The advancement of the technology enables the use of machine learning in various purpose. One of the interesting uses of the machine learning is to ensure the security and implementation of smart home. It has been found out that the typical systems are sometimes failed to detect the tricks of the buglers. The security system in the smart house will be based on the machine learning, so that it will be intelligent enough to detect the advanced tricks of the buglers.
Problem: The problem of the current security system of the house security is that it cannot always prevent the security threats.
Retrospective solution of the problem: In the past the, these kind of problems were solved by the improvement of the sensor components and the advanced alarming system . However, these steps were helpful at initial stage of implementation but it fails to protect the security in long run as the intruders are also using the advanced technology for the trespassing.
Smart house security system: In order to solve this problem , machine learning , which is an integrated part of artificial intelligence is used (Artem & Vasyl,2017). The whole system based on the machine learning process makes the security system smart. The components of the security system in the smart house are-
Structure of the proposed security system
(Source: Created by the author)
Each of the equipment has different functions in maintaining the security of the house. The functions of the different components are triggered on the basis of the situation happening with the security system in the house.
There are different levels of protections of the security. The first level of the protection is the perimeter protection which is done by the monitoring camera.
In case, if the intruders can still manage to get into the house they will be caught by the monitoring cameras and sensors installed into the house.
The third level of the security may include the hidden cameras and the sensors that responds on the human temperature.
The forth level of the security helps to detect the intruders using the machine learning mechanism. The further level of the security is activated in case the intruder can manage to bypass all the three security levels of the system.
The face detection is also used in maintaining the security of the house. The pattern detection of the machine learning can be used in this case (Jacobsson, Boldt & Carlsson,2016). The system can recognize the face pattern of the owner of the hose and can differentiate it with the pattern of the intruders.
Implementation of the Artificial Intelligence in the system:
Figure 1: Flow chart of the working principal of the proposed system
(Source: Artem & Vasyl,2017)
The network sensor used in the system triggers when it feels the movement of the human in the house. Initially, the video recording and video streaming turned off to save the energy. In case, the sensor is triggered the video camera starts to work automatically. Cameras are turned for the face detection. In case if the system founds the face pattern matching with the pattern recorded n the system it will not set the alarm on. Unless it will set the alarm on and will inform the owner of the house through SMS.
There proposed smart security system is made with some components which have different functionality.
Face recognition is one f the main function of the proposed smart security system. Unlike the previous system , where the detection of the human presence in the room is only done by the sensors, the new system will start to detect the face of the intruders when the sensor detect the human presence. In order to detect the face, many already developed solutions can be applied. OpenCV library can be used for this function in the system. Certain addons for the face recognition can be added to the library. The user interfaces of the proposed system will ge developed using JavaScript. In this case, clmtrackr can be used to for model coordination of the face during the face recognition process.
The identification of the human face is very complex, moreover, sometimes human faces seems similar. In this case, the face recognition system in the smart security system can be divided into three sections-
The automated decision making of the proposed system is based on the application of neural network. Face recognition system detects the face by coordination of the faces and developing the geometric structure of the face. In case, if the face pattern is not match with the face pattern familiar with the system , the next step is decided by the system itself. The decision making of the system based on the data given by the face detection system along with the past data stored in the system, The machine learning implemented in the system will calculate the presence of the residents in terms of hours of the day and date. The calculation of the probability of presence of the residents in the hose is determined by the neural network.
The training of the neural network is done by collecting the data in the normal day long with the presence of the house owners (Zhang, Shan & Huang,2015). Any disruption in the received data when compared with these collected data can be regarded as the abnormal situation for the neural network.
Type of neural network used: Neural network with controlled learning is used. NN technologies can be used in this case.
Size of neural network: Nine input neurons and two output neurons are used in this case. Input neurons are dedicated to the communication channels. Two output neuron are used by the security system software.
Figure 3: Neural Network Structure
(Source: Artem & Vasyl,2017)
The system has proved its usefulness as it also maintains a sound data integration by providing the message alert through email and messages. The children and the old age people can feel safe alone in the house as the system sends the updated security information about the house to the other residents of the house.
The smart security system has proved its effectiveness in maintaining the security of the house. However, there are certain issues those can be highlighted regarding the system. The implementation cost is high, which can be reduced so that everyone can implement the system in their house (Mosenia & Jha,2017). In the technical aspect the modification can be made in decision making field. The modified system can include the users of the system directly and the decision can be made by the machine considering the suggestions from the house owners. The possible flaws in the automation system is needed to be found out, as the intruders can use the advanced technology to exploit the flaws of the system.
Smart security system is not a new concept. However, the main objective of this implementation is to develop a system which will be intelligent enough to detect the intruders along with that the system will inform the residents of the hose about the trespassing that is happening in the house. The system will be helpful for the resident as well as it will be beneficial for maintain the laws. However, the success of the system depends on the implementation of the system .It is assumed the proposed security system will be successful as it is advanced in technical aspects and eliminate the drawbacks of the previous security systems.
References:
Artem, K., & Vasyl, T. (2017, July). Structure and model of the smart house security system using machine learning methods. In Advanced Information and Communication Technologies (AICT), 2017 2nd International Conference on (pp. 105-108). IEEE.
Ateniese, G., Mancini, L. V., Spognardi, A., Villani, A., Vitali, D., & Felici, G. (2015). Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers. International Journal of Security and Networks, 10(3), 137-150.
Bakar, U. A. B. U. A., Ghayvat, H., Hasanm, S. F., & Mukhopadhyay, S. C. (2016). Activity and anomaly detection in smart home: A survey. In Next Generation Sensors and Systems (pp. 191-220). Springer, Cham.
Jacobsson, A., Boldt, M., & Carlsson, B. (2016). A risk analysis of a smart home automation system. Future Generation Computer Systems, 56, 719-733.
Mosenia, A., & Jha, N. K. (2017). A comprehensive study of security of internet-of-things. IEEE Transactions on Emerging Topics in Computing, 5(4), 586-602.
Shen, V. R., Yang, C. Y., & Chen, C. H. (2015). A smart home management system with hierarchical behavior suggestion and recovery mechanism. Computer Standards & Interfaces, 41, 98-111.
Simpson, A. K., Roesner, F., & Kohno, T. (2017, March). Securing vulnerable home IoT devices with an in-hub security manager. In Pervasive Computing and Communications Workshops (PerCom Workshops), 2017 IEEE International Conference on (pp. 551-556). IEEE.
Zhang, J., Shan, Y., & Huang, K. (2015). ISEE Smart Home (ISH): Smart video analysis for home security. Neurocomputing, 149, 752-766.
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