Identifying Internet Crime Using Mamdani Fuzzy Rules and Fuzzy Classification Algorithm

Musa Mojarad¹ , Afsaneh Haghanin Nia² , Neda Galehdari³ , Khayronsa Bazarganzadeh⁴

Section:Research Paper, Product Type: Journal-Paper
Vol.8 , Issue.2 , pp.18-23, Jun-2021

Online published on Jun 30, 2021

Copyright © Musa Mojarad, Afsaneh Haghanin Nia, Neda Galehdari, Khayronsa Bazarganzadeh . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library

XML View     PDF Download

How to Cite this Paper

Abstract :
Crime prevention has always been one of the basic and important issues in human life that has been applied in different ways throughout history. In this research, fuzzy decision tree classification model, Mamdani fuzzy rules are used to identify cybercrime. The aim of this study is to identify the important features of specific crimes and classify crimes into three different categories. We use only a few features for classification work, which reduces the size of the collection. Reducing the complexity of the tree reduces the fuzzy decision for classifying mass data sets. In order to improve the results of fuzzy decision tree classification, the optimal number of language semesters in fuzzy sets for each feature is identified. Experiments have been performed to evaluate the proposed model on the actual data set of the crime and the results of the proposed model in the average mode show an accuracy of 98,13% in detecting the type crime. The results show the superiority of the proposed method compared to other methods.

Key-Words / Index Term :
Internet crime detection, Fuzzy decision tree, Mamdani fuzzy rules, Feature selection

References :
[1] Soleimanian Gharehchopogh, F., & Haggi, S. (2020). An Optimization K-modes clustering algorithm with elephant herding optimization algorithm for crime clustering. Journal of Advances in Computer Engineering and Technology, 6(2), 79-90.
[2] Farsi, M., Daneshkhah, A., Far, A. H., Chatrabgoun, O., & Montasari, R. (2018). Crime data mining, threat analysis and prediction. In Cyber Criminology (pp. 183-202). Springer, Cham.
[3] Inouye, D. I., Yang, E., Allen, G. I., & Ravikumar, P. (2017). A review of multivariate distributions for count data derived from the Poisson distribution. Wiley Interdisciplinary Reviews: Computational Statistics, 9(3), e1398.
[4] Moon, B., McCluskey, J. D., McCluskey, C. P., & Lee, S. (2013). Gender, general theory of crime and computer crime: An empirical test. International journal of offender therapy and comparative criminology, 57(4), 460-478.
[5] McMahon, R., Serrato, D., Bressler, L., & Bressler, M. (2015). Fighting cybercrime calls for developing effective strategy. Journal of Technology Research, 6, 1.
[6] Li, S. T., Kuo, S. C., & Tsai, F. C. (2010). An intelligent decision-support model using FSOM and rule extraction for crime prevention. Expert Systems with Applications, 37(10), 7108-7119.
[7] [Gerber, M. S. (2014). Predicting crime using Twitter and kernel density estimation. Decision Support Systems, 61, 115-125.
[8] Tuominen, T., Korhonen, T., Hämäläinen, H., Katajisto, J., Vartiainen, H., Joukamaa, M., ... & Lauerma, H. (2017). The factors associated with criminal recidivism in Finnish male offenders: importance of neurocognitive deficits and substance dependence. Journal of Scandinavian Studies in Criminology and Crime Prevention, 18(1), 52-67.
[9] Akers, R. (2017). Social learning and social structure: A general theory of crime and deviance. Routledge.
[10] Prabakaran, S., & Mitra, S. (2018, April). Survey of Analysis of Crime Detection Techniques Using Data Mining and Machine Learning. In Journal of Physics: Conference Series (Vol. 1000, No. 1, p. 012046). IOP Publishing.
[11] Chastain, B., Qiu, F., & Piquero, A. R. (2016). Crime theory evaluation using simulation models of residential burglary. American Journal of Criminal Justice, 41(4), 814-833.
[12] Li, S. T., Kuo, S. C., & Tsai, F. C. (2010). An intelligent decision-support model using FSOM and rule extraction for crime prevention. Expert Systems with Applications, 37(10), 7108-7119.
[13] Annabathula, R. (2007). A Web-based tool for analysis of crime laboratory data. West Virginia University.