Android, the most popular mobile operating system, has attracted millions of users around the world. Meanwhile, the number of new Android malware instances has grown exponentially in recent years. On the one hand, existing Android malware detection systems have shown that distilling the program semantics into a graph representation and detecting malicious programs by conducting graph matching are able to achieve high accuracy on detecting Android malware. However, these traditional graph-based approaches always perform expensive program analysis and suffer from low scalability on malware detection. On the other hand, because of the high scalability of social network analysis, it has been applied to complete large-scale malware detection. However, the social-network-analysis-based method only considers simple semantic information (i.e., centrality) for achieving market-wide mobile malware scanning, which may limit the detection effectiveness when benign apps show some similar behaviors as malware. In this article, we aim to combine the high accuracy of traditional graph-based method with the high scalability of social-network-analysis--based method for Android malware detection. Instead of using traditional heavyweight static analysis, we treat function call graphs of apps as complex social networks and apply social-network--based centrality analysis to unearth the central nodes within call graphs. After obtaining the central nodes, the average intimacies between sensitive API calls and central nodes are computed to represent the semantic features of the graphs. We implement our approach in a tool called IntDroid and evaluate it on a dataset of 3,988 benign samples and 4,265 malicious samples. Experimental results show that IntDroid is capable of detecting Android malware with an F-measure of 97.1% while maintaining a True-positive Rate of 99.1%. Although the scalability is not as fast as a social-network-analysis--based method (i.e., MalScan ), compared to a traditional graph-based method, IntDroid is more than six times faster than MaMaDroid . Moreover, in a corpus of apps collected from GooglePlay market, IntDroid is able to identify 28 zero-day malware that can evade detection of existing tools, one of which has been downloaded and installed by more than ten million users. This app has also been flagged as malware by six anti-virus scanners in VirusTotal, one of which is Symantec Mobile Insight .

With the increasing number of layers and parameters in neural networks, the energy consumption of neural networks has become a great concern to society, especially to users of handheld or embedded devices. In this paper, we investigate the robustness of neural networks against energy-oriented attacks. Specifically, we propose ILFO (Intermediate Output-Based Loss Function Optimization) attack against a common type of energy-saving neural networks, Adaptive Neural Networks (AdNN). AdNNs save energy consumption by dynamically deactivating part of its model based on the need of the inputs. ILFO leverages intermediate output as a proxy to infer the relation between input and its corresponding energy consumption. ILFO has shown an increase up to 100 % of the FLOPs (floating-point operations per second) reduced by AdNNs with minimum noise added to input images. To our knowledge, this is the first attempt to attack the energy consumption of an AdNN.