Abstract:
Cloud computing (CC) and the Internet of Things (IoT) are widely used technologies
across a variety of fields including science, medicine, security, and economics, providing
efficient data management and automation capabilities. However, data quality
and security challenges remain significant concerns for both technologies. Low-cost
sensors and controllers that make up IoT devices are often vulnerable to security
breaches and generate diverse and difficult-to-manage data. Although CC provides
services for diverse user requirements, its data security and management transparency
are still areas of concern. To mitigate vulnerabilities, IoT data must be stored in cloud
or edge services using SQL or NoSQL databases, and secure connections between CC
and IoT must be established. CC provides high-performance hardware and software
to users on a rental basis, reducing the need for significant infrastructure investments.
This study investigates the interaction between CC and IoT, focusing on protocols
and security measures for microcontrollers that generate and transmit data to the
cloud, enable real-time data retrieval after local server storage, and facilitate anomaly
detection through analysis. The primary objective of this thesis is to enhance MLbased
anomaly detection in data management through the integration of IoT and
cloud computing, while also identifying benefits and challenges associated with integrating
IoT and cloud technologies, assessing the security measures implemented in
database management for IoT devices and cloud computing systems, and providing
insights into optimal methods for implementing database management and security
measures in the interaction between IoT and cloud technologies. Experimental tests
and evaluations demonstrate the effectiveness of our proposed solution