Evaluation of deceptive mails using filtering & WEKA

In this paper we evaluate different supervised methods, we study the impact of different algorithms on deceptive messages. In deception detection, Bayesian Filters are widely and successfully applied for the sake of detecting and eliminating spam but, fail to function well in scenarios where false positives are penalized heavily. In our method we use different classifiers with different data sets. We have used WEKA interface in our integrated classification model and tested diverse classification algorithms. Our experimental study show significant performance in terms of classification accuracy with reduction of false positive instances. Random Forest & SVM Classifiers outperforms the conventional one in terms of increasing the true positives and the true negatives and increasing the overall accuracy.