IoT devices are now enriching people’s life. However, the security of IoT devices seldom attracts manufacturers’ attention. There are already some solutions to the problem of connecting a smart device to a user’s wireless network based on the 802.11 transmission such as Smart Config from TI. However, it is insecure in many situations, and it does not have a satisfactory transmission speed, which does not mean that it has a low bit rate. It usually takes a long time for the device to recognize the data it receives and decode them. In this paper, we propose a new Wi-Fi connection method based on audio waves. This method is based on MFSK (Multiple frequency-shift keying) and works well in short distance, which enables the correctness and efficiency. In addition, audio waves can hardly be eavesdropped, which provides higher security than other methods. We also put forward an encryption solution by using jamming signal, which can greatly improve the security of the transmission.
Cloud computing enables users to utilize IT resources conveniently with low‐level cost, but it also brings some new threats. The co‐resident attack is one of the typical examples, where malicious users steal information from legal users by starting a virtual machine (VM) and building a side‐channel between VMs on the same server. Most of current studies focus mainly on defending the side‐channel attack, which requires modifications to the existing underlying architecture of cloud platforms. Some studies focus on security‐aware VMs deployment policies that can defeat co‐resident attacks through reduction of co‐residences. However, most of these studies ignore workload‐balance and energy‐efficiency. In addition, they neglect that data held in different VMs should have different security levels. Motivated by these issues, in this article, we first formalize the multi‐attackers co‐resident attack problem. The co‐located VMs rate (CVR) metric is proposed to evaluate the security level in cloud and a security‐aware policy named least‐danger‐based‐on‐reputation (LDBR) is presented to minimize the metric. Also, we propose extensional LDBR (E‐LDBR) policy to balance security, workload‐balance and energy consumption, and weighted‐LDBR (W‐LDBR) policy that considers users' different security levels. Experimental results indicate that LDBR outperforms PSSF (previous‐selected‐server‐first) by 15.02%‐22.07% in security, E‐LDBR is more balanced in workload and consumes less energy consumption, and W‐LDBR can reduce security threats by 13.3% compared with PSSF.
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