Abstract: A method and system for predicting subsequent cyber-attacks in attack campaigns are provided. The method includes receiving events data related to cyber-attacks occurring in a network during a predefined time window; extracting at least one sequence from the received events data at least one attack vector; generating a sequence signature for each of the at least one extracted sequence; comparing each sequence signature to a representation of historic sequence signatures to determine at least partially matching sequence signature; and based on the matching sequence, determining at least one subsequent cyber-attack in a respective sequence.

Abstract: A method and system for predicting subsequent cyber-attacks in attack campaigns are provided. The method includes receiving events data related to cyber-attacks occurring in a network during a predefined time window; extracting at least one sequence from the received events data at least one attack vector; generating a sequence signature for each of the at least one extracted sequence; comparing each sequence signature to a representation of historic sequence signatures to determine at least partially matching sequence signature; and based on the matching sequence, determining at least one subsequent cyber-attack in a respective sequence.

Abstract: A method and apparatus for processing top-k queries are disclosed. For example, the method receives a top-k query with a value for a number of samples and a value of a confidence parameter. The method samples in accordance to the number of samples, and determines a top-k weight of a sample top-k set. The method bounds the top-k weight in an interval having an upper bound and a lower bound such that the top-k weight is in the interval with a probability equal to one minus the value of the confidence parameter, and provides a response to the top-k query in accordance with the upper and lower bounds.

Abstract: A method and apparatus for processing top-k queries are disclosed. For example, the method receives a top-k query with a value for a number of samples and a value of a confidence parameter. The method samples in accordance to the number of samples, and determines a top-k weight of a sample top-k set. The method bounds the top-k weight in an interval having an upper bound and a lower bound such that the top-k weight is in the interval with a probability equal to one minus the value of the confidence parameter, and provides a response to the top-k query in accordance with the upper and lower bounds.