IoT -based adversarial attack's effect on cloud data platform services in a smart building context
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
© Academic Conferences International, 2020
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. ...
PublisherAcademic Conferences International
Parent publication ISBN978-1-912764-52-5
ConferenceInternational Conference on Cyber Warfare and Security
Is part of publicationICCWS 2020 : Proceedings of the 15th International Conference on Cyber Warfare and Security
Publication in research information system
MetadataShow full item record
Showing items with similar title or keywords.
Vähäkainu, Petri; Lehto, Martti; Kariluoto, Antti (Peregrine Technical Solutions, 2020)Deficiency of correctly implemented and robust defence leaves Internet of Things devices vulnerable to cyber threats, such as adversarial attacks. A perpetrator can utilize adversarial examples when attacking Machine ...
Artificial Intelligence in Protecting Smart Building’s Cloud Service Infrastructure from Cyberattacks Vähäkainu, Petri; Lehto, Martti; Kariluoto, Antti; Ojalainen, Anniina (Springer, 2020)Gathering and utilizing stored data is gaining popularity and has become a crucial component of smart building infrastructure. The data collected can be stored, for example, into private, public, or hybrid cloud service ...
Terziyan, Vagan; Gryshko, Svitlana; Golovianko, Mariia (Elsevier, 2021)
Golovianko, Mariia; Gryshko, Svitlana; Terziyan, Vagan; Tuunanen, Tuure (Elsevier, 2021)
Countering Adversarial Inference Evasion Attacks Towards ML-Based Smart Lock in Cyber-Physical System Context Vähäkainu, Petri; Lehto, Martti; Kariluoto, Antti (Springer, 2021)Machine Learning (ML) has been taking significant evolutionary steps and provided sophisticated means in developing novel and smart, up-to-date applications. However, the development has also brought new types of hazards ...