2
Associate Professor, Department of Computer Engineering, Islamic Azad University, Mashhad
Abstract
At recent years, online social network sites have been popular dramatically. Cybercrimes use from social media as a new platform at acceptation of some types of computer crimes like phishing, spamming, malware spread and cyber harassment. In this research, we will improve the function of detecting cybercrime with the help of useful information in the messages. Choosing the best features with high separation. Strength between cyber harassment tweets and none cyber harassment is a complex activity which extremely needs substantially effort in making Machine Learning Model. In this way, we compare function of five classification methods Naive Bayes, Support Vector Machine, Decision Tree, k-Nearest Neighbor and Neural Network under five different tuning in order to selecting the best adjustment for suggested features. Also, we have improved C and Sigma parameters by using the bat, genetics and particle swarm algorithms. Additionally, we have compared five classification methods with default parameters and parameters obtained with optimization algorithms. Finally, we have shown that bat algorithm has had the best performance among other optimization algorithms. According to the research we did the most accuracy with the SVM model to 86.56 and the highest precision to 87.14.
A. Ebrahimi and S. Abolghasen, “Comprehensiveness to crime database in order to predict and identify crimes by using data mining techniques,” Electronic industries Journal, Term 6, 1394. (In Persian)##
B. Javad’zade, “Analyzing the centrality of social networks in cyber scope dealing with soft threats approach,” Scientific–Promotional quarterly passive defense, sixth year, no. 1, pp. 69-78, 1394. (In Persian)##
A. Abadi, “Electronic crimes detection by using data mining methods,” second national conference of computer engineering research, Ltamedan, Ekbatan research group,1395.(In Persian)##
A. Buczak and M. Gifford, “Fuzzy association rule mining for community crime pattern discovery,” In ACM SIGKDD Workshop on Intelligence and Security Informatics, ACM, 2010.##
T. Davidson, D. Warmsley, and M. Macy, “Automated hate speech detection and the problem of offensive language,” arxiv preprint arxiv:1703. 04009, 2017.##
H. Deylami and Y. Singh, “Cybercrime detection techniques based on support vector machines,” Artificial Intelligence Research, vol. 2(1), no.1, 2012.##
D. Karlis and L. Meligkotsidou, “Finite mixtures of multivariate Poisson distributions with application,” Journal of statistical Planning and Inference, vol. 137(6), pp. 1942-1960, 2007.##
J. Khan and S. Shaikh, “Computing in social networks with relationship algebra,” Journal of Network and Computer Applications, vol. 31, no. 4, pp. 862-878, 2008.##
B. Moon, J. McCluskey, and C. McCluskey, “A general theory of crime and computer crime: An empirical test,” Journal of Criminal Justice, vol. 38, no. 4, pp. 767-772, 2010.##
M. Malmasi, H. Shervin, and M. Zampieri, “Detecting Hate Speech in Social Media,” arxiv preprint arxiv:1712. 06427, 2017.##
A. Gaydhani, V. Dama, and S. Kendra, “Detecting hate speech and offensive Language on Twitter using machine learnimg : An N-gram and TFIDF based approach,” avxiv:1809. 08651v1, 2018.##
P. Tasi and P. Shyang, “Bat Algorithm Inspried Algorithm for Solving Numerical Optimization Problems,” Applied Mechanics and Materials, vol. 148-149, 2012.##
)
