Abstract: A non-transitory computer readable medium for detection of objects of one or more given classes, the non-transitory computer readable medium stores instructions for: performing an aerial images based (AIB) detection to find multiple object locations; wherein the performing of the AIB detection comprises applying an AIB detection machine learning process on aerial images; performing a ground-level based (GLB) detection of the objects, based on the multiple object locations; wherein the performing of the GLB detection comprises applying a GLB detection machine learning process on ground-level images; and classifying, by a classification machine learning process, objects captured in the ground-level images to a plurality of classes, wherein the plurality of classes comprise the one or more given classes; and responding to the classifying, when finding one or more objects of the one or more given classes.

Abstract: A method for malicious source code detection, the method includes (a) obtaining, by a processing circuit, an embedding of a source code for a function; (b) applying, by the processing circuit, an anomaly detection process on the embedding of the source code; and (c) concluding, by the processing circuit, that the source code comprises a malicious code when the anomaly detection process indicates that the embedding of the source code is an outlier.

Abstract: A method for protecting user privacy in an online social network, according to which negative examples of fake profiles and positive examples of legitimate profiles are chosen from the database of existing users of the social network. Then, a predetermined set of features is extracted for each chosen fake and legitimate profile, by dividing the friends or followers of the chosen examples to communities and analyzing the relationships of each node inside and between the communities. Classifiers that can detect other existing fake profiles according to their features are constructed and trained by using supervised learning.

Abstract: The present invention relates to a method for determining the hierarchical structure of an organization, using data from a social network, for example, Facebook. The method is partially indirect, as it includes some determinations with respect to the departmental division of the organization as well as determination of leadership personnel that are not explicitly indicated anywhere in the social network. The method of the invention is mainly based on analyzing the connections between people, or more particularly the method is based on analysis of “friends” lists of persons within Facebook (or another social network).

Abstract: A method for protecting user privacy in an online social network, according to which negative examples of fake profiles and positive examples of legitimate profiles are chosen from the database of existing users of the social network. Then, a predetermined set of features is extracted for each chosen fake and legitimate profile, by dividing the friends or followers of the chosen examples to communities and analyzing the relationships of each node inside and between the communities. Classifiers that can detect other existing fake profiles according to their features are constructed and trained by using supervised learning.

Abstract: A method for protecting user privacy in an online social network, comprising the steps of defining, for a given primary user of an online social network who is authorized to post multimedia information in an account of the social network, a personal profile type that characterizes a level of desired privacy and that is selected from a group of predetermined profile types; defining a personal profile type selected from the group for each of a plurality of secondary users who are interested in accessing posted multimedia information of the primary user while functioning as a friend thereof; and denying a request for friendship initiated by one of the plurality of secondary users when the profile type of the primary user and of the one of the plurality of secondary users are incompatible as defined by predetermined rules, that may be stored in the privacy setting module.