A computer-implemented method for generating a sequence that classifies a data object to determine whether the data object is a malicious object, the method comprising: evaluating a data object based on a plurality of rules, each rule of the plurality of rules associated with a respective weight value and with a respective trait, to select expressed traits, wherein an expressed trait corresponds to a rule that matches the data object; generating a sequence comprising each of the weight values associated with the expressed traits; calculating a sequence weight comprising multiplying weight values associated with each of the expressed traits by a weight multiplier, wherein calculating a sequence weight comprises reducing a weight multiplier of an expressed trait for a subsequent expression of the same trait, including: determining whether a first rule (220) of the plurality of rules matches the data object for a first time; in accordance with determining that the first rule (220) of the plurality of rules matches the data object for the first time, utilizing a first weight multiplier in calculating the sequence weight; determining whether the first rule (220) of the plurality of rules matches the data object for a second time that is subsequent to the first time; and in accordance with determining that the first rule (220) of the plurality of rules matches the data object for the second time that is subsequent to the first time, utilizing a second weight multiplier that is less than the first weight multiplier, by reducing the first weight multiplier by a decay constant, in calculating the sequence weight; and storing the generated sequence in a selected target memory of the electronic device. evaluating a data object based on a plurality of rules, each rule of the plurality of rules associated with a respective weight value and with a respective trait, to select expressed traits, wherein an expressed trait corresponds to a rule that matches the data object; generating a sequence comprising each of the weight values associated with the expressed traits; calculating a sequence weight comprising multiplying weight values associated with each of the expressed traits by a weight multiplier, wherein calculating a sequence weight comprises reducing a weight multiplier of an expressed trait for a subsequent expression of the same trait, including: determining whether a first rule (220) of the plurality of rules matches the data object for a first time; in accordance with determining that the first rule (220) of the plurality of rules matches the data object for the first time, utilizing a first weight multiplier in calculating the sequence weight; determining whether the first rule (220) of the plurality of rules matches the data object for a second time that is subsequent to the first time; and in accordance with determining that the first rule (220) of the plurality of rules matches the data object for the second time that is subsequent to the first time, utilizing a second weight multiplier that is less than the first weight multiplier, by reducing the first weight multiplier by a decay constant, in calculating the sequence weight; and determining whether a first rule (220) of the plurality of rules matches the data object for a first time; in accordance with determining that the first rule (220) of the plurality of rules matches the data object for the first time, utilizing a first weight multiplier in calculating the sequence weight; determining whether the first rule (220) of the plurality of rules matches the data object for a second time that is subsequent to the first time; and in accordance with determining that the first rule (220) of the plurality of rules matches the data object for the second time that is subsequent to the first time, utilizing a second weight multiplier that is less than the first weight multiplier, by reducing the first weight multiplier by a decay constant, in calculating the sequence weight; and storing the generated sequence in a selected target memory of the electronic device.