Abstract
The faster and regular usage of Web 2.0 technologies like Online Social Networks (OSNs) addicted to millions of users worldwide. This popularity made target for spammers and fake users to spread phishing attack, viruses, false news, pornography and unwanted advertisements like URLs, images and videos etc. The present paper proposes a behavioral analysis-based framework for classifying spam contents in real time by aggregating machine learning techniques and genetic algorithm. The main procedure of the work is, firstly based on social networks spam policy, novel profile based and content-based features are proposed to facilitate spam detection. Secondly, accumulate a dataset from various social networks like Facebook, Twitter, and Instagram including spam and non-spam profiles. For suitable feature selections, we have used a genetic algorithm and various classifiers for decision making. In order to attest the effectiveness of our proposed framework, we have compared with existing techniques.
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Sahoo, S.R., Gupta, B.B., Choi, C., Hsu, CH., Chui, K.T. (2020). Behavioral Analysis to Detect Social Spammer in Online Social Networks (OSNs). In: Chellappan, S., Choo, KK.R., Phan, N. (eds) Computational Data and Social Networks. CSoNet 2020. Lecture Notes in Computer Science(), vol 12575. Springer, Cham. https://doi.org/10.1007/978-3-030-66046-8_26
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DOI: https://doi.org/10.1007/978-3-030-66046-8_26
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