Abstract: Computer implemented methods, systems, and computer program products include program code executing on a processor(s) that monitors execution of a script to complete a task via a command line interface, and based on completing the task, generates, based on the script, a non-directional representation of the script and a directional representation of the script. The processor(s) clusters the non-directional representation of the script with non-directional representations of similar scripts (successfully in advance of the monitoring). The processor(s) merges the directional representation of the script to a cluster comprising the non-directional representation of the script.

Abstract: According to one embodiment, a method, computer system, and computer program product for physical object tracking in virtual reality is provided. The present invention may include receiving input from a user identifying a specific physical object during a virtual reality session in a virtual reality environment; identifying the specific physical object in the physical proximity of the user; tracking the location of the specific physical object relative to the user; and responsive to receiving a command from the user, displaying the specific physical object to the user through a portal in the virtual reality environment.

Abstract: Techniques are provided for implanting emovectors with virtual representatives. In one embodiment, the techniques involve generating a virtual representative based on visual data of a first user, generating an emovector of a second user of a virtual environment, generating an input of a machine learning model based on the emovector of the second user, and controlling the virtual representative based on an output of the machine learning model.

Abstract: Examples of techniques for discriminant power based threat detection are described herein. An aspect includes identifying a plurality of detector names associated with an indicator of compromise, wherein each of the plurality of detector names has a respective associated discriminant power. Another aspect includes determining a plurality of malware families, wherein each malware family of the plurality of malware families is linked to at least one detector name of the plurality of detector names. Another aspect includes, for each malware family of the plurality of malware families, determining a sum of the associated discriminant power of any detector names that are linked to the malware family. Another aspect includes determining that the indicator of compromise belongs to a malware family of the plurality of malware families that has a highest sum.

Abstract: Examples of techniques for discriminant power based threat detection are described herein. An aspect includes identifying a plurality of detector names associated with an indicator of compromise, wherein each of the plurality of detector names has a respective associated discriminant power. Another aspect includes determining a plurality of malware families, wherein each malware family of the plurality of malware families is linked to at least one detector name of the plurality of detector names. Another aspect includes, for each malware family of the plurality of malware families, determining a sum of the associated discriminant power of any detector names that are linked to the malware family. Another aspect includes determining that the indicator of compromise belongs to a malware family of the plurality of malware families that has a highest sum.