Android operating system occupies a high share in the mobile terminal market. It promotes the rapid development of Android applications (apps). However, the emergence of Android malware greatly endangers the security of Android smartphone users. Existing research works have proposed a lot of methods for Android malware detection, but they did not make the utilization of apps' functional category information so that the strong similarity between benign apps in the same functional category is ignored. In this paper, we propose an Android malware detection scheme based on the functional classification. The benign apps in the same functional category are more similar to each other, so we can use less features to detect malware and improve the detection accuracy in the same functional category. The aim of our scheme is to provide an automatic application functional classification method with high accuracy. We design an Android application functional classification method inspired by the hyperlink induced topic search (HITS) algorithm. Using the results of automatic classification, we further design a malware detection method based on app similarity in the same functional category. We use benign apps from the Google Play Store and use malware apps from the Drebin malware set to evaluate our scheme. The experimental results show that our method can effectively improve the accuracy of malware detection.

The Next Generation Internet Initiative was launched in the U.S. to advance key networking technologies that will enable a new wave of applications on the Internet. Now, in its third year, the program has launched and fostered over one hundred new research projects in partnership with academic, industrial and government laboratories. One key research area that has been emphasized within the program is the next-generation optical networking. Given the ever increasing demand for network bandwidth, and the recent phenomenal advances in WDM technologies, the Next Generation Internet is expected to be an IP-based optical WDM network. As IP over WDM networking technologies mature, a number of important architectural, management and control issues have surfaced. These issues need to be addressed before a true Next Generation Optical Internet can emerge. This paper provides a brief introduction to the overall goals and activities of DARPA's NGI program and describes the key architectural, management, and control issues for the Optical Internet. We review the different IP/WDM networking architectural models and their tradeoffs. We outline and discuss several management and control issues and possible solutions related to the configuration, fault, and performance management of IP over dynamic WDM networks. We present an analysis and supporting simulation results demonstrating the potential benefits of dynamic IP over WDM networks. We then discuss the issues related to IP/WDM traffic engineering in more detail, and present the approach taken in the NGI SuperNet Network Control and Management Project funded by DARPA. In particular, we motivate and present an innovative integrated traffic-engineering framework for re-configurable IP/WDM networks. It builds on the strength of Multi-Protocol Label Switching (MPLS) for fine-grain IP load balancing, and on the strength of Re-configurable WDM networking for reducing the IP network's weighted-hop-distance, and for expanding the bottleneck bandwidth.

In this paper, we modify the Legendre-Sidelnikov sequence which was defined by M. Su and A. Winterhof and consider its exact autocorrelation values. This new sequence is balanced for any p,q and proved to possess low autocorrelation values in most cases.

The thermal imaging pedestrian segmentation system has excellent performance in different illumination conditions, but it has some drawbacks(e.g., weak pedestrian texture information, blurred object boundaries). Meanwhile, high-performance large models have higher latency on edge devices with limited computing performance. To solve the above problems, in this paper, we propose a real-time thermal infrared pedestrian segmentation method. The feature extraction layers of our method consist of two paths. Firstly, we utilize the lossless spatial downsampling to obtain boundary texture details on the spatial path. On the context path, we use atrous convolutions to improve the receptive field and obtain more contextual semantic information. Then, the parameter-free attention mechanism is introduced at the end of the two paths for effective feature selection, respectively. The Feature Fusion Module (FFM) is added to fuse the semantic information of the two paths after selection. Finally, we accelerate method inference through multi-threading techniques on the edge computing device. Besides, we create a high-quality infrared pedestrian segmentation dataset to facilitate research. The comparative experiments on the self-built dataset and two public datasets with other methods show that our method also has certain effectiveness. Our code is available at https://github.com/mcjcs001/LEIPNet.

Software defect prediction (SDP) plays a vital role in allocating testing resources reasonably and ensuring software quality. When there are not enough labeled historical modules, considerable semi-supervised SDP methods have been proposed, and these methods utilize limited labeled modules and abundant unlabeled modules simultaneously. Nevertheless, most of them make use of traditional features rather than the powerful deep feature representations. Besides, the cost of the misclassification of the defective modules is higher than that of defect-free ones, and the number of the defective modules for training is small. Taking the above issues into account, we propose a cost-sensitive and sparse ladder network (CSLN) for SDP. We firstly introduce the semi-supervised ladder network to extract the deep feature representations. Besides, we introduce the cost-sensitive learning to set different misclassification costs for defective-prone and defect-free-prone instances to alleviate the class imbalance problem. A sparse constraint is added on the hidden nodes in ladder network when the number of hidden nodes is large, which enables the model to find robust structures of the data. Extensive experiments on the AEEEM dataset show that the CSLN outperforms several state-of-the-art semi-supervised SDP methods.

Nowadays, malware is a serious threat to the Internet. Traditional signature-based malware detection method can be easily evaded by code obfuscation. Therefore, many researchers use the high-level structure of malware like function call graph, which is impacted less from the obfuscation, to find the malware variants. However, existing graph match methods rely on approximate calculation, which are inefficient and the accuracy cannot be effectively guaranteed. Inspired by the successful application of graph convolutional network in node classification and graph classification, we propose a novel malware similarity metric method based on graph convolutional network. We use graph convolutional network to compute the graph embedding vectors, and then we calculate the similarity metric of two graph based on the distance between two graph embedding vectors. Experimental results on the Kaggle dataset show that our method can applied to the graph based malware similarity metric method, and the accuracy of clustering application with our method reaches to 97% with high time efficiency.