Near-field communication (NFC) is a set of communication protocols that enable two electronic devices. Its security and reliability are welcomed by mobile terminal manufactures, banks, telecom operators, and third-party payment platforms. Simultaneously, it has also drawn more and more attention from hackers and attackers, and NFC-enabled devices are facing increasing threats. To improve the security of the NFC technology, the paper studied the technology of discovering security vulnerabilities of NFC Data Exchange Format (NDEF), the most important data transmission protocol. In the paper, we proposed an algorithm, GTCT (General Test Case Construction and Test), based on fuzzing to construct test cases and test the NDEF protocol. GTCT adopts four strategies to construct test cases, manual, generation, mutation, and “reverse analysis,” which can detect logic vulnerabilities that fuzzing cannot find and improve the detection rate. Based on GTCT, we designed an NDEF vulnerability discovering framework and developed a tool named “GNFCVulFinder” (General NFC Vulnerability Finder). By testing 33 NFC system services and applications on Android and Windows Phones, we found eight vulnerabilities, including DoS vulnerabilities of NFC service, logic vulnerabilities about opening Bluetooth/Wi-Fi/torch, design flaws about the black screen, and DoS of NFC applications. Finally, we give some security suggestions for the developer to enhance the security of NFC.

With the development of Internet technology, network security is under diverse threats. In particular, attackers can spread malicious uniform resource locators (URL) to carry out attacks such as phishing and spam. The research on malicious URL detection is significant for defending against these attacks. However, there are still some problems in the current research. For instance, malicious features cannot be extracted efficiently. Some existing detection methods are easy to evade by attackers. We design a malicious URL detection model based on a dynamic convolutional neural network (DCNN) to solve these problems. A new folding layer is added to the original multilayer convolution network. It replaces the pooling layer with the k-max-pooling layer. In the dynamic convolution algorithm, the width of feature mapping in the middle layer depends on the vector input dimension. Moreover, the pooling layer parameters are dynamically adjusted according to the length of the URL input and the depth of the current convolution layer, which is beneficial to extracting more in-depth features in a wider range. In this paper, we propose a new embedding method in which word embedding based on character embedding is leveraged to learn the vector representation of a URL. Meanwhile, we conduct two groups of comparative experiments. First, we conduct three contrast experiments, which adopt the same network structure and different embedding methods. The results prove that word embedding based on character embedding can achieve higher accuracy. We then conduct the other three experiences, which use the same embedding method proposed in this paper and use different network structures to determine which network is most suitable for our model. We verify that the model designed in this paper has the highest accuracy (98%) in detecting malicious URL through these experiences.