Abstract: A processing device comprises a processor coupled to a memory and is configured to determine a first set of features from domain name system (DNS) information, the first set of features being defined over a domain, and to determine a second set of features from the DNS information, the second set of features being defined over internet protocol (IP) addresses returned for the domain. The processing device is further configured to compute a fast-flux score based on the first and second sets of features, and to utilize the fast-flux score to characterize fast-flux activity relating to the domain. For example, the processing device can be configured to compare the fast-flux score to a threshold, and to generate an indicator of the presence or absence of fast-flux activity based on a result of the comparison. The processing device may be implemented in a computer network or network security system.

Abstract: A processing device comprises a processor coupled to a memory and is configured to obtain at least one rule set utilized to detect malicious activity in a computer network, to determine one or more trigger conditions for each of a plurality of rules of the at least one rule set, to identify alerts generated responsive to the determined trigger conditions, to compute correlations between respective pairs of the plurality of rules based on the identified alerts, and to aggregate groups of two or more of the plurality of rules into respective aggregated rules based at least in part on the computed correlations. The aggregated rules are illustratively applied in conjunction with remaining unaggregated ones of the plurality of rules of the one or more rule sets to detect malicious activity in the computer network. The processing device may be implemented in a computer network or network security system.

Abstract: Data driven device detection is provided, whereby a device is detected by obtaining a plurality of feature values for a given device; obtaining a set of device attributes for a plurality of potential devices; calculating a probability value that the given device is each potential device within the plurality of potential devices; identifying a candidate device associated with a maximum probability value among the calculated probability values; and labeling the given device as the candidate device if the associated maximum probability value satisfies a predefined threshold. The predefined threshold can be a function, for example, of whether the given user has previously used this device. The obtained feature values can be obtained for a selected set of features satisfying one or more predefined characteristic criteria. The device attributes can be obtained, for example, from a profile for each of the plurality of potential devices.