Visible to the public Improve Iot Security System Of Smart-Home By Using Support Vector Machine

TitleImprove Iot Security System Of Smart-Home By Using Support Vector Machine
Publication TypeConference Paper
Year of Publication2019
AuthorsHsu, Hsiao-Tzu, Jong, Gwo-Jia, Chen, Jhih-Hao, Jhe, Ciou-Guo
Conference Name2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS)
Date Publishedfeb
KeywordsAccuracy, building occupants safety, civil engineering computing, composability, Databases, Design engineering, domestic safety, doors, electric motors, electrical products control, human resource management, human resources preservation, Internet of Things, Internet of Things security system, machine learning, machine learning algorithms, Monitoring, motors, Network, Predictive Metrics, pubcrawl, Resiliency, security, self-learning, smart-home system, support vector machine, support vector machine algorithm, Support vector machines, Switches, unsupervised learning, Windows, windows (construction)
AbstractThe traditional smart-home is designed to integrate the concept of the Internet of Things(IoT) into our home environment, and to improve the comfort of home. It connects electrical products and household goods to the network, and then monitors and controls them. However, this paper takes home safety as the main axis of research. It combines the past concept of smart-home and technology of machine learning to improve the whole system of smart-home. Through systematic self-learning, it automatically figure out whether it is normal or abnormal, and reports to remind building occupants safety. At the same time, it saves the cost of human resources preservation. This paper make a set of rules table as the basic criteria first, and then classify a part of data which collected by traditional Internet of Things of smart-home by manual way, which includes the opening and closing of doors and windows, the starting and stopping of motors, the connection and interruption of the system, and the time of sending each data to label, then use Support Vector Machine(SVM) algorithm to classify and build models, and then train it. The executed model is applied to our smart-home system. Finally, we verify the Accuracy of anomaly reporting in our system.
Citation Keyhsu_improve_2019