Abstract: An artefact is received. Thereafter, features are extracted from the artefact and a vector is populated. Later, one of a plurality of available classification models is selected. The classification models use different scoring paradigms while providing the same or substantially similar classifications. The vector is input into the selected classification model to generate a score. The score is later provided to a consuming application or process. The classification model can characterize the artefact as being malicious or benign to access, execute, or continue to execute so that appropriate remedial action can be taken or initiated by the consuming application or process. Related apparatus, systems, techniques and articles are also described.

Abstract: An artefact is received. Features are later extracted from the artefact and are used to populate a vector. The vector is input into a classification model to generate a score. This score is then modified using a time-based oscillation function and is provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.

Abstract: An artefact is received and parsed into a plurality of observations. A first subset of the observations are inputted into a machine learning model trained using historical data to classify the artefact. In addition, a second subset of the observations are inputted into a xenospace centroid configured to classify the artefact. Thereafter, the artefact is classified based on a combination of an output of the machine learning model and an output of xenospace centroid. Related apparatus, systems, techniques and articles are also described.

Abstract: A system is provided for training a machine learning model to detect malicious container files. 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 processor provides operations including: processing a container file with a trained machine learning model, wherein the trained machine learning is trained to determine a classification for the container file indicative of whether the container file includes at least one file rendering the container file malicious; and providing, as an output by the trained machine learning model, an indication of whether the container file includes the at least one file rendering the container file malicious. Related methods and articles of manufacture, including computer program products, are also disclosed.

Abstract: An artefact is received. Features are extracted from this artefact which are, in turn, used to populate a vector. The vector is then input into a classification model to generate a score. The score is then modified using a step function so that the true score is not obfuscated. Thereafter, the modified score can be provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.

Abstract: An artefact is received. Features from such artefact are extracted and then populated in a vector. Subsequently, one of a plurality of available dimension reduction techniques are selected. Using the selected dimension reduction technique, the features in the vector are reduced. The vector is then input into a classification model and the score can be provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.

Abstract: An artefact is received. Features are extracted from this artefact which are, in turn, used to populate a vector. The vector is then input into a classification model to generate a score. The score is then modified using a step function so that the true score is not obfuscated. Thereafter, the modified score can be provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.

Abstract: An artefact is received. Features are extracted from this artefact which are, in turn, used to populate a vector. The vector is then input into a classification model to generate a score. The score is then modified using a step function so that the true score is not obfuscated. Thereafter, the modified score can be provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.

Abstract: An artefact is received. Features from such artefact are extracted and then populated in a vector. Subsequently, one of a plurality of available dimension reduction techniques are selected. Using the selected dimension reduction technique, the features in the vector are reduced. The vector is then input into a classification model and the score can be provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.

Abstract: An artefact is received. Features are later extracted from the artefact and are used to populate a vector. The vector is input into a classification model to generate a score. This score is then modified using a time-based oscillation function and is provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.

Abstract: An artefact is received. Thereafter, features are extracted from the artefact and a vector is populated. Later, one of a plurality of available classification models is selected. The classification models use different scoring paradigms while providing the same or substantially similar classifications. The vector is input into the selected classification model to generate a score. The score is later provided to a consuming application or process. The classification model can characterize the artefact as being malicious or benign to access, execute, or continue to execute so that appropriate remedial action can be taken or initiated by the consuming application or process. Related apparatus, systems, techniques and articles are also described.

Abstract: An artefact is received. Features are extracted from this artefact which are, in turn, used to populate a vector. The vector is then input into a classification model to generate a score. The score is then modified using a step function so that the true score is not obfuscated. Thereafter, the modified score can be provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.

Abstract: A system is provided for training a machine learning model to detect malicious container files. 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 processor provides operations including: processing a container file with a trained machine learning model, wherein the trained machine learning is trained to determine a classification for the container file indicative of whether the container file includes at least one file rendering the container file malicious; and providing, as an output by the trained machine learning model, an indication of whether the container file includes the at least one file rendering the container file malicious. Related methods and articles of manufacture, including computer program products, are also disclosed.

Abstract: An artefact is received and parsed into a plurality of observations. A first subset of the observations are inputted into a machine learning model trained using historical data to classify the artefact. In addition, a second subset of the observations are inputted into a xenospace centroid configured to classify the artefact. Thereafter, the artefact is classified based on a combination of an output of the machine learning model and an output of xenospace centroid. Related apparatus, systems, techniques and articles are also described.