A4 Artikkeli konferenssijulkaisussa
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-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Vähäkainu, Petri; Lehto, Martti; Kariluoto, Antti

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

Emojulkaisun toimittajat: Payne, Brian K.; Wu, Hongyi

Konferenssi:

Konferenssin paikka ja aika: Norfolk, United States, 12.-13.3.2020

ISBN: 978-1-912764-52-5

Lehti tai sarja: The proceedings of the ... international conference on cyber warfare and security

ISSN: 2048-9870

eISSN: 2048-9889

Julkaisuvuosi: 2020

Artikkelin sivunumerot: 457-465

Kirjan kokonaissivumäärä: 658

Kustantaja: Academic Conferences International

Kustannuspaikka: Reading

Julkaisumaa: Britannia

Julkaisun kieli: englanti

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

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus: 

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/78828

Tiivistelmä

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.

YSO-asiasanat: älytekniikka; älytalot; esineiden internet; pilvipalvelut; tietoturva; tekoäly; verkkohyökkäykset

Vapaat asiasanat: adversarial attacks; artificial intelligence-based applications; attack vectors; cloud service; data platform

Liittyvät organisaatiot

OKM-raportointi: Kyllä

Raportointivuosi: 2020

JUFO-taso: 1