Alert Logic, Inc.

Mikhail Govshteyn

Christopher A Church

Eugene Golovinsky

Paul Fisher

Pavel S Trakhtman

Christopher D Baker

Christopher D Holm

An appliance is co-located on a network with computing devices. Log messages generated by the computing devices are collected by the appliance, filtered based on the content and stored in transmission priority queues based on the content. The appliance packetizes the log messages based on the transmission priority queue and the available bandwidth and compresses the packet. The appliance encrypts the packet, digitally signs the encrypted packet and sends the packet to a first data center over a public network. The first data center stores the packet in reliable storage and performs processing on the data. A copy of the packet is sent to a second data center that stores the copy and performs processing on the copied data. The appliance deletes the packet from its buffer after it has received acknowledgement that the second data center has received the packet.

Log message archiving and processing using a remote internet infrastructure

Systems and methods for correlating log messages into actionable incidents. Some embodiments implement a method which includes comparing a plurality of disparate log messages to a plurality of incident descriptions. The disparate log messages can be parsed. When the messages correlate with an incident description an incident case can be created. Workflow steps can be associated with the incident case and output along with the incident case. Additional disparate log messages can be compared to the incident expressions and, when additional messages correlate with the correlated incident description, the incident case can be adjusted. In some embodiments, the adjustment can include adding workflow steps to the incident case. Results of various workflow steps can be monitored and adjustments can be made accordingly. In some embodiments, the results can include out-of-bounds activities.

Systems and methods for correlating log messages into actionable security incidents and managing human responses

Embodiments of the invention provide a security expert system (SES) that automates intrusion detection analysis and threat discovery that can use fuzzy logic and forward-chaining inference engines to approximate human reasoning process. Embodiments of the SES can analyze incoming security events and generate a threat rating that indicates the likelihood of an event or a series of events being a threat. In one embodiment, the threat rating is determined based on an attacker rating, a target rating, a valid rating, and, optionally, a negative rating. In one embodiment, the threat rating may be affected by a validation flag. The SES can analyze the criticality of assets and calibrate/recalibrate the severity of an attack accordingly to allow for triage. The asset criticality can have a user-defined value. This ability allows the SES to protect and defend critical network resources in a discriminating and selective manner if necessary (e.g., many attacks).

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Houston, TX, United States of America

Mikhail Govshteyn