TY - GEN
T1 - Behavioral Analysis to Detect Social Spammer in Online Social Networks (OSNs)
AU - Sahoo, Somya Ranjan
AU - Gupta, B. B.
AU - Choi, Chang
AU - Hsu, Ching Hsien
AU - Chui, Kwok Tai
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Facebook
KW - Machine learning
KW - Online social networks
KW - PSO
UR - http://www.scopus.com/inward/record.url?scp=85101410894&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-66046-8_26
DO - 10.1007/978-3-030-66046-8_26
M3 - Conference contribution
AN - SCOPUS:85101410894
SN - 9783030660451
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 321
EP - 332
BT - Computational Data and Social Networks - 9th International Conference, CSoNet 2020, Proceedings
A2 - Chellappan, Sriram
A2 - Choo, Kim-Kwang Raymond
A2 - Phan, NhatHai
T2 - 9th International Conference on Computational Data and Social Networks, CSoNet 2020
Y2 - 11 December 2020 through 13 December 2020
ER -