Abstract: Detecting anomalous behavior using a browser extension, including: gathering first information describing activity associated with a user and generated by a browser extension on a user device; gathering second information describing activity associated with the user and generated by an application executed on the user device; and determining, based on the first information and the second information, whether the user has deviated from normal activity.

Abstract: An example method includes detecting, using sensors, packets throughout a datacenter. The sensors can then send packet logs to various collectors which can then identify and summarize data flows in the datacenter. The collectors can then send flow logs to an analytics module which can identify the status of the datacenter and detect an attack.

Abstract: Generating user-specific polygraphs for network activity, including: gathering information describing network activity associated with a user and generating, based on the information, a user-specific polygraph that includes one or more destinations associated with the network activity.

Abstract: Detecting deviations from typical user behavior, including: identifying a geographic location of a device that is associated with a user; determining device activity associated with the user; and detecting, based on a profile associated with the user, that the device activity associated with the user deviates from normal activity for the user.

Abstract: An example method can include choosing a pattern or patterns of network traffic. This pattern can be representative of a certain type of traffic such as an attack. The pattern can be associated with various components of a network and can describe expected behavior of these various components. A system performing this method can then choose a nodes or nodes to generate traffic according to the pattern and send an instruction accordingly. After this synthetic traffic is generated, the system can compare the behavior of the components with the expected behavior. An alert can then be created to notify an administrator or otherwise remedy any problems.

Abstract: Systems, methods, and computer-readable media are provided for determining a packet's round trip time (RTT) in a network. A system can receive information of a packet sent by a component of the network and further determine an expected acknowledgement (ACK) sequence number associated with the packet based upon received information of the packet. The system can receive information of a subsequent packet received by the component and determine an ACK sequence number and a receiving time of the subsequent packet. In response to determining that the ACK sequence number of the subsequent TCP packet matches the expected ACK sequence number, the system can determine a round trip time (RTT) of the packet based upon the received information of the packet and the received information of the subsequent packet.

Abstract: Detecting anomalous behavior of a device, including: generating, using information describing historical activity associated with a user device, a trained model for detecting normal activity for the user device; gathering information describing current activity associated with the user device; and determining, by using the information describing current activity associated with the user device as input to the trained model, whether the user device has deviated from normal activity.

Abstract: Detecting anomalous behavior using a browser extension, including: gathering first information describing activity associated with a user and generated by a browser extension on a user device; gathering second information describing activity associated with the user and generated by an application executed on the user device; and determining, based on the first information and the second information, whether the user has deviated from normal activity.

Abstract: Methods, systems, and products for offline workflows in an edge-based data platform, including: accessing log data describing activity associated with a user; generating, based on the log data, one or more alerts; and initiating, based on the one or more alerts, a workflow to acknowledge the one or more alerts by the user.

Abstract: Distinguishing user-initiated activity from application-initiated activity, including: gathering first information generated by a browser extension of a browser executed on a user device, wherein the first information describes activity associated with the browser; gathering second information generated by a client application executed on the user device, wherein the second information describes activity associated with the user device; and determining whether a user has deviated from normal activity by determining, based on the first information, whether at least a portion of the activity described in the second information comprises user-initiated activity or browser-initiated activity.

Abstract: Systems and methods are provided for automatically discovering applications/clusters in a network and mapping dependencies between the applications/clusters. A network monitoring system can capture network flow data using sensors executing on physical and/or virtual servers of the network and sensors executing on networking devices connected to the servers. The system can determine a graph including nodes, representing at least the servers, and edges, between pairs of the nodes of the graph indicating the network flow data includes one or more observed flows between pairs of the servers represented by the pairs of the nodes. The system can determine a dependency map, including representations of clusters of the servers and representations of dependencies between the clusters, based on the graph. The system can display a first representation of a first cluster of the dependency map and information indicating a confidence level of identifying the first cluster.

Abstract: An example method includes detecting, using sensors, packets throughout a datacenter. The sensors can then send packet logs to various collectors which can then identify and summarize data flows in the datacenter. The collectors can then send flow logs to an analytics module which can identify the status of the datacenter and detect an attack.

Abstract: Systems, methods, and computer-readable media are provided for determining a packet's round trip time (RTT) in a network. A system can receive information of a packet sent by a component of the network and further determine an expected acknowledgement (ACK) sequence number associated with the packet based upon received information of the packet. The system can receive information of a subsequent packet received by the component and determine an ACK sequence number and a receiving time of the subsequent packet. In response to determining that the ACK sequence number of the subsequent TCP packet matches the expected ACK sequence number, the system can determine a round trip time (RTT) of the packet based upon the received information of the packet and the received information of the subsequent packet.

Abstract: Detecting anomalous behavior of a device, including: generating, using information describing historical activity associated with a user device, a trained model for detecting normal activity for the user device; gathering information describing current activity associated with the user device; and determining, by using the information describing current activity associated with the user device as input to the trained model, whether the user device has deviated from normal activity.

Abstract: Generating user-specific polygraphs for network activity, including: gathering information describing network activity associated with a user and generating, based on the information, a user-specific polygraph that includes one or more destinations associated with the network activity.

Abstract: An example method includes detecting, using sensors, packets throughout a datacenter. The sensors can then send packet logs to various collectors which can then identify and summarize data flows in the datacenter. The collectors can then send flow logs to an analytics module which can identify the status of the datacenter and detect an attack.

Abstract: Systems, methods, and computer-readable media are provided for determining a packet's round trip time (RTT) in a network. A system can receive information of a packet sent by a component of the network and further determine an expected acknowledgement (ACK) sequence number associated with the packet based upon received information of the packet. The system can receive information of a subsequent packet received by the component and determine an ACK sequence number and a receiving time of the subsequent packet. In response to determining that the ACK sequence number of the subsequent TCP packet matches the expected ACK sequence number, the system can determine a round trip time (RTT) of the packet based upon the received information of the packet and the received information of the subsequent packet.

Abstract: This disclosure generally relates to a method and system for generating a communication graph of a network using an application dependency mapping (ADM) pipeline. In one aspect of the disclosure, the method comprises receiving network data (e.g., flow data and process information at each node) from a plurality of sensors associated with a plurality of nodes of the network, determining a plurality of vectors and an initial graph of the plurality of nodes based upon the network data, determining similarities between the plurality of vectors, clustering the plurality of vectors into a plurality of clustered vectors based upon the similarities between the plurality of vectors, and generating a communication graph of the network system based upon the plurality of clustered vectors.

Abstract: Detecting anomalous behavior of a device, including: generating, using information describing historical activity associated with a user device, a trained model for detecting normal activity for the user device; gathering information describing current activity associated with the user device; and determining, by using the information describing current activity associated with the user device as input to the trained model, whether the user device has deviated from normal activity.

Abstract: Detecting deviations from typical user behavior, including: identifying a geographic location of a device that is associated with a user; determining device activity associated with the user; and detecting, based on a profile associated with the user, that the device activity associated with the user deviates from normal activity for the user.