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An efficient hybrid system for anomaly detection in social networks

Cybersecurity 2021年第1期4卷 136-146页

作者： Md.Shafiur Rahman Sajal Halder mdashraf uddin Uzzal Kumar Acharjee Department of Computer Science and Engineering Dhaka International UniversityDhakaBangladesh Department of Computer Science and Engineering Jagannath UniversityDhakaBangladesh Department Computer Science and Information Technology RMIT UniversityMelbourneAustralia Internet Commerce Security Laboratory Federation University AustraliaBallarat VIC 3350Australia

Anomaly detection has been an essential and dynamic research area in the data mining.A wide range of applications including different social medias have adopted different state-of-the-art methods to identify anomaly for ensuring user’s security and *** social network refers to a forum used by different groups of people to express their thoughts,communicate with each other,and share the content *** social networks also facilitate abnormal activities,spread fake news,rumours,misinformation,unsolicited messages,and propaganda post malicious ***,detection of abnormalities is one of the important data analysis activities for the identification of normal or abnormal users on the social *** this paper,we have developed a hybrid anomaly detection method named DT-SVMNB that cascades several machine learning algorithms including decision tree(C5.0),Support Vector Machine(SVM)and Naïve Bayesian classifier(NBC)for classifying normal and abnormal users in social *** have extracted a list of unique features derived from users’profile and *** two kinds of dataset with the selected features,the proposed machine learning model called DT-SVMNB is *** model classifies users as depressed one or suicidal one in the social *** have conducted an experiment of our model using synthetic and real datasets from social *** performance analysis demonstrates around 98%accuracy which proves the effectiveness and efficiency of our proposed system.

关键词： Anomaly Detection Machine Learning Hybrid Anomaly Detection Social Networks

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