Search results for "attack vectors"
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Artificial Intelligence in Protecting Smart Building’s Cloud Service Infrastructure from Cyberattacks
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 buildin…
IoT -based adversarial attack's effect on cloud data platform services in a smart building context
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…