Abstract: Embodiments are directed monitoring network traffic using network monitoring computers. Metrics may be determined based on monitoring network traffic associated with entities in the network such that the metrics may be included in profiles associated each entity. The profiles may be compared with other profiles in a context database based on the metrics included in each profile and each other profile. In response to the profiles being unmatched by other profiles one or more active probes may be performed to collect other metrics that may be used to update profiles. In response to the one or more profiles being matched by the other profiles in the context database, a timestamp associated with the other profiles may be updated to a current time value. Reports that include information associated with the entities and the profiles or the updated profiles may be generated.

Abstract: Embodiments are directed monitoring network traffic using network monitoring computers. Activity associated with a document in a network may be determined based on the network traffic. A profile may be generated based on a summarization of the activity associated with the document such that the profile may be stored in a data store that stores other profiles. Similar profiles may be determined based on a classification of each profile in the data store based on similarities between the profile and the other profiles in the data store. In response to determining similar profiles, locations in the network associated with documents that correspond to the similar profiles may be determined. Locations may be classified based on the activity, the similar profiles and access policies. In response to portions of the locations being classified as inconsistent with the access policies may be reported.

Abstract: Embodiments are directed to monitoring network traffic using network monitoring computers (NMCs). NMCs may determine requests provided to a server based on a first portion of network traffic. NMCs may determine suspicious requests based on characteristics of the provided requests. NMCs may employ the characteristics of the suspicious requests to provide correlation information that is associated with the suspicious requests. NMCs may determine dependent actions associated with the server based on a second portion of the network traffic and the correlation information. And, in response to determining anomalous activity associated with the evaluation of the dependent actions, NMCs may provide reports associated with the anomalous activity.

Abstract: Embodiments are directed to monitoring communication between computers using network monitoring computers (NMCs). NMCs identify a secure communication session established between two of the computers based on an exchange of handshake information associated with the secure communication session. Key information that corresponds to the secure communication session may be obtained from a key provider such that the key information may be encrypted by the key provider. NMCs may decrypt the key information. NMCs may derive the session key based on the decrypted key information and the handshake information. NMCs may decrypt network packets included in the secure communication session. NMCs may be employed to inspect the one or more decrypted network packets to execute one or more rule-based policies.

Abstract: Embodiments are directed to monitoring network traffic using network monitoring computers (NMCs). Two or more network segments coupled by a traffic forwarding device (TFD) may be monitored. External network addresses and internal network addresses may be determined based on encrypted network traffic exchanged between external endpoints and the TFD and internal network traffic exchanged between internal endpoints and the TFD. Metrics associated with the external network addresses or the internal network addresses may be determined based on the monitoring. Correlation scores may be provided for the external network addresses and the internal network addresses based on of a correlation model, the metrics, or the other metrics. If a correlation score associated with an external network address and an internal network address exceeds a threshold value, the external network address and the internal network address may be associated with each other based on the correlation score.

Abstract: Embodiments are directed to monitoring network traffic using network monitoring computers (NMCs). Networks may be configured to protect servers using centralized security protocols. Centralized security protocols may depend on centralized control provided by authentication control servers. If a client intends to access protected servers it may communicate with the authentication control server to obtain keys that enable it to access the requested servers. NMCs may monitor network traffic the centralized security protocol to collect metrics associated with the control servers, clients, or resource servers.

Abstract: Embodiments are direct to monitoring communication between computers may be using network monitoring computers (NMCs). Network packets that are communicated between the computers may be captured and stored in a data store. If the NMCs identify a secure communication session established between two computers, the NMCs may obtain key information that corresponds to the secure communication session that includes a session key that may be provided by a key provider. Correlation information associated with the secure communication session may be captured by the NMCs. The correlation information may include tuple information associated with the secure communication session. And, the key information and the correlation information may be stored in a key escrow. The key information may be indexed in the key escrow using the correlation information.

Abstract: Embodiments are directed to monitoring network traffic using a monitoring engine that monitors network traffic in networks to provide metrics. An inference engine may provide activity profiles based on portions of the network traffic where each activity profile includes features associated with the portions of network traffic. The inference engine may determine other activity profiles correlated with the activity profiles based on correlation models such that the determination of the other activity profiles occurs prior to monitoring an occurrence of other portions of the network traffic. The inference engine may modify monitoring actions of the monitoring engine based on the other activity profiles. The inference engine may provide reports based on the portions of the network traffic, the activity profiles, the other portions of the network traffic, or the other activity profiles.

Abstract: Embodiments are directed to monitoring network traffic to determine users and assets based on the network traffic. A user role model may assign a user role and provide a role confidence score for the users based on network traffic associated with the users. An asset model may assign an asset type and provide an asset confidence score the assets based on network traffic associated with the assets. The users may be associated with assets based on the network traffic. The role confidence scores provided for the users may be modified based on the asset type assigned to assets associated with the users. The asset confidence score provided for the assets may be modified based on the user role assigned to the users associated with the assets. A report that includes information about the user roles and the asset types may be provided.

Abstract: Embodiments are directed to monitoring network traffic. A monitoring engine may monitor network traffic associated with a plurality of entities in networks to provide metrics. And provide a device relation model based on the plurality of entities, the network traffic, and the metrics. An inference engine may associate each entity in the plurality of entities with an importance score based on the device relation model and the metrics such that each importance score is associated with a significance of an entity to operations of the networks. An alert engine may generate a plurality of alerts associated with the plurality of entities based on the metrics. And provide one or more alerts from the plurality of alerts to one or more users based on one or more ranked importance scores associated with one or more entities.

Abstract: Embodiments are directed to monitoring network traffic using network monitoring computers (NMCs). Networks may be configured to protect servers using centralized security protocols. Centralized security protocols may depend on centralized control provided by authentication control servers. If a client intends to access protected servers it may communicate with the authentication control server to obtain keys that enable it to access the requested servers. NMCs may monitor network traffic the centralized security protocol to collect metrics associated with the control servers, clients, or resource servers.

Abstract: Embodiments are directed to monitoring network traffic using network monitoring computers (NMCs). Two or more network segments coupled by bridge devices may be monitored by NMCs. The bridge devices may modify network traffic passed from one network segment to another network segment. Flows in network segments may be determined based on monitored network traffic associated with the network segments. Other flows in other network segments may be determined based on other monitored network traffic associated with the other network segments. A correlation score for two or more flows in different network segments may be provided based on a correlation model. Two or more related flows may be determined based on a value of the correlation score of the two or more related flows located in different network segments. A report that includes information about the two or more related flows may be provided.

Abstract: Embodiments are directed to monitoring network traffic over a network using one or more network monitoring computers. A monitoring engine may be instantiated to perform actions, including: monitoring network traffic to identify client requests provided by clients and server responses provided by servers in response to the client requests; determining request metrics associated with the client requests; and determining response metrics associated with the server responses. An analysis engine may be instantiated that performs actions, including: comparing the request metrics with the response metrics; determining atypical behavior associated with the clients based on the comparison such that the atypical behavior includes an absence of adaption by the clients to changes in the server responses; and providing alerts that may identify the clients be associated with the atypical behavior.

Abstract: Embodiments are directed to monitoring network traffic using network monitoring computers (NMCs). Two or more network segments coupled by a traffic forwarding device (TFD) may be monitored. External network addresses and internal network addresses may be determined based on encrypted network traffic exchanged between external endpoints and the TFD and internal network traffic exchanged between internal endpoints and the TFD. Metrics associated with the external network addresses or the internal network addresses may be determined based on the monitoring. Correlation scores may be provided for the external network addresses and the internal network addresses based on of a correlation model, the metrics, or the other metrics. If a correlation score associated with an external network address and an internal network address exceeds a threshold value, the external network address and the internal network address may be associated with each other based on the correlation score.

Abstract: Embodiments are directed to monitoring network traffic using network monitoring computers (NMCs). Metrics may be determined based on monitoring network traffic associated with a plurality of entities each associated with a profile that includes the metrics for each entity. Beaconing metrics associated with beaconing activity may be determined based on the metrics. The profile of each entity may be compared with the beaconing metrics to determine the entities that may be engaged in beaconing activity. The entities may be characterized based on beaconing activity such that the beaconing activity includes communication with endpoints associated with the third parties, employing communication protocols associated with the third-parties, or exchanging payloads consistent with the beaconing activity. Reports that include information associated with the entities and its beaconing activity may be generated.

Abstract: Embodiments are directed to monitoring network traffic associated with networks to provide metrics. A monitoring engine may determine an anomaly based on the metrics exceeding threshold values. An inference engine may be instantiated to provide an anomaly profile based on portions of the network traffic that are associated with the anomaly. The inference engine may provide an investigation profile based on the anomaly profile such that the investigation profile includes information associated with investigation activities associated with an investigation of the anomaly. The inference engine may monitor the investigation of the anomaly based on other portions of the network traffic such that the other portions of the network traffic are associated with monitoring an occurrence of the investigation activities. The inference engine may modify a performance score associated with the investigation profile based on the occurrence of the investigation activities and a completion status of the investigation.

Abstract: Embodiments are directed to monitoring network traffic using network monitoring computers (NMCs). Networks may be configured to protect servers using centralized security protocols. Centralized security protocols may depend on centralized control provided by authentication control servers. If a client intends to access protected servers it may communicate with the authentication control server to obtain keys that enable it to access the requested servers. NMCs may monitor network traffic the centralized security protocol to collect metrics associated with the control servers, clients, or resource servers.

Abstract: Embodiments are directed monitoring network traffic using network monitoring computers. Metrics may be determined based on monitoring network traffic associated with entities in the network such that the metrics may be included in profiles associated each entity. The profiles may be compared with other profiles in a context database based on the metrics included in each profile and each other profile. In response to the profiles being unmatched by other profiles one or more active probes may be performed to collect other metrics that may be used to update profiles. In response to the one or more profiles being matched by the other profiles in the context database, a timestamp associated with the other profiles may be updated to a current time value. Reports that include information associated with the entities and the profiles or the updated profiles may be generated.

Abstract: Embodiments are directed to monitoring network traffic using NMCs that may be arranged to provide scores based on threat assessments associated with anomaly classes such that the anomaly classes may be associated with types of anomalous activity. NMCs may employ the anomaly classes, the scores, characteristics of the anomaly classes, or the like, to determine triage models. The NMCs may modify the scores based on the triage models or archival information associated with the anomaly classes. The NMCs may associate the modified scores with the anomaly classes. In response to detecting anomalous activity, the NMCs may provide other scores based on the anomalous activity and provide a report that includes the other scores to a user.

Abstract: Embodiments are directed to monitoring communication between computers using network monitoring computers (NMCs). NMCs identify a secure communication session established between two of the computers based on an exchange of handshake information associated with the secure communication session. Key information that corresponds to the secure communication session may be obtained from a key provider such that the key information may be encrypted by the key provider. NMCs may decrypt the key information. NMCs may derive the session key based on the decrypted key information and the handshake information. NMCs may decrypt network packets included in the secure communication session. NMCs may be employed to inspect the one or more decrypted network packets to execute one or more rule-based policies.