Abstract: A system that uses a client's behavioral biometrics—mouse dynamics, keystrokes, and mouse click patterns—to create a Machine Learning (ML) based customized security model for each client/user to secure website log-ins. The ML model can differentiate the user of interest from an impersonator—human or non-human (robot). The model collects relevant behavioral biometric data from the client when a new account is created by the client/user on a website or when the client initially logs-in to the website. The collected biometric data are used to train an ensemble of ML-based classifiers—a Multilayer Perceptron (MLP) classifier, a Support Vector Machine (SVM) classifier, and an Adaptive Boosting (AdaBoost) classifier—in the model. The trained versions of these classifiers are polled to give an optimal prediction in real-time (while the user is logging in). As a result, real-time fraud detection can be accomplished without impacting the log-in performance of the website.

Abstract: Various systems and methods are provided for defining a CAPTCHA generator that is configured to generate CAPTCHA challenges by using at least a first parameter and a first plurality of values associated with the first parameter; defining an adversary program, where the adversary program is configured to automatically attempt to solve the CAPTCHA challenges; performing a first feedback loop that includes generating a first plurality of CAPTCHA challenges, receiving feedback from a group of human users and feedback from the adversary program; and using the feedback received from the human user and the feedback received from the adversary program to modify a weight associated with a first value among the plurality of values in order to generate future CAPTCHA challenges that create less inconvenience for human users but which are more difficult for adversary programs.

Abstract: As an example, a server hosting a search engine may receive a search query and determine a searched time interval, a searched object, and a searched event. The server may select, based on the searched time interval, a portion of an object-event bipartite graph that was created using information gathered from social media sites. The server may compare attributes of individual events in the portion with attributes of the searched event to identify a set of relevant events. The server may determine objects associated with the relevant events and compare attributes of individual objects with the attributes of the searched object to identify a set of relevant objects. The search engine may provide search results that include the set of relevant objects ordered according to their similarity to the searched object.

Abstract: Various systems and methods are provided for defining a CAPTCHA generator that is configured to generate CAPTCHA challenges by using at least a first parameter and a first plurality of values associated with the first parameter; defining an adversary program, where the adversary program is configured to automatically attempt to solve the CAPTCHA challenges; performing a first feedback loop that includes generating a first plurality of CAPTCHA challenges, receiving feedback from a group of human users and feedback from the adversary program; and using the feedback received from the human user and the feedback received from the adversary program to modify a weight associated with a first value among the plurality of values in order to generate future CAPTCHA challenges that create less inconvenience for human users but which are more difficult for adversary programs.

Abstract: As an example, a server hosting a search engine may receive a search query and determine a searched time interval, a searched object, and a searched event. The server may select, based on the searched time interval, a portion of an object-event bipartite graph that was created using information gathered from social media sites. The server may compare attributes of individual events in the portion with attributes of the searched event to identify a set of relevant events. The server may determine objects associated with the relevant events and compare attributes of individual objects with the attributes of the searched object to identify a set of relevant objects. The search engine may provide search results that include the set of relevant objects ordered according to their similarity to the searched object.

Abstract: A system that uses a client's behavioral biometrics—mouse dynamics, keystrokes, and mouse click patterns—to create a Machine Learning (ML) based customized security model for each client/user to secure website log-ins. The ML model can differentiate the user of interest from an impersonator—human or non-human (robot). The model collects relevant behavioral biometric data from the client when a new account is created by the client/user on a website or when the client initially logs-in to the website. The collected biometric data are used to train an ensemble of ML-based classifiers—a Multilayer Perceptron (MLP) classifier, a Support Vector Machine (SVM) classifier, and an Adaptive Boosting (AdaBoost) classifier—in the model. The trained versions of these classifiers are polled to give an optimal prediction in real-time (while the user is logging in). As a result, real-time fraud detection can be accomplished without impacting the log-in performance of the website.