Abstract: Techniques and architectures for representing data with one or more n-dimensional representations and/or using one or more models to identify malware are described herein. For example, the techniques and architectures may determine one or more coordinates for one or more points based on one or more sets of bits in the data and generate an n-dimensional representation for the data based on the one or more points. The techniques and architectures may evaluate the n-dimensional representation with one or more machine-trained models to detect malware.

Abstract: Techniques and architectures for representing data with one or more n-dimensional representations and/or using one or more models to identify malware are described herein. For example, the techniques and architectures may determine one or more coordinates for one or more points based on one or more sets of bits in the data and generate an n-dimensional representation for the data based on the one or more points. The techniques and architectures may evaluate the n-dimensional representation with one or more machine-trained models to detect malware.

Abstract: Techniques and architectures include representing data with one or more n-dimensional representations and using one or more analysis models to identify target properties associated with the one or more n-dimensional representations. For example, data can be represented as a plurality of points in a coordinate system. A set of points in the plurality of points can be identified and an n-dimensional model can be generated for the set of points. The n-dimensional model can be compared to a plurality of n-dimensional models that are tagged as including a target property associated with malicious behavior, benign behavior, and/or a vulnerability. Based on the comparison, a likelihood can be determined that the data includes the target property.

Abstract: Techniques and architectures for representing data with one or more n-dimensional representations and/or using one or more models to identify malware are described herein. For example, the techniques and architectures may determine one or more coordinates for one or more points based on one or more sets of bits in the data and generate an n-dimensional representation for the data based on the one or more points. The techniques and architectures may evaluate the n-dimensional representation with one or more machine-trained models to detect malware.

Abstract: Techniques and architectures for representing data with one or more n-dimensional representations and/or using one or more models to identify threats associated with the one or more n-dimensional representations are described herein. For example, the techniques and architectures may determine one or more coordinates for one or more points based on one or more sets of bits in the data and generate an n-dimensional representation for the data based on the one or more points. The techniques and architectures may evaluate the n-dimensional representation with one or more machine-trained models to detect a threat associated with the data, such as malware or another threat.

Abstract: Techniques and architectures for representing data with one or more n-dimensional representations and/or using one or more models to identify threats associated with the one or more n-dimensional representations are described herein. For example, the techniques and architectures may determine one or more coordinates for one or more points based on one or more sets of bits in the data and generate an n-dimensional representation for the data based on the one or more points. The techniques and architectures may evaluate the n-dimensional representation with one or more machine-trained models to detect a threat associated with the data, such as malware or another threat.