Cisco Technology, Inc.

Jan Jusko

Tomas Pevny

Martin Rehak

Martin Grill

Blake Harrell Anderson

Vincent E Parla

Martin Vejman

David A McGrew

Jan Kohout

Danila Khikhlukha

Harshit Nayyar

Michal Sofka

Ivan Nikolaev

David Mcgrew

Jan Stiborek

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.

Correlating endpoint and network views to identify evasive applications

In one embodiment, a device obtains execution records regarding executions of a plurality of binaries. The execution records comprise command line arguments used during the execution. The device determines measures of similarity between the executions of the binaries based on their command line arguments. The device clusters the executions into clusters based on the determined measures of similarity. The device flags the command line arguments for a particular one of the clusters as an indicator of compromise for malware, based on at least one of the binaries associated with the particular cluster being malware.

Automated creation of lightweight behavioral indicators of compromise (IOCS)

In one embodiment, a service receives a plurality of process hashes for processes executed by a plurality of devices. The service receives traffic data indicative of traffic between the plurality of devices and a plurality of remote server domains. The service forms a bipartite graph based on the processes hashes and the traffic data. A node of the graph represents a particular process hash or server domain and an edge between nodes in the graph represents network traffic between a process and a server domain. The service identifies, based on the bipartite graph, a subset of the plurality of processes as exhibiting polymorphic malware behavior. The service causes performance of a mitigation action in the network based on the identified subset of processes identified as exhibiting polymorphic malware behavior.

Structural command and control detection of polymorphic malware

In one embodiment, a device analyzes network traffic data using a clustering process, to identify a cluster of addresses associated with the network traffic data for which the associated network traffic has similar behavioral characteristics. The device calculates a set of rankings for the cluster by comparing the cluster to different sets of malicious addresses. The device aggregates the set of rankings into a final ranking by setting the rankings in the set as current rankings and iteratively calculating an average of any subset of the current rankings that comprises correlated rankings. The calculated average replaces the rankings in the subset as a current ranking. When none of the current rankings are correlated, the device performs an aggregation across all of the current rankings to form the final ranking. The device provides data indicative of the cluster for review by a supervisor, based on the final ranking.

Active prioritization of investigation targets in network security

In one embodiment, a method includes obtaining a set of samples, each of the set of samples including sample values for each of a plurality of variables in a variable space. The method includes receiving, for each of an initial subset of the set of samples, a label for the sample as being either malicious or legitimate; identifying one or more boundaries in the variable space based on the labels and sample values for each of the initial subset; selecting an incremental subset of the unlabeled samples of the set of samples, wherein the incremental subset includes at least one unlabeled sample including sample values further from any of the one or more boundaries than an unlabeled sample that is not included in the incremental subset; and receiving, for each of the incremental subset, a label for the sample as being either malicious or legitimate.

Network security classification

Detecting network services based on network flow data is disclosed. Using a networking device, network flow data is obtained for a plurality of endpoints of a telecommunications network. Each endpoint of the plurality of endpoints is uniquely described by data comprising an IP address, a port, and a communication protocol. For each endpoint of a set of at least one endpoint selected from the plurality of endpoints, a plurality of peers of the endpoint is determined by detecting communication between the endpoint and the plurality of peers based on the network flow data. For each peer of a set of peers selected from the plurality of peers, a difference between a number of peers of the endpoint and a number of peers of said each peer is determined based on the network flow data. It is determined if the endpoint is a service based on the difference determined for each peer of the set of peers. Network management is performed based on the determination of whether the endpoint is a service.

Detecting network services based on network flow data

In one embodiment, a method includes receiving client-server connection data for clients and servers, the data including IP addresses corresponding to the servers, for each one of a plurality of IP address pairs performing a statistical test to determine whether the IP addresses in the one IP address pair are related by common clients based on the number of the clients connecting to each of the IP addresses in the one IP address pair, generating a graph including a plurality of vertices and edges, each of the vertices corresponding to a different IP address, each edge corresponding to a different IP address pair determined to be related by common clients in the statistical test, and clustering the vertices yielding clusters, a subset of the IP addresses in one of the clusters providing an indication of the IP addresses of the servers serving a same application.

Server grouping system

In one embodiment a method, system and apparatus is described for detecting a malicious network connection, the method system and apparatus including determining, for each connection over a network, if each connection is a persistent connection, if, as a result of the determining, a first connection is determined to be a persistent connection, collecting connection statistics for the first connection, creating a feature vector for the first connection based on the collected statistics, performing outlier detection for all of the feature vector for all connections over a network which have been determined to be persistent connections, and reporting detected outliers. Related methods, systems and apparatus are also described.

Growing community of inventors

Prague, Czechia

Jan Jusko