Abstract:
In one embodiment, a device in a network detects an encrypted traffic flow associated with a client in the network. The device captures contextual traffic data regarding the encrypted traffic flow from one or more unencrypted packets associated with the client. The device performs a classification of the encrypted traffic flow by using the contextual traffic data as input to a machine learning-based classifier. The device generates an alert based on the classification of the encrypted traffic flow.
Abstract:
In one embodiment, a service receives traffic telemetry data regarding encrypted traffic sent by an endpoint device in a network. The service analyzes the traffic telemetry data to infer characteristics of an application on the endpoint device that generated the encrypted traffic. The service receives, from a monitoring agent on the endpoint device, application telemetry data regarding the application. The service determines that the application is evasive malware based on the characteristics of the application inferred from the traffic telemetry data and on the application telemetry data received from the monitoring agent on the endpoint device. The service initiates performance of a mitigation action in the network, after determining that the application on the endpoint device is evasive malware.
Abstract:
In one embodiment, a security device in a computer network detects potential domain generation algorithm (DGA) searching activity using a domain name service (DNS) model to detect abnormally high DNS requests made by a host attempting to locate a command and control (C&C) server in the computer network. The server device also detects potential DGA communications activity based on applying a hostname-based classifier for DGA domains associated with any server internet protocol (IP) address in a data stream from the host. The security device may then correlate the potential DGA searching activity with the potential DGA communications activity, and identifies DGA performing malware based on the correlating, accordingly.
Abstract:
In one embodiment, a device obtains simulation environment data regarding traffic generated within a simulation environment in which malware is executed. The device trains a malware detector using the simulation environment data. The device obtains deployment environment characteristics of a network to which the malware detector is to be deployed. The device configures the malware detector to ignore data in the simulation environment data that is associated with one or more environment characteristics that are not present in the deployment environment characteristics.
Abstract:
In one embodiment, a device in a network detects an encrypted traffic flow associated with a client in the network. The device captures contextual traffic data regarding the encrypted traffic flow from one or more unencrypted packets associated with the client. The device performs a classification of the encrypted traffic flow by using the contextual traffic data as input to a machine learning-based classifier. The device generates an alert based on the classification of the encrypted traffic flow.
Abstract:
In one embodiment, a device obtains simulation environment data regarding traffic generated within a simulation environment in which malware is executed. The device trains a malware detector using the simulation environment data. The device obtains deployment environment characteristics of a network to which the malware detector is to be deployed. The device configures the malware detector to ignore data in the simulation environment data that is associated with one or more environment characteristics that are not present in the deployment environment characteristics.
Abstract:
Detecting illegitimate typosquatting with Internet Protocol (IP) information includes, at a computing device having connectivity to a network, obtaining a list of domains and filtering the list to generate a list of monitored domain strings. IP information is passively determined for domains associated with each of the monitored domain strings. A domain requested in network traffic for the network is identified as a candidate typosquatting domain and the candidate typosquatting domain is determined to be an illegitimate typosquatting domain based at least on the IP information. An action is initiated related to the illegitimate typosquatting domain.
Abstract:
Detecting DGA-based malware is disclosed. In an embodiment, a number of domain name server requests originating from a particular host among a plurality of hosts is determined. The number of domain name server requests are directed to one or more domain name servers. A number of internet protocol addresses contacted by the particular host is determined. Based on the number of domain name server requests and the number of internet protocol addresses contacted existence of malware on the particular host is determined.
Abstract:
A method of tracking users over network hosts based on behavior includes analyzing data representing behavior of active network hosts during two or more time windows at a computing apparatus having connectivity to a network. Based on the analyzing, a profile is generated for each network host active in the network during the two or more time windows. Similarity between the profiles for the two or more time windows are determined and, based on the similarity, it may be determined that an identity associated with one of the active network hosts during a time window of the two or more time windows has changed.
Abstract:
Network traffic logs of network traffic to and from host devices connected to a network that were collected over time are accessed. For each host device identified in the logs, a set of network traffic features indicative of whether the host device behaves like a Network Address Translation (NAT) device or an end host device is extracted from the logs for the host device. Each feature has values that vary over time based on the logs. A trained host device behavior classifier classifies the host device as either a NAT device or an end host device based on one or more of the feature values.