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
Date
2020Copyright
© 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.
...
Publisher
SpringerParent publication ISBN
978-3-030-35745-0Is part of publication
Cyber Defence in the Age of AI, Smart Societies and Augmented HumanityISSN Search the Publication Forum
1613-5113Keywords
Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/35182733
Metadata
Show full item recordCollections
License
Related items
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 ... -
Adversarial Attack’s Impact on Machine Learning Model in Cyber-Physical Systems
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 ... -
On Attacking Future 5G Networks with Adversarial Examples : Survey
Zolotukhin, Mikhail; Zhang, Di; Hämäläinen, Timo; Miraghaei, Parsa (MDPI AG, 2023)The introduction of 5G technology along with the exponential growth in connected devices is expected to cause a challenge for the efficient and reliable network resource allocation. Network providers are now required to ... -
Instrumenting OpenCTI with a Capability for Attack Attribution Support
Ruohonen, Sami; Kirichenko, Alexey; Komashinskiy, Dmitriy; Pogosova, Mariam (MDPI AG, 2024)In addition to identifying and prosecuting cyber attackers, attack attribution activities can provide valuable information for guiding defenders’ security procedures and supporting incident response and remediation. However, ... -
APT Cyber-attack Modelling : Building a General Model
Lehto, Martti (Academic Conferences International Ltd, 2022)The global community continues to experience an increase in the scale, sophistication, and successful perpetration of cyber-attacks. As the quantity and value of electronic information have increased, so too have the efforts ...