Abstract: Methods and systems for network scanning activity detection are disclosed. The methods and systems include: obtaining darknet data from darknet monitoring sensors; applying the darknet data to a trained machine learning model; obtaining one or more labels of honeypot data corresponding to the darknet data based on the trained machine learning model; and provide a result of threat behaviors of internet protocols based on the one or more labels. Other aspects, embodiments, and features are also claimed and described.

Abstract: Systems and methods are disclosed that implement a near-real-time approach for characterizing Internet Background Radiation to detect and characterize network scanner activity. Various implementations can use deep representation learning to address the high dimensionality of the scanning data. In one experiment, the combination of DNN-based Autoencoder algorithms and K-means clustering was used to detect scanner activity. The insights that can be gained from clustering Darknet data can be used in instances of high-intensity scanners, malware classes that are either newly emerging or long-standing, and other situations.

Abstract: Systems and methods that facilitate detection of cross-channel fraud are discussed. Detection of cross-channel fraud includes analyzing one or more fraud accounts previously subject to fraud. The analyzing includes identifying one or more common patterns of events associated with fraud. Detection of cross-channel fraud also includes determining a cross-channel fraud metric that measures a likelihood of fraud and monitoring a plurality of events associated with a customer. The detection of cross-channel fraud also includes determining a first account fraud probability associated with the customer based at least in part on a comparison between the plurality of events and the one or more common patterns of events. The plurality of events are analyzed in connection with the cross-channel fraud metric to determine an account cross-channel fraud score associated with the customer.

Abstract: Collaborative agents for simulating teamwork (CAST) are provided with a recognition-primed decision (RPD) model, thereby enhancing analysis through linking and sharing information using knowledge and experience distributed among team members. The RPD model is integrated within a CAST architecture to the extent that agents can proactively seek and fuse information to enhance the quality and timeliness of the decision-making process. The approach, which is applicable to both human assistants and virtual teammates, can approximately track human's decision-making process and effectively interact with human users. A disclosed example relates to teams of agents analyzing terrorist activities.

Abstract: Collaborative agents for simulating teamwork (CAST) are provided with a recognition-primed decision (RPD) model, thereby enhancing analysis through linking and sharing information using knowledge and experience distributed among team members. The RPD model is integrated within a CAST architecture to the extent that agents can proactively seek and fuse information to enhance the quality and timeliness of the decision-making process. The approach, which is applicable to both human assistants and virtual teammates, can approximately track human's decision-making process and effectively interact with human users. A disclosed example relates to teams of agents analyzing terrorist activities.

Abstract: The prevent invention provides a fire prevention automation commanding control system using satellite-location/geography-information, which uses a central processing computer to integrate radio transmission, satellite location, geography information, facility control, image monitoring, underground piping control, piping monitoring, mobile/stationary communication, and a fire detection alarm device into an integrated fire prevention system performing automatic command, control, communication, information processing and on-site evidence collection, i.e., so-called C31 system, so that it is possible to rapidly detect fire and effectively command and control the rescue operation.

Abstract: The present invention relates to an intelligent taxi total service system having an intelligent automatic management system, which provides automation functions such as inquiring, broadcasting positioning, tracing, recording, searching, confirming, charging, receipt printing, navigating, real time traffic information, security, emergency help requesting and communication, so as to achieve a total service system with efficacy of high security, high reliability, and time saving.

Abstract: A system for recovering from certain types of system software startup problems employs a user-hidden secondary startup volume stored in the computer. During a normal startup procedure, if an error is detected which would normally result in a startup failure, the computer's startup routine branches to an alternate startup application stored in the secondary volume. This startup application boots the computer from a minimal operating system stored in the secondary volume. As a result, the user is not left with a non-functioning computer. As further features of the invention, the startup application can attempt to automatically fix the detected problem, or it can suggest possible steps to be taken by the user, in order to fix the problem that resulted in the need to use the alternate startup application.