Microsoft Technology Licensing, LLC

Fady Nasereldeen

Andrey Karpovsky

Yotam Livny

Tomer Rotstein

Roy Levin

Naama Kraus

Tamer Salman

Michael Makhlevich

A computing system is configured to train a machine-learning model for detecting suspicious network activities based on a training dataset. The training of the machine-learning model may be supervised or unsupervised training. The training dataset includes multiple strings. For each of the multiple strings, the computing system extracts one or more N-grams substrings, where N is a natural number that is equal to or greater than 2. The computing system then determines a probability of each N-grams substring that may occur in a string. When the machine-learning model is executed, it is configured to classify whether a given string contained in network communication is a random string. In response to classifying that the given string is a random string, an alert is generated at a particular computing system to which the network communication is directed.

Random strings classification for detecting suspicious network activities

Unauthorized use of user credentials in a network is detected. Data indicative of text strings being used to access resources in the network is accessed. Regex models are determined for the text strings. Troupings of the regex models are determined based on an optimization of a cumulative weighted function. A regex model having a cumulative weighted function that exceeds a predetermined threshold is identified. An alert is generated when the cumulative weighted function for the identified regex model exceeds the predetermined threshold.

Detection of abnormal entities based on syntactic grouping of strings preliminary class

Unauthorized use of user credentials in a network is detected. Data indicative of text strings being used to access resources in the network is accessed. Regex models are determined for the text strings. Groupings of the regex models are determined based on an optimization of a cumulative weighted function. A regex model having a cumulative weighted function that exceeds a predetermined threshold is identified. An alert is generated when the cumulative weighted function for the identified regex model exceeds the predetermined threshold.

Detection of abnormal entities based on syntactic grouping of strings

Embodiments are provided for integrating feedback into alert managing processes having defined alert policies. These policies define conditions that, when satisfied by certain detected activities, triggers an alert to be sent to a client. A determination is made that a current detected activity does satisfy the condition(s). Subsequent to determining that the set of conditions is satisfied and prior to actually generating the alert, the current detected activity is determined to share a relationship with previously received feedback that caused the alert policy to be modified. After being modified, the alert policy specified whether the alert is to be sent to the client, modified and then sent, suspended, or disabled. The alert is then either generated or refrained from being generated based on the alert policy.

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Fady Nasereldeen