Artificial Intelligence in Protecting Smart Building’s Cloud Service Infrastructure from Cyberattacks
Vähäkainu, P., Lehto, M., Kariluoto, A., & Ojalainen, A. (2020). Artificial Intelligence in Protecting Smart Building’s Cloud Service Infrastructure from Cyberattacks. In H. Jahankhani, S. Kendzierskyj, N. Chelvachandran, & J. Ibarra (Eds.), Cyber Defence in the Age of AI, Smart Societies and Augmented Humanity (pp. 289-315). Springer. Advanced Sciences and Technologies for Security Applications. https://doi.org/10.1007/978-3-030-35746-7_14
© Springer Nature Switzerland AG 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 infrastructure or distributed service by utilizing data platforms. The stored data can be used when implementing services, such as building automation (BAS). Cloud services, IoT sensors, and data platforms can face several kinds of cybersecurity attack vectors such as adversarial, AI-based, DoS/DDoS, insider attacks. If a perpetrator can penetrate the defenses of a data platform, she can cause significant harm to the system. For example, the perpetrator can disrupt a building’s automatic heating system or break the heating equipment by using a suitable attack vector for a data platform. This chapter focuses on examining possibilities to protect cloud storage or data platforms from incoming cyberattacks by using, for instance, artificial-intelligence-based tools or trained neural networks that can detect and prevent typical attack vectors. ...
Parent publication ISBN978-3-030-35745-0
Is part of publicationCyber Defence in the Age of AI, Smart Societies and Augmented Humanity
Publication in research information system
MetadataShow full item record
Showing items with similar title or keywords.
IoT -based adversarial attack's effect on cloud data platform services in a smart building context Vähäkainu, Petri; Lehto, Martti; Kariluoto, Antti (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 ...
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 ...
Practices and Infrastructures for Machine Learning Systems : An Interview Study in Finnish Organizations Muiruri, Dennis; Lwakatare, Lucy Ellen; Nurminen, Jukka K.; Mikkonen, Tommi (Institute of Electrical and Electronics Engineers (IEEE), 2022)Using interviews, we investigated the practices and toolchains for machine learning (ML)-enabled systems from 16 organizations across various domains in Finland. We observed some well-established artificial intelligence ...
Voutilainen, Janne; Kari, Martti (Academic Conferences International, 2020)In 2019, e-criminals adopted new tactics to demand enormous ransoms from large organizations by using ransomware, a phenomenon known as “big game hunting.” Big game hunting is an excellent example of a sophisticated and ...
Simola, Jussi; Lehto, Martti; Rajamäki, Jyri (Academic Conferences International, 2021)Centralized hybrid emergency model with predictive emergency response functions are necessary when the purpose is to protect the critical infrastructure (CI). A shared common operational picture among Public Protection and ...