Abstract:
Systems and methods for distributed rule-based correlation of events are provided. A notification of a partial match of a distributed rule by an event of a first subset of events is received. The notification includes a set of properties of the event of the first subset of events. The distributed rule is evaluated using the set of properties of the event of the first subset of events and a set of properties of an event of a second subset of events. A complete match of the rule is determined based on the evaluation, and a correlation event is generated.
Abstract:
A drill down manager system may include an introspect module to determine fields for visual components, and a mappings module to map a drill down to a visual component based on the fields and data outputs for the drill down. The system may present the data outputs for the drill down in the visual component mapped to the drill down.
Abstract:
Systems and methods for evaluation of events are provided. A user-specific reference baseline comprising a set of temporally-ordered sequences of events. An event of a sequence of events in a current session is received. A determination is made as to whether the event at least partially matches the reference baseline using an attribute of the event and a temporal position of the event within the sequence of events in the current session.
Abstract:
A network asset information management system (101) may include an asset determination and event prioritization module (105) to generate real-time asset information based on network activity involving an asset (102). A rules module (109) may include a set of rules for monitoring the network activity involving the asset. An information analysis module (110) may evaluate the real-time asset information and the rules to generate a notification (111) related to the asset. The rules may include rules for determining vulnerabilities and risks associated with the asset based on comparison of a level of traffic identified to or from an IP address related to the asset to a predetermined threshold. The notification may include a level of risk associated with the asset.
Abstract:
A process includes analyzing events reported by computing devices on a network to recognize patterns of events that occurred on the network and sharing with a community, information concerning the patterns detected. The process may also use consolidated information on the patterns to select one or more of the patterns for analysis that identifies whether the selected patterns result from malicious activity. The consolidated information includes information on the patterns detected on the network and information concerning corresponding patterns of events that occurred elsewhere.
Abstract:
Fields are determined for pattern discovery in event data. Cardinality and repetitiveness statistics are determined for fields of event data. A set of the fields are selected based on the cardinality and repetitiveness for the fields. The fields may be included in a pattern discovery profile.
Abstract:
Systems and methods for merging partially aggregated query results are provided. A partially aggregated query result is determined. Each query of a plurality of queries is executed on a plurality of events at a defined schedule and a time duration. A key and a value of the partially aggregated query result are identified. It is determined whether a function for the partially aggregated query result is identified. If so, a related partially aggregated query result is determined using the key. The partially aggregated query result is merged with the related partially aggregated query result.
Abstract:
Security events associated with network devices and an actor category model are stored (501, 503). The actor category model includes levels arranged in a hierarchy and each level is associated with a subcategory for a category of the model. Security events are correlated with the actor category model (505), and a determination of whether a security threat exists is performed based on the correlating (506).
Abstract:
A session table includes one or more records, where each record represents a session. Session record information is stored in various fields, such as key fields, value fields, and timestamp fields. Session information is described as keys and values in order to support query/lookup operations. A session table is associated with a filter, which describes a set of keys that can be used for records in that table. A session table is populated using data contained in security information/events. Rules are created to identify events related to session information, extract the session information, and use the session information to modify a session table. A session table is partitioned so that the number of records in each session table partition is decreased. A session table is processed periodically so that active sessions are moved to the current partition.
Abstract:
Pattern discovery performed on event data may include selecting an initial set of parameters for the pattern discovery. The parameters may specify conditions for identifying a pattern in the event data. A pattern discovery run is executed on the event data based on the initial set of parameters, and a parameter may be adjusted based on the output of the pattern discovery run.