Cisco Technology, Inc.

Jan Kohout

Martin Kopp

Martin Grill

Tomas Pevny

David A McGrew

Blake Harrell Anderson

Lukas Bajer

Martin Rehak

Jan Brabec

Cenek Skarda

Petr Somol

Jan Jusko

Danila Khikhlukha

Marc René Jacques Marie Dupont

Martin Vejman

Kyrylo Shcherbin

David Mcgrew

Jaroslav Hlavac

Tornas Pevny

Jan Mrkos

In one embodiment, a device obtains input features for a neural network-based model. The device pre-defines a set of neurons of the model to represent known behaviors associated with the input features. The device constrains weights for a plurality of outputs of the model. The device trains the neural network-based model using the constrained weights for the plurality of outputs of the model and by excluding the pre-defined set of neurons from updates during the training.

Learning of malicious behavior vocabulary and threat detection through behavior matching

Techniques for combining threat-related events associated with different modalities to provide a complete insight into cyber attack life cycles. The techniques may include receiving telemetry data associated with one or more modalities and detecting, based at least in part on the telemetry data, one or more abnormal events associated with security incidents. The one or more abnormal events may include at least a first abnormal event associated with a first modality and a second abnormal event associated with a second modality. The techniques may also include determining that an entity associated with the abnormal events is a same entity and, based at least in part on the entity comprising the same entity, determining that a correlation between the abnormal events is indicative of a security incident. Based at least in part on the correlation, an indication associated with the security incident may be output.

Comprehensible threat detection

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.

Analyzing encrypted traffic behavior using contextual traffic data

In one embodiment, a traffic analysis service obtains telemetry data regarding encrypted traffic associated with a particular device in the network, wherein the telemetry data comprises Transport Layer Security (TLS) features of the traffic. The service determines, based on the TLS features from the obtained telemetry data, a set of one or more TLS fingerprints for the traffic associated with the particular device. The service calculates a measure of similarity between the set of one or more TLS fingerprints for the traffic associated with the particular device and a set of one or more TLS fingerprints of traffic associated with a second device. The service determines, based on the measure of similarity, that the particular device and the second device were operated by the same user.

Device detection in network telemetry with TLS fingerprinting

In one embodiment, a device in a network obtains log data regarding replication of files stored on an endpoint client to a file replication service. The device tracks, based on the obtained logs, encryption changes to the files that convert the files from unencrypted files to encrypted files. The device determines that the tracked encryption changes to the files are indicative of a ransomware infection on the endpoint client. The device initiates a mitigation action regarding the ransomware infection.

Ransomware detection using file replication logs

In one embodiment, a device in a network receives traffic information regarding one or more secure sessions in the network. The device associates the one or more secure sessions with corresponding certificate validation check traffic indicated by the received traffic information. The device makes a self-signed certificate determination for an endpoint domain of a particular secure session based on whether the particular secure session is associated with certificate validation check traffic. The device causes the self-signed certificate determination for the endpoint domain to be used as input to a malware detector.

Identifying self-signed certificates using HTTP access logs for malware detection

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.

Leveraging endpoint and network environment inferences for malware traffic classification

A computing device having connectivity to a network stores one or more existing device models, where each of the one or more existing device models is a representation of a different client device used by a first authenticated user to access the network. The computing device obtains a device sample, which comprises network traffic data that is captured during a period of time and which is generated by a particular client device associated with the authenticated user of the network. The computing device determines, based on one or more relational criteria, whether the device sample should be assigned to one of the one or more existing device models or to an additional device model that has not yet been created. The computing device then determines relative identity of the particular client device based on whether the device sample is assigned to one of the one or more device models or to an additional device model that has not yet been created.

Client device tracking

In one embodiment, a traffic analysis service obtains telemetry data regarding network traffic associated with a device in a network. The traffic analysis service forms a histogram of frequencies of the traffic features from the telemetry data for the device. The traffic features are indicative of endpoints with which the device communicated. The traffic analysis service associates a device type with the device, by comparing the histogram of the traffic features from the telemetry data to histograms of traffic features associated with other devices. The traffic analysis service initiates, based on the device type associated with the device, an adjustment to treatment of the traffic associated with the device by the network.

Classification of IoT devices based on their network traffic

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.

Illegitimate typosquatting detection with internet protocol information

In one embodiment, a device in a network determines a set of lattice points in a multi-dimensional space constructed using message characteristics of messages exchanged between endpoint nodes in the network. The device uses the lattice points to derive vector representations of communication channels in the network with each of the communication channels being associated with one or more of the exchanged messages. A vector representation of an application in the network is based on one or more of the derived vector representations of one or more channels used to exchange messages associated with the application. The device identifies the application as associated with a first one of the channels by determining a measure of similarity between the first channel and the vector representation of the application that approximates a maximum mean discrepancy (MMD) distance between the message characteristics for the vector representations of the first channel and the application.

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Roudnice Nad Labem, Czechia

Jan Kohout