Abstract: A network traffic flow is directed to a computing services system is determined as being unrelated to the service that the system provides. In response, the network traffic flow is deflected away from the computing services system. Demand of the sender of the network traffic flow is managed by shaping the deflected network traffic flow in responding to the sender and/or by misinforming the sender in responding to the sender.

Abstract: Log entries and baseline log entries have timestamps, and can be structured over columns of respective data types. Temporal inconsistency can be identified by comparing a probability distribution of time differences between the timestamps of the log entries with a probability distribution of time differences between the timestamps of the baseline log entries. Data type inconsistency can be identified by comparing a data type of each column of the log entries with a data type of a corresponding column of the baseline log entries. Columnar inconsistency can be identified by comparing a number of the columns of the log entries with a number of the columns of the baseline log entries. In response to identification of temporal, data type, and/or columnar inconsistency, that an abnormality exists in collecting the log entries is detected.

Abstract: A service receives, from client computing devices of client networks, information regarding incoming network traffic addressed to dark Internet Protocol (IP) address spaces the of client networks. The service can predict a cyber attack based on the information received from the client computing devices of the client networks. The server computing device notifies the client computing device of each client network affected by the predicted cyber attack.

Abstract: Network communication events are filtered to remove the network communication events having a predicted unrelatedness to beaconing. Each network communication event has a timestamp, a source entity, and a destination entity. The filtered network communication events are aggregated by unique source entity-destination entity pairs. For each unique source entity-destination entity pair, the network communication events are timestamp-sorted, time differentials between the timestamps of adjacent network communication events are calculated, and a beacon likelihood metric is calculated from the calculated time differentials. Which of the unique source entity-destination entity pairs are indicative of beaconing are identified based on the beacon likelihood metric calculated for each unique source entity-destination entity pair.

Abstract: Techniques and apparatuses are described to enable a strategically coordinated fictitious ecosystem of disinformation for cyber threat intelligence collection in a computing network. The ecosystem comprises fictitious profiles and supporting fictitious infrastructure information to portray in-depth, apparent authenticity of the ecosystem. Malicious communications from an adversary directed at the ecosystem are monitored, and threat intelligence about the adversary is collected to prevent future attacks.

Abstract: In some examples, a system constructs, based on event data representing a plurality of events in a system, a representation of the plurality of events, the representation including information relating the events, and computes issue indications corresponding to potential issues in the system. The system adds information based on the issue indications to the representation to form an enriched representation, and searches the enriched representation to find a chain of events representing an issue in the system.

Abstract: In some examples, a Domain Name System (DNS) server receives, over a network, DNS queries containing domain names, extracts a common domain name shared by the domain names, determines whether a measure of an amount of data relating to the DNS queries containing the common domain name exceeds a threshold, and in response to determining that the measure of the amount of data relating to the DNS queries containing the common domain name exceeds the threshold, trigger a countermeasure action to address a threat associated with the DNS queries.

Abstract: For each of a number of naming deviation types, the number of deviations within a domain name of a domain is determined. Each naming deviation type is a different type of deviation from domain name naming rules. For each naming deviation type for which the number of deviations is non-zero, first benign and malicious probabilities that benign and malicious domains, respectively, have the naming deviation type are estimated. Second benign and malicious probabilities that any given domain is respectively benign and malicious are estimated. Probabilities that the domain is benign and malicious are estimated based on the number of deviations for each naming deviation type and based on the estimated first and second benign and malicious probabilities. Whether the domain is benign or malicious is determined based on the estimated probabilities that the domain is benign and malicious.

Abstract: A network traffic flow is directed to a computing services system is determined as being unrelated to the service that the system provides. In response, the network traffic flow is deflected away from the computing services system. Demand of the sender of the network traffic flow is managed by shaping the deflected network traffic flow in responding to the sender and/or by misinforming the sender in responding to the sender.

Abstract: According to examples, an apparatus may include a memory on which is stored instructions that when executed by a processor, cause the processor to extract, from network traffic data, a connectivity matrix that identifies connectivity data between entities and group the entities into a plurality of clusters based on the extracted connectivity matrix. The processor may also, for each cluster of the plurality of clusters, identify at least one representative entity that is to represent the entities in the cluster and output the identified at least one representative entity for identification of group behaviors of the entities in the plurality of clusters, in which the identified group behaviors are to be used for information technology management.

Abstract: First-order anomaly scores are received from related anomaly detectors. Each first-order anomaly score indicates a likelihood of an anomaly at a target system. A relatedness measure of the related anomaly detectors is determined, based on the first-order anomaly scores that have been received. A higher-order anomaly score is determined based on the relatedness measure that has been determined. The higher-order anomaly score indicates a likelihood of an anomaly at the target system. An anomaly at the target system is detected based on the higher-order anomaly score.

Abstract: Systems and methods for identifying, in a domain name, n-grams that do not appear in words of a given language, where n is greater than two are disclosed. The disclosed systems and methods may include comparing a value based on a number of the identified n-grams to a threshold and indicating that the domain name is potentially generated by malware in response to the value having a specified relationship with respect to the threshold.

Abstract: An apparatus may include a processor that may be caused to access a plurality of known domain names. The processor may be caused to determine a character embedding based on the plurality of known domain names. The character embedding may map each character of a known domain name to a respective vector. The processor may be caused to input the character embedding to a deep learning layer of a neural network. The processor may be caused to access a target domain name to be classified. The processor may be caused to classify the target domain name based on an output of the deep learning layer.

Abstract: In some examples, a system counts a number of digits in a domain name. The system compares a value based on the number of digits to a threshold, and indicates that the domain name is potentially generated by malware in response to the value having a specified relationship with respect to the threshold.

Abstract: Techniques and apparatuses are described to enable a strategically coordinated fictitious ecosystem of disinformation for cyber threat intelligence collection in a computing network. The ecosystem comprises fictitious profiles and supporting fictitious infrastructure information to portray in-depth, apparent authenticity of the ecosystem. Malicious communications from an adversary directed at the ecosystem are monitored, and threat intelligence about the adversary is collected to prevent future attacks.

Abstract: In some examples, for a device that transmitted domain names, a system determines a dissimilarity between the domain names, compares a value derived from the determined dissimilarity to a threshold, and identifies the device as malware infected in response to the comparing.

Abstract: In some examples, a Domain Name System (DNS) server is to receive, over a network, a DNS query containing a domain name, the DNS query sent by a device. The DNS server is to determine whether the domain name is potentially generated by malware. In response to determining that the domain name is potentially generated by malware, the DNS server is to generate a DNS response containing information indicating that the domain name is potentially generated by malware, and send the DNS response to the network.

Abstract: According to examples, an apparatus may include a processor and a memory on which is stored machine readable instructions to cause the processor to access network traffic traces including a plurality of timestamps, the plurality of timestamps having an order with respect to each other. The instructions may also cause the processor to encrypt the plurality of timestamps to anonymize the plurality of timestamps while preserving the order of the plurality of timestamps with respect to each other and to store the encrypted plurality of timestamps in a data store.

Abstract: For each of a number of naming deviation types, the number of deviations within a domain name of a domain is determined. Each naming deviation type is a different type of deviation from domain name naming rules. For each naming deviation type for which the number of deviations is non-zero, first benign and malicious probabilities that benign and malicious domains, respectively, have the naming deviation type are estimated. Second benign and malicious probabilities that any given domain is respectively benign and malicious are estimated. Probabilities that the domain is benign and malicious are estimated based on the number of deviations for each naming deviation type and based on the estimated first and second benign and malicious probabilities. Whether the domain is benign or malicious is determined based on the estimated probabilities that the domain is benign and malicious.

Abstract: A technique includes dynamically assigning, by a server, network addresses selected from a plurality of network addresses to network devices of a network based on a schedule. The schedule represents a time during which a given network address is to remain unassigned. The technique includes, based on the schedule, detecting anomalous behavior associated with the network.