Abstract: Described are techniques to enable computers to efficiently determine if they should run a program based on an immediate (i.e., real-time, etc.) analysis of the program. Such an approach leverages highly trained ensemble machine learning algorithms to create a real-time discernment on a combination of static and dynamic features collected from the program, the computer's current environment, and external factors. Related apparatus, systems, techniques and articles are also described.

Abstract: Described are techniques to enable computers to efficiently determine if they should run a program based on an immediate (i.e., real-time, etc.) analysis of the program. Such an approach leverages highly trained ensemble machine learning algorithms to create a real-time discernment on a combination of static and dynamic features collected from the program, the computer's current environment, and external factors. Related apparatus, systems, techniques and articles are also described.

Abstract: Described are techniques to enable computers to efficiently determine if they should run a program based on an immediate (i.e., real-time, etc.) analysis of the program. Such an approach leverages highly trained ensemble machine learning algorithms to create a real-time discernment on a combination of static and dynamic features collected from the program, the computer's current environment, and external factors. Related apparatus, systems, techniques and articles are also described.

Abstract: In one respect, there is provided a system for loading managed applications. The system may include at least one processor and at least one memory. The memory may include program code which when executed by the at least one memory provides operations including: generating a single process, the generating comprising running a native code executable, the running of the native code execute loading a loader manager as part of the single process; loading, by the loader manager running within the single process, a runtime environment corresponding to a non-native code application; and loading, by the loader manager, the non-native code application, the non-native code application being loaded to run as part of the single process.

Abstract: Described are techniques to enable computers to efficiently determine if they should run a program based on an immediate (i.e., real-time, etc.) analysis of the program. Such an approach leverages highly trained ensemble machine learning algorithms to create a real-time discernment on a combination of static and dynamic features collected from the program, the computer's current environment, and external factors. Related apparatus, systems, techniques and articles are also described.

Abstract: A method in one embodiment includes identifying a set of known vulnerabilities and a set of new vulnerabilities in an asset, selecting one or more scripts that include checks for vulnerabilities in a union of the set of known vulnerabilities and the set of new vulnerabilities, and using the selected scripts to scan the asset. Known vulnerabilities and new vulnerabilities may be identified by accessing results of previous scans on the asset. The method may also include identifying a plurality of assets to scan in a network, identifying a plurality of sets of known vulnerabilities and a plurality of sets of new vulnerabilities in substantially all assets in the plurality of assets, and inserting checks for vulnerabilities included in a union of the plurality of sets of known vulnerabilities and the plurality of sets of new vulnerabilities into the selected scripts.

Abstract: A method in one embodiment includes identifying a set of known vulnerabilities and a set of new vulnerabilities in an asset, selecting one or more scripts that include checks for vulnerabilities in a union of the set of known vulnerabilities and the set of new vulnerabilities, and using the selected scripts to scan the asset. Known vulnerabilities and new vulnerabilities may be identified by accessing results of previous scans on the asset. The method may also include identifying a plurality of assets to scan in a network, identifying a plurality of sets of known vulnerabilities and a plurality of sets of new vulnerabilities in substantially all assets in the plurality of assets, and inserting checks for vulnerabilities included in a union of the plurality of sets of known vulnerabilities and the plurality of sets of new vulnerabilities into the selected scripts.