US 8,131,655 B1Granted

Spam filtering using feature relevance assignment in neural networks

AssigneeBitdefender IPR Management Ltd.

InventorsAlexandru C Cosoi, Madalin S Vlad, Valentin Sgarciu

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Abstract

In some embodiments, a spam filtering method includes computing a pattern relevance for each of a set of message feature patterns, and using a neural network filter to classify incoming messages as spam or ham according to the pattern relevancies. Each message feature pattern is characterized by the simultaneous presence within a message of a specific set of message features (e.g., the presence of certain keywords within the message body, various message header heuristics, various message layout features, etc.). Each message feature may be spam- or ham-identifying, and may receive a tunable feature relevance weight from an external source (e.g. data file and/or human operator). The external feature relevance weights modulate the set of neuronal weights calculated through a training process of the neural network.

Claims16

Dependent claims

A spam filtering method comprising employing a computer system to perform the steps of:

computing a set of pattern relevancies for a set of feature patterns, wherein at least one pattern relevance of the set of pattern relevancies is computed according to a set of feature relevance weights determined through a process external to neuronal training;
and
classifying a target message as spam or ham according to a result of a processing of the target message by a neural network filter according to the set of pattern relevancies by:
assigning a pattern relevance of the set of pattern relevancies to each neuron of a subset of neurons of the neural network filter;
computing a target input vector characterizing the presence of a set of spam/ham identifying message features within the target message;
selecting an active neuron of the subset of neurons according to a scalar product between the target input vector and a first set of neuronal weights of the neural network filter;
computing a recognition score according to a scalar product between the target input vector and a second set of neuronal weights of the neural network filter, and according to the pattern relevance corresponding to the active neuron;
and
comparing the recognition score to a predefined vigilance threshold.

6 more elements

A non-transitory computer-readable medium storing instructions, which, when executed by a computer system, cause the computer system to form:

a training classifier configured to compute a set of pattern relevancies for a set of feature patterns, wherein at least one pattern relevance of the set of pattern relevancies is computed according to a set of feature relevance weights determined through a process external to neuronal training;
and
a neural network filter configured to classify a target message as spam or ham according to the set of pattern relevancies by
assigning a pattern relevance of the set of pattern relevancies to each neuron of a subset of neurons of the neural network filter;
computing a target input vector characterizing the presence of a set of spam/ham identifying message features within the target message;
selecting an active neuron of the subset of neurons according to a scalar product between the target input vector and a first set of neuronal weights of the neural network filter;
computing a recognition score according to a scalar product between the target input vector and a second set of neuronal weights of the neural network filter, and according to the pattern relevance corresponding to the active neuron;
and
comparing the recognition score to a predefined vigilance threshold.

6 more elements

Dependencies are inferred from the claim text.