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An efficient hybrid system for anomaly detection in social networks

- Rahman, Md Shafiur, Halder, Sajal, Uddin, Ashraf, Acharjee, Uzzal

Authors: Rahman, Md Shafiur , Halder, Sajal , Uddin, Ashraf , Acharjee, Uzzal
Date: 2021
Type: Text , Journal article
Relation: Cybersecurity Vol. 4, no. 1 (2021), p.
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Description: 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. © 2021, The Author(s).

CRICRATE : A cricket match conduction and player evaluation framework

- Uddin, Md Ashraf, Hasan, Mahmudul, Halder, Sajal, Ahamed, Sajeeb, Acharjee, Uzzal

Authors: Uddin, Md Ashraf , Hasan, Mahmudul , Halder, Sajal , Ahamed, Sajeeb , Acharjee, Uzzal
Date: 2019
Type: Text , Conference paper
Relation: International Conference on Emerging Technologies in Data Mining and Information Security, IEMIS 2018 Vol. 755, p. 491-500
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Description: Cricket has appeared as one of the most favorite outdoor games in the present world. The cricket players represent a country and create economic, political, and diplomatic relations among nations. The cricket board of a country requires selecting the fittest players for the upcoming team among some good players. We propose an architecture called Cricket Match Conduction and Player Evaluation Framework by developing some algorithms to predict the score of the players as well as the algorithm to evaluate the man of the match in one day or test cricket match. We implemented the framework by Weka and web technology. © Springer Nature Singapore Pte Ltd. 2019.

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