Biblio
Filters: Keyword is smart home security [Clear All Filters]
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2020. Image Processing Technique for Smart Home Security Based On the Principal Component Analysis (PCA) Methods. 2020 6th International Conference on Wireless and Telematics (ICWT). :1–4.
Smart home is one application of the pervasive computing branch of science. Three categories of smart homes, namely comfort, healthcare, and security. The security system is a part of smart home technology that is very important because the intensity of crime is increasing, especially in residential areas. The system will detect the face by the webcam camera if the user enters the correct password. Face recognition will be processed by the Raspberry pi 3 microcontroller with the Principal Component Analysis method using OpenCV and Python software which has outputs, namely actuators in the form of a solenoid lock door and buzzer. The test results show that the webcam can perform face detection when the password input is successful, then the buzzer actuator can turn on when the database does not match the data taken by the webcam or the test data and the solenoid door lock actuator can run if the database matches the test data taken by the sensor. webcam. The mean response time of face detection is 1.35 seconds.
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2019. Anomaly Detection Models for Smart Home Security. 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). :19–24.
Recent years have seen significant growth in the adoption of smart homes devices. These devices provide convenience, security, and energy efficiency to users. For example, smart security cameras can detect unauthorized movements, and smoke sensors can detect potential fire accidents. However, many recent examples have shown that they open up a new cyber threat surface. There have been several recent examples of smart devices being hacked for privacy violations and also misused so as to perform DDoS attacks. In this paper, we explore the application of big data and machine learning to identify anomalous activities that can occur in a smart home environment. A Hidden Markov Model (HMM) is trained on network level sensor data, created from a test bed with multiple sensors and smart devices. The generated HMM model is shown to achieve an accuracy of 97% in identifying potential anomalies that indicate attacks. We present our approach to build this model and compare with other techniques available in the literature.