Abstract: A method comprises receiving, at a network infrastructure device, a flow of packets, determining, using the network infrastructure device and for a first subset of the packets, that the first subset corresponds to a first datagram and determining a first length of the first datagram, determining, using the network infrastructure device and for a second subset of the packets, that the second subset corresponds to a second datagram that was received after the first datagram, and determining a second length of the second datagram, determining, using the network infrastructure device, a duration value between a first arrival time of the first datagram and a second arrival time of the second datagram, sending, to a collector device that is separate from the network infrastructure device, the first length, the second length, and the duration value for analysis.

Abstract: 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.

Abstract: In an embodiment, the method comprises receiving an identification of an anomaly associated with a false positive identification of a security threat by the intrusion detection system, wherein a first set of feature data identifies features of the anomaly; creating a plurality of training sets each comprising identifications of a plurality of samples of network communications; for the anomaly and each training set of the plurality of training sets, training a decision tree that is stored in digital memory of the security analysis computer; based at least in part on the plurality of trained decision trees, extracting a set of features that distinguish the anomaly from the plurality of samples; generating one or more rules associated with the anomaly from the extracted set of features and causing programming the security analysis computer with the one or more rules.

Abstract: Rapidly detecting network threats with targeted detectors includes, at a computing device having connectivity to a network, determining features of background network traffic. Features are also extracted from a particular type of network threat. A characteristic of the particular type of network threat that best differentiates the features of the particular type of network threat from the features of the background network traffic is determined. A targeted detector for the particular type of network threat is created based on the characteristic and an action is applied to particular incoming network traffic identified by the targeted detector as being associated with the particular type of network threat.

Abstract: In one embodiment, a device in a network maintains a plurality of machine learning-based detectors for an intrusion detection system. Each detector is associated with a different portion of a feature space of traffic characteristics assessed by the intrusion detection system. The device provides data regarding the plurality of detectors to a user interface. The device receives an adjustment instruction from the user interface based on the data provided to the user interface regarding the plurality of detectors. The device adjusts the portions of the feature space associated with the plurality of detectors based on the adjustment instruction received from the user interface.

Abstract: In one embodiment, a device in a network receives traffic data associated with a particular communication channel between two or more nodes in the network. The device generates a mean map by employing kernel embedding of distributions to the traffic data. The device forms a representation of the communication channel by identifying a set of lattice points that approximate the mean map. The device generates a traffic classifier using the representation of the communication channel. The device uses machine learning to jointly identify the set of lattice points and one or more parameters of the traffic classifier. The device causes the traffic classifier to analyze network traffic sent via the communication channel.

Abstract: In one embodiment, a device in a network identifies an set of services of a domain accessed by a plurality of users in the network. The device generates a service usage model for the domain based on the set of services accessed by the plurality of users. The service usage model models usage of the services of the domain by the plurality of users. The device trains a machine learning-based classifier to analyze traffic in the network using a set of training feature vectors. A particular training feature vector includes data indicative of service usage by one of the users for the domain and the modeled usage of the services of the domain by the plurality of users. The device causes classification of traffic in the network associated with a particular user by the trained machine learning-based classifier.

Abstract: Identifying malicious executables by analyzing proxy logs includes, at a server having connectivity to the Internet, retrieving sets of proxy logs from a plurality of proxy servers. Each proxy server of the plurality of proxy servers is associated with a network and generates network traffic logs for one or more nodes included in the network. Then, a set of executables hosted by each of the one or more nodes associated with each of the plurality of proxy servers is determined. Each set of executables is analyzed to detect a specific executable and portions of each of the network traffic logs that are associated with the specific executable are identified. An alert is generated indicating the portions of each of the network traffic logs as likely to be associated with the specific executable.

Abstract: In an embodiment, the method comprises receiving network performance data for a computer network; receiving, from an intrusion detection system, network anomaly data indicating a plurality of anomalies that have occurred in the computer network; based, at least in part, on the network performance data and the network anomaly data, generating feature data; for each anomaly of the plurality of anomalies, using the feature data to determine a minimal set of features that distinguishes the anomaly from non-anomalies in the plurality of anomalies, and creating a mapping of the anomaly to the minimal set of features; based at least in part on the mapping, generating explanation rules for the plurality of anomalies; for a particular anomaly, identifying a particular rule of the explanation rules that is associated with the particular anomaly, and generating explanation data for the particular anomaly based upon the particular rule.

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: Presented herein are techniques for classifying devices as being infected with malware based on learned indicators of compromise. A method includes receiving at a security analysis device, traffic flows from a plurality of entities destined for a plurality of users, aggregating the traffic flows into discrete bags of traffic, wherein the bags of traffic comprise a plurality of flows of traffic for a given user over a predetermined period of time, extracting features from the bags of traffic and aggregating the features into per-flow feature vectors, aggregating the per-flow feature vectors into per-destination domain aggregated vectors, combining the per-destination-domain aggregated vectors into a per-user aggregated vector, and classifying a computing device used by a given user as infected with malware when indicators of compromise detected in the bags of traffic indicate that the per-user aggregated vector for the given user includes suspicious features among the extracted features.

Abstract: Rapidly detecting network threats with targeted detectors includes, at a computing device having connectivity to a network, determining features of background network traffic. Features are also extracted from a particular type of network threat. A characteristic of the particular type of network threat that best differentiates the features of the particular type of network threat from the features of the background network traffic is determined. A targeted detector for the particular type of network threat is created based on the characteristic and an action is applied to particular incoming network traffic identified by the targeted detector as being associated with the particular type of network threat.

Abstract: In one embodiment, a device in a network receives traffic data associated with a particular communication channel between two or more nodes in the network. The device generates a mean map by employing kernel embedding of distributions to the traffic data. The device forms a representation of the communication channel by identifying a set of lattice points that approximate the mean map. The device generates a traffic classifier using the representation of the communication channel. The device uses machine learning to jointly identify the set of lattice points and one or more parameters of the traffic classifier. The device causes the traffic classifier to analyze network traffic sent via the communication channel.

Abstract: 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.

Abstract: Actual traffic logs of network traffic to and from host devices in a network are collected over time. Artificial traffic logs for each of multiple artificial network address translation (NAT) devices are generated from the actual traffic logs. The actual traffic logs and the artificial traffic logs are labeled as being indicative of non-NAT devices and NAT devices, respectively, to produce labeled traffic logs. From the labeled traffic logs for each artificial NAT device and each non-NAT device, respective, correspondingly labeled, network traffic features indicative of whether the device behaves like a NAT device or a non-NAT device are extracted. A classifier device is trained using the network traffic features extracted for each artificial NAT device and each non-NAT device to classify between an actual NAT device and an actual non-NAT device based on further actual traffic logs.

Abstract: Identifying malicious executables by analyzing proxy logs includes, at a server having connectivity to the Internet, retrieving sets of proxy logs from a plurality of proxy servers. Each proxy server of the plurality of proxy servers is associated with a network and generates network traffic logs for one or more nodes included in the network. Then, a set of executables hosted by each of the one or more nodes associated with each of the plurality of proxy servers is determined. Each set of executables is analyzed to detect a specific executable and portions of each of the network traffic logs that are associated with the specific executable are identified. An alert is generated indicating the portions of each of the network traffic logs as likely to be associated with the specific executable.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.