Systems and methods for dynamically integrating heterogeneous anti-spam filters
First Claim
1. A spam filtering method comprising employing at least one processor to perform the steps of:
- computing a relevance of a first anti-spam filter according to a relationship R=M−
T×
f(N,P), wherein M represents a training maturity of the first filter, T is an update age of the first filter indicative of a time period since a latest update of the first filter, wherein P is a false positive classification indicator of the first filter, N is a false negative classification indicator of the first filter, and wherein ƒ
is a function of N and P;
computing a relevance of a second anti-spam filter according to a training maturity of the second filter, an update age of the second filter indicative of a time period since the latest update of the second filter, a false positive classification indicator of the second filter, and a false negative classification indicator of the second filter; and
determining whether an electronic communication is spam or non-spam according to a first result generated by applying the first anti-spam filter to the electronic communication, a second result generated by applying the second anti-spam filter to the electronic communication, the relevance of the first filter, and the relevance of the second filter.
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Abstract
In some embodiments, a spam filtering method includes computing the relevance of each of a plurality of anti-spam filters according to a relevance parameter set, and deciding whether an electronic message is spam or non-spam according to the relevancies and individual classification scores generated by the anti-spam filters. The relevance of an anti-spam filter indicates the degree to which a classification score produced by that particular filter determines the final classification of a given message. The relevance parameter set of each anti-spam filter may include, among others, a training maturity indicative of the degree of training of the filter, a filter update age indicative of the time elapsed since the latest update of the filter, a false-positive classification indicator, and a false-negative classification indicator of the anti-spam filter.
119 Citations
16 Claims
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1. A spam filtering method comprising employing at least one processor to perform the steps of:
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computing a relevance of a first anti-spam filter according to a relationship R=M−
T×
f(N,P), wherein M represents a training maturity of the first filter, T is an update age of the first filter indicative of a time period since a latest update of the first filter, wherein P is a false positive classification indicator of the first filter, N is a false negative classification indicator of the first filter, and wherein ƒ
is a function of N and P;computing a relevance of a second anti-spam filter according to a training maturity of the second filter, an update age of the second filter indicative of a time period since the latest update of the second filter, a false positive classification indicator of the second filter, and a false negative classification indicator of the second filter; and determining whether an electronic communication is spam or non-spam according to a first result generated by applying the first anti-spam filter to the electronic communication, a second result generated by applying the second anti-spam filter to the electronic communication, the relevance of the first filter, and the relevance of the second filter. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A non-transitory computer readable medium storing instructions, which, when executed by a computer system, cause the computer system to form:
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a first anti-spam filter and a second anti-spam filter configured to input an electronic communication and to compute first and second spam classification scores, respectively, for the electronic communication; a filter relevance calculator configured to compute a relevance of the first anti-spam filter and a relevance of the second anti-spam filter, wherein computing the relevance of the first filter comprises setting the relevance of the first filter to a value substantially equal to R=M−
T×
f(N,P), wherein M, T, N, P represent a training maturity of the first filter, an update age of the first filter indicative of a time period since a latest update of the first filter, a false negative classification indicator of the first filter, and a false positive classification indicator of the first filter, respectively, wherein ƒ
represents a function of N and P, and wherein the second relevance is computed according to a training maturity of the second filter, an update age of the second filter indicative of a time period since a latest update of the second filter, a second false negative classification indicator of the second filter, and a second false positive classification indicator of the second filter; anda decision module connected to the first and second anti-spam filters and to the filter relevance calculator, configured to determine whether an electronic communication is spam or non-spam according to the first and second spam classification scores, the relevance of the first filter, and the relevance of the second filter.
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8. A spam filtering method comprising employing at least one processor to perform the steps of:
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computing a relevance of a first anti-spam filter according to a first relevance parameter set, the first relevance parameter set including a training maturity of the first filter, a false positive classification indicator of the first filter, and a false negative classification indicator of the first filter, and an update age of the first filter indicative of a time period since a latest update of the first filter; computing a relevance of a second anti-spam filter according to a second relevance parameter set, the second relevance parameter set including an a training maturity of the second filter, a false positive classification indicator of the second filter, and a false negative classification indicator of the second filter, and an update age of the second filter, indicative of a time period since a latest update of the second filter; and determining whether an electronic communication is spam or non-spam according to a first result generated by applying the first filter to the electronic communication, a second result generated by applying the second filter to the electronic communication, the relevance of the first filter, and the relevance of the second filter. - View Dependent Claims (9, 10, 11, 12, 13, 14, 15)
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16. A non-transitory computer readable medium encoding instructions which, when executed by at least one processor, cause the at least one processor to perform the steps of:
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computing a relevance of a first anti-spam filter according to a difference between i. a training maturity of the first filter and ii. a product of an update age of the first filter and a function of a false negative classification indicator and a false positive classification indicator of the first filter, computing a relevance of a second anti-spam filter according to a training maturity of the second filter, an update age of the second filter indicative of a time period since a latest update of the second filter, a false negative classification indicator of the second filter, and a false positive classification indicator of the second filter; and determining whether an electronic communication is spam or non-spam according to a first result generated by applying the first anti-spam filter to the electronic communication, a second result generated by applying the second anti-spam filter to the electronic communication, the relevance of the first filter, and the relevance of the second filter.
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Specification