ZigBee networks, with their characteristics of high availability, low power consumption and cost-effective devices, are perfectly appropriate to construct Wireless Sensor Networks (WSNs). Also, the natures of WSN listed above bring significant benefits over traditional communication networks used i n smart home systems. A smart home system is meant to improve the quality of life through offering various automated, interactive and comfortable services, such as sensing and communicating the family member’s health information with their doctors, or remotely controlling the appliances via cellular phones, emails etc. These critical services make the security of personal privacy and the authority of control commands vital issues in Smart Home environments. While the smart home system suffer from many attacks, the security of the smart home system become an important and hard problem. And the authentication is the first parclose to the security of the system. However, according to our analysis, most system fail to achieve the authentication between the user and the device, Which leading to the compromise of the whole system. In this paper, we analyze the authentication challenges between the user the device in WSNs and in smart home Systems. To thoroughly detect, defense and foresee the authentication vulnerabilities existing in smart home networks, we proposed a security evaluation technique based on attack graph generation. We discuss the distinction between the attack graphs deployed in traditional networks and in smart home networks. Furthermore, we apply this technique into an experiment, and the results prove its practicality. And we then suggest a widely used protocol to the smart home authentication system.

Symbolic analysis is a commonly used approach for static bug finding. It usually performs a precise path-by-path symbolic simulation from program inputs. A major challenge is its scalability and precision on interprocedural analysis. The former limits the application to large programs. The latter may lead to many false alarms.