In modern wireless communication network, the increased consumer demands for multi-type applications and high quality services have become a prominent trend, and put considerable pressure on the wireless network. In t...In modern wireless communication network, the increased consumer demands for multi-type applications and high quality services have become a prominent trend, and put considerable pressure on the wireless network. In that case, the Quality of Experience(Qo E) has received much attention and has become a key performance measurement for the application and service. In order to meet the users' expectations, the management of the resource is crucial in wireless network, especially the Qo E based resource allocation. One of the effective way for resource allocation management is accurate application identification. In this paper, we propose a novel deep learning based method for application identification. We first analyse the requirement of managing Qo E for wireless communication, and review the limitation of the traditional identification methods. After that, a deep learning based method is proposed for automatically extracting the features and identifying the type of application. The proposed method is evaluated by using the practical wireless traffic data, and the experiments verify the effectiveness of our method.展开更多
Radio frequency identification （RFID） systems suffer many security risks because they use an insecure wireless communication channel between tag and reader. In this paper, we analyze two recently proposed RFID authe...Radio frequency identification （RFID） systems suffer many security risks because they use an insecure wireless communication channel between tag and reader. In this paper, we analyze two recently proposed RFID authentication protocols. Both protocols are vulnerable to tag information leakage and untraceability attacks. For the attack on the first protocol, the adversary only needs to eavesdrop on the messages between reader and tag, and then perform an XOR operation. To attack the second protocol successfully, the adversary may execute a series of carefully designed challenges to determine the tag＇s identification.展开更多
基金supported by NSAF under Grant(No.U1530117)National Natural Science Foundation of China(No.61471022 and No.61201156)
文摘In modern wireless communication network, the increased consumer demands for multi-type applications and high quality services have become a prominent trend, and put considerable pressure on the wireless network. In that case, the Quality of Experience(Qo E) has received much attention and has become a key performance measurement for the application and service. In order to meet the users' expectations, the management of the resource is crucial in wireless network, especially the Qo E based resource allocation. One of the effective way for resource allocation management is accurate application identification. In this paper, we propose a novel deep learning based method for application identification. We first analyse the requirement of managing Qo E for wireless communication, and review the limitation of the traditional identification methods. After that, a deep learning based method is proposed for automatically extracting the features and identifying the type of application. The proposed method is evaluated by using the practical wireless traffic data, and the experiments verify the effectiveness of our method.
文摘Radio frequency identification （RFID） systems suffer many security risks because they use an insecure wireless communication channel between tag and reader. In this paper, we analyze two recently proposed RFID authentication protocols. Both protocols are vulnerable to tag information leakage and untraceability attacks. For the attack on the first protocol, the adversary only needs to eavesdrop on the messages between reader and tag, and then perform an XOR operation. To attack the second protocol successfully, the adversary may execute a series of carefully designed challenges to determine the tag＇s identification.
