Social Engineering Detection Using Natural Language Processing and Machine Learning

This paper presents a system to identify social engineering attacks using only text as input. This system can be used in different environments which the input is text such as SMS, chats, emails, etc. The system uses Natural Language Processing to extract features from the dialog text such as URL's report and count, spell check, blacklist count, and others. The features are used to train Machine Learning algorithms (Neural Network, Random Forest and SVM) to perform classification of social engineering attacks. The classification algorithms showed an accuracy over 80% to detect this type of attacks.