Adaboost and SVM based cybercrime detection and prevention model

This paper aims to propose cybercrime detection and prevention model by using Support Vector Machine (SVM) and AdaBoost algorithm in order to reduce data damaging due to running of malicious codes. The performance of this model will be evaluated on a Facebook dataset, which includes benign executable and malicious codes. The main objective of this paper is to find the effectiveness of different classifiers on the Facebook dataset for crime detection. Finally, we try to compare the classifier accuracy of SVM and AdaBoost by using Weka 3.7.4 software in order to choose the best model to classify the Facebook dataset with high accuracy.