Spam? Not any more !! Detecting spam s using neural networks ECE/CS/ME 539 Project presentation Submitted by Sivanadyan, Thiagarajan

Importance of the topic  Spam is unsolicited and unwanted s  Wastage of bandwidth, storage space and most of all, recipient’s time Goals of the Anti-spam Network  Reliably block spam mails  Should not block any non-spam mails, but can allow few spam mails to slip through  Adapt to the specific types of messages

Input Features – Data Set  Original data set: 57 input attributes  Output attribute: 1 (for spam) 0 (for nonspam)  Inputs derived from content  Attributes indicate the frequency of specific words and characters  Examples: ‘credit’, ‘free’ (in spam) ‘meeting’, ’project’, (in nonspam)

Preprocess the data  Choose only the inputs which differ for spam and non-spam mails  Two reduced data sets are obtained (21 Inputs and 9 Inputs)  The data is made zero mean, unit variance (4025 Input Vectors)  Split the data into two independent training and testing data sets

MLP Implementation  Learning by back propagation algorithm  Using complete data set Poor performance (Classification rate: 63.2%)Poor performance (Classification rate: 63.2%) Classified most of the mails as non-spamClassified most of the mails as non-spam  Using reduced data set (Inputs – 21) Good performance (Classification rate: 93.8%)Good performance (Classification rate: 93.8%) All the non-spam is detectedAll the non-spam is detected Optimal MLP Configuration: Optimal MLP Configuration:

Cross Validation  Using reduced data set (Inputs – 9) Good performance (Classification rate: 92.1%)Good performance (Classification rate: 92.1%) Nearly all the non-spam is detectedNearly all the non-spam is detected Optimal MLP Configuration: Optimal MLP Configuration:  Using Cross - Validation Negligible improvement in performanceNegligible improvement in performance Since all the data is derived from the same source, cross validation offers no advantageSince all the data is derived from the same source, cross validation offers no advantage

Inference of the results  Larger number of inputs does not necessarily improve the performance  It is important to remove redundant and irrelevant features  There is no optimum MLP configuration for all inputs – need to adapt depending on the content  A combination of other types of spam filters along with neural networks can be used

Conclusion  Neural networks are a viable option in spam filtering  A number of heuristic methods are being increasingly applied in this field  Need to exploit the differences between spam and ‘good’ s  Further opportunities Data sets from different sources need to be used for trainingData sets from different sources need to be used for training Fuzzy logic and combinational algorithms can be used in this applicationFuzzy logic and combinational algorithms can be used in this application