Abstract: Some embodiments improve the security of service principals, service accounts, and other application identity accounts by detecting compromise of account credentials. Application identity accounts provide computational services with access to resources, as opposed to human identity accounts which operate on behalf of a particular person. Authentication attempt access data is submitted to a machine learning model which is trained specifically to detect application identity account anomalies. Heuristic rules are applied to the anomaly detection result to reduce false positives, yielding a compromise assessment suitable for access control mechanism usage. Embodiments reflect differences between application identity accounts and human identity accounts, in order to avoid inadvertent service interruptions, improve compromise detection for application identity accounts, and facilitate compromise containment and recovery efforts by focusing on credentials individually.

Abstract: One or more techniques and/or systems are provided for risk assessment. Historical authentication data and/or compromised user account data may be evaluated to identify a set of authentication context properties associated with user authentication sessions and/or a set of malicious account context properties associated with compromised user accounts (e.g., properties indicative of whether a user recently visited a malicious site, created a fake social network profile, logged in from unknown locations, etc.). The set of authentication context properties and/or the set of malicious account context properties may be annotated to create an annotated context property training set that may be used to train a risk assessment machine learning model to generate a risk assessment model. The risk assessment model may be used to evaluate user context properties of a user account event to generate a risk analysis metric indicative of a likelihood the user account event is malicious or safe.

Abstract: One or more techniques and/or systems are provided for risk assessment. Historical authentication data and/or compromised user account data may be evaluated to identify a set of authentication context properties associated with user authentication sessions and/or a set of malicious account context properties associated with compromised user accounts (e.g., properties indicative of whether a user recently visited a malicious site, created a fake social network profile, logged in from unknown locations, etc.). The set of authentication context properties and/or the set of malicious account context properties may be annotated to create an annotated context property training set that may be used to train a risk assessment machine learning model to generate a risk assessment model. The risk assessment model may be used to evaluate user context properties of a user account event to generate a risk analysis metric indicative of a likelihood the user account event is malicious or safe.

Abstract: One or more techniques and/or systems are provided for risk assessment. Historical authentication data and/or compromised user account data may be evaluated to identify a set of authentication context properties associated with user authentication sessions and/or a set of malicious account context properties associated with compromised user accounts (e.g., properties indicative of whether a user recently visited a malicious site, created a fake social network profile, logged in from unknown locations, etc.). The set of authentication context properties and/or the set of malicious account context properties may be annotated to create an annotated context property training set that may be used to train a risk assessment machine learning model to generate a risk assessment model. The risk assessment model may be used to evaluate user context properties of a user account event to generate a risk analysis metric indicative of a likelihood the user account event is malicious or safe.

Abstract: One or more techniques and/or systems are provided for risk assessment. Historical authentication data and/or compromised user account data may be evaluated to identify a set of authentication context properties associated with user authentication sessions and/or a set of malicious account context properties associated with compromised user accounts (e.g., properties indicative of whether a user recently visited a malicious site, created a fake social network profile, logged in from unknown locations, etc.). The set of authentication context properties and/or the set of malicious account context properties may be annotated to create an annotated context property training set that may be used to train a risk assessment machine learning model to generate a risk assessment model. The risk assessment model may be used to evaluate user context properties of a user account event to generate a risk analysis metric indicative of a likelihood the user account event is malicious or safe.