An efficient hybrid system for anomaly detection in social networks

Cybersecurity

article

Rahman, Md. Shafiur¹  Halder, Sajal²  Uddin, Md. Ashraf²  Acharjee, Uzzal Kumar² 

[1] Department of Computer Science and Engineering, Dhaka International University;Department of Computer Science and Engineering, Jagannath University;Department Computer Science and Information Technology, RMIT University;Internet Commerce Security Laboratory, Federation University Australia

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

DOI  :  10.1186/s42400-021-00074-w

学科分类：社会科学、人文和艺术（综合）

来源: Springer

【 摘 要 】

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 privacy. The social network refers to a forum used by different groups of people to express their thoughts, communicate with each other, and share the content needed. This social networks also facilitate abnormal activities, spread fake news, rumours, misinformation, unsolicited messages, and propaganda post malicious links. Therefore, detection of abnormalities is one of the important data analysis activities for the identification of normal or abnormal users on the social networks. In 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 networks. We have extracted a list of unique features derived from users’ profile and contents. Using two kinds of dataset with the selected features, the proposed machine learning model called DT-SVMNB is trained. Our model classifies users as depressed one or suicidal one in the social network. We have conducted an experiment of our model using synthetic and real datasets from social network. The performance analysis demonstrates around 98% accuracy which proves the effectiveness and efficiency of our proposed system.

【 授权许可】

CC BY