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This paper presents the cybercrime detection model by using support vector machines (SVMs) to classify social network (Facebook) dataset. We try to compare between three kinds of classification algorithms such as: SVMs, AdaBoostM1, and NaiveBayes in order to find a high percentage of classification accuracy. Finally, we conclude SVMs as the best(More)
Malware is an international software disease. Research shows that the effect of malware is becoming chronic. To protect against malware detectors are fundamental to the industry. The effectiveness of such detectors depends on the technology used. Therefore, it is paramount that the advantages and disadvantages of each type of technology are scrutinized(More)
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(More)
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