Mobile device identification techniques can be applied to secure authentication, and will be of particular importance for the security of mobile networks, such as avoiding spoofing attacks. For Android devices, explicit identifiers, e.g., Android ID, are used to uniquely identify a device. However, permissions are required to gain such identifiers, and this could cause the permission abuse and the leakage of user privacy. To address these issues, we use the combination of implicit identifiers that cannot identify a device individually. We first investigate 38 implicit identifiers that are acquired without requesting any permission. Then, a feature selection algorithm is used to choose effective identifiers as the device fingerprint, and three algorithms are designed to identify the devices. Finally, we conduct experimental evaluations on 50 830 fingerprints from 2239 different Android devices. The empirical results demonstrate the effectiveness and efficiency of our algorithms.

Rogue access point attack is one of the most important security threats for wireless local networks and has attracted great attention from both academia and industry. Utilizing received signal strength information is an effective solution to detect rogue access points. However, the received signal strength information is formed by multi-dimensional received signal strength vectors that are collected by multiple sniffers, and these received signal strength vectors are inevitably lacking in some dimensions due to the limited wireless transmission range and link instability. This will result in high false alarm rate for rogue access point detection. To solve this issue, we propose a received signal strength–based practical rogue access point detection approach, considering missing received signal strength values in received signal strength vectors collected in practical environment. First, we present a preprocessing scheme for received signal strength vectors, eliminating missing values by means of data filling, filtering, and averaging. Then, we perform clustering analysis on the received signal strength vectors, where we design a distance measurement method that dynamically uses partial components in received signal strength vectors to minimize the distance deviation due to missing values. Finally, we conduct the experiments to evaluate the performance of the practical rogue access point detection. The results demonstrate that the practical rogue access point detection can significantly reduce the false alarm rate while ensuring a high detection rate.

Wi-Fi network has an open nature so that it needs to face greater security risks compared to wired network. The MAC address represents the unique identifier of the device, and is easily obtained by an attacker. Therefore MAC address randomization is proposed to protect the privacy of devices in a Wi-Fi network. However, implicit identifiers are used by attackers to identify user’s device, which can cause the leakage of user’s privacy. We propose device identification based on 802.11ac probe request frames. Here, a detailed analysis on the effectiveness of 802.11ac fields is given and a novel device identification method based on deep learning whose average f1-score exceeds 99% is presented. With a purpose of preventing attackers from obtaining relevant information by the device identification method above, we design a novel defense mechanism based on stream cipher. In that case, the original content of probe request frame is hidden by encrypting probe request frames and construction of probe request is reserved to avoid the finding of attackers. This defense mechanism can effectively reduce the performance of the proposed device identification method whose average f1-score is below 30%. In general, our research on attack and defense mechanism can preserve device privacy better.

The technologies for wireless broadband access and mobile devices have been changing very fast.People expect that information and services can be delivered from the Internet anytime and anywhere even while they are moving.Under the current circumstances,the Mobile Internet becomes an emerging technology and expects to grow up in an accelerated pace.The challenges we are facing from the Mobile Internet come from the mobile devices,access networks,services and security and privacy protection.It is essential for us to further explore the fundamental theories and key technologies to meet the requirements from our national information industry and broad research communities.We conduct a survey of the Mobile Internet on four aspects,namely mobile devices,access networks,services and security and privacy protection.We further give our research on test-bed of WLAN base station and performance optimization of wireless mesh networks.Finally we conclude the paper by elaborating the future research directions on the Mobile Internet.

Security is a critical concern in low-power IoT (Internet of Things) wireless sensors because these resource constrained devices are easy to attack and meanwhile the energy constraint sensors will consume a lot of energy to run algorithms for security purposes. We study the energy efficiency data transmission problem in IoT sensors that use capacitors to harvest wireless energy while considering the energy cost for running security algorithms. Energy harvesting with capacitors has the characteristic that the energy harvesting rate varies over time, and it is getting slower and slower as the capacitor gets more and more wireless energy. This observation will result in a trade-off for data transmission in two ways: (1) dividing data into more number of packets, thus the sensors can receive wireless energy at a higher harvesting rate, but it will result in extra energy consumption; (2) dividing data into less numbers of packets—in this way, the sensor cannot utilize the high harvesting rate, but the extra energy cost is less. We studied two sets of this problem where the low-power sensors can harvest enough wireless energy or not, and give algorithms to transmit all the data or as much data as possible, respectively, while taking into account extra cost. The theoretical performance of the proposed algorithms is also analyzed. Both theoretical analysis and extensive simulations show that the proposed algorithms have good performance.