform services in a smart building context (2020)

Vähäkainu, P., Lehto, M., & Kariluoto, A. (2020). IoT -based adversarial attack's effect on cloud data platform services in a smart building context. In B. K. Payne, & H. Wu (Eds.), ICCWS 2020 : Proceedings of the 15th International Conference on Cyber Warfare and Security (pp. 457-465). Academic Conferences International. The proceedings of the ... international conference on cyber warfare and security. https://doi.org/10.34190/ICCWS.20.041

JYU authors or editors

Publication details

All authors or editors: Vähäkainu, Petri; Lehto, Martti; Kariluoto, Antti

Parent publication: ICCWS 2020 : Proceedings of the 15th International Conference on Cyber Warfare and Security

Parent publication editors: Payne, Brian K.; Wu, Hongyi

Conference:

Place and date of conference: Norfolk, United States, 12.-13.3.2020

ISBN: 978-1-912764-52-5

Journal or series: The proceedings of the ... international conference on cyber warfare and security

ISSN: 2048-9870

eISSN: 2048-9889

Publication year: 2020

Pages range: 457-465

Number of pages in the book: 658

Publisher: Academic Conferences International

Place of Publication: Reading

Publication country: United Kingdom

Publication language: English

DOI: https://doi.org/10.34190/ICCWS.20.041

Publication open access: Not open

Publication channel open access: 

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/78828

Abstract

IoT sensors and sensor networks are widely employed in businesses. The common problem is a remarkable number of IoT device transactions are unencrypted. Lack of correctly implemented and robust defense leaves the organization's IoT devices vulnerable to numerous cyber threats, such as adversarial and man-in-the-middle attacks or malware infections. A perpetrator can utilize adversarial examples when attacking machine learning (ML) models, such as convolutional neural networks (CNN) or deep neural networks (DNN) used, e.g., in DaaS cloud data platform service of smart buildings. DaaS cloud data platform's function in this study is to connect data from multiple IoT sensors, databases, private on-premises cloud services, public or hybrid cloud services into a metadata database. This study focuses on reviewing adversarial attack threats towards artificial intelligence systems in the smart building's context where the DaaS cloud data platform services under various information propagation chain structures utilizing ML models and reviews. Adversarial examples can be malicious inputs to ML models providing erroneous model outputs while appearing to be unmodified in human eyes. This kind of attack can knock out the classifier, prevent ML model from generalizing well, and from learning high-level representation, but instead to learn superficial dataset regularity. The purpose of this study is to investigate, detect, and prevent cyber-attack vectors, such as adversarial attacks towards DaaS cloud data platform.

Keywords: intelligent systems; smart houses; Internet of things; cloud services; data security; artificial intelligence; cyber attacks

Free keywords: adversarial attacks; artificial intelligence-based applications; attack vectors; cloud service; data platform

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2020

JUFO rating: 1