No abstract

Online social networking sites have become increasingly popular over the last few years. As a result, new interdisciplinary research directions have emerged in which social network analysis methods are applied to networks containing hundreds of millions of users. Unfortunately, links between individuals may be missing either due to an imperfect acquirement process or because they are not yet reflected in the online network (i.e., friends in the real world did not form a virtual connection). The primary bottleneck in link prediction techniques is extracting the structural features required for classifying links. In this article, we propose a set of simple, easy-to-compute structural features that can be analyzed to identify missing links. We show that by using simple structural features, a machine learning classifier can successfully identify missing links, even when applied to a predicament of classifying links between individuals with at least one common friend. We also present a method for calculating the amount of data needed in order to build more accurate classifiers. The new Friends measure and Same community features we developed are shown to be good predictors for missing links. An evaluation experiment was performed on ten large social networks datasets: Academia.edu, DBLP, Facebook, Flickr, Flixster, Google+, Gowalla, TheMarker, Twitter, and YouTube. Our methods can provide social network site operators with the capability of helping users to find known, offline contacts and to discover new friends online. They may also be used for exposing hidden links in online social networks.

Abstract. Inferring Online Social Networks (OSN) group members may help to evaluate the authenticity of an applicant asking to join a certain group, and secure vulnerable populations online, such as children. We propose machine learning based methods, which associate OSN members' affiliation with virtual groups based on personal, topological, and group affiliation features. The study applies and evaluates the methods empirically, on two social networks (Ning and TheMarker). The experimental results demonstrate that one can accurately determine the group genuine members. Our study compares personal, topological and group based classification models. The results show that topological and group affiliation attributes contribute the most to group inference accuracy. Additionally, we examine the relations among the groups and identify group clustering tendencies where some groups are more tightly connected than others.

Recently, a new type of mobile malware applications with self-updating capabilities was found on the official Google Android marketplace. Malware applications of this type cannot be detected using the standard signatures approach or by applying regular static or dynamic analysis methods. In this paper we first describe and analyze this new type of mobile malware and then present a new network-based behavioral analysis for identifying such malware applications. For each application, a model representing its specific traffic pattern is learned locally on the device. Machine-learning methods are used for learning the normal patterns and detection of deviations from the normal application's behavior. These methods were implemented and evaluated on Android devices.

Abstract. Along with recent technological advances more and more new threats and advanced cyber-attacks appear unexpectedly. Developing methods which allow for identification and defense against such unknown threats is of great importance. In this paper we propose new ensemble method (which improves over the known cross-feature analysis, CFA, technique) allowing solving anomaly detection problem in semi-supervised settings using well established supervised learning algorithms. Theoretical correctness of the proposed method is demonstrated. Empirical evaluation results on Android malware datasets demonstrate effectiveness of the proposed approach and its superiority against the original CFA detection method.