2019 · 12 citations · 16 references
ComputingComputer ScienceCybersecurityComputer SecurityFeature EngineeringMachine LearningClassificationDecision TreesEmail SecurityEnsemble MethodsMalware ClassificationSupport Vector MachinesEmail Malware DetectionK-nearest NeighborsMachine Learning For Malware ClassificationMachine Learning For Phishing DetectionPhishing ClassificationPhishing Email AnalysisRandom Forest ClassificationSpam Detection
An inherent part of everyday life and work on a computer is ownership and use of an email address. The main aim of this paper is to analyze existing approaches to classification of malicious emails. We have implemented a system, which is able to distinguish between legitimate and malicious emails. Subsequently, malicious emails are classified into three subcategories: spam, scam, and phishing. We prepared a labeled dataset. We extracted several features from emails contained in the dataset. Within the system, we have implemented four supervised machine learning methods (Random Forest, Decision Tree, Support Vector Machines, k-Nearest Neighbors) and evaluated them. According to our results, the Random Forest is the most suitable approach for email classification.
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Scikit-learn: Machine Learning in Python
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