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Design of a neural network model for classification, detection and prediction of cybercrime types

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Design of a neural network model for classification, detection and prediction of cybercrime types

Hadi Mokhtari Par, Mohammad Pourmahmood Aghababa

Department of Information Technology, Faculty of Electrical and Computer Engineering, Urmia University ofTechnology, Band Road, Urmia, Iran, E-mail: آدرس ایمیل جهت جلوگیری از رباتهای هرزنامه محافظت شده اند، جهت مشاهده آنها شما نیاز به فعال ساختن جاوا اسکریپت دارید

Department of Electrical Engineering, Faculty ofElectrical and ComputerEngineering, Urmia University of Technology, Band Road, Urmia, Iran, E-mail: آدرس ایمیل جهت جلوگیری از رباتهای هرزنامه محافظت شده اند، جهت مشاهده آنها شما نیاز به فعال ساختن جاوا اسکریپت دارید

Abstract.Nowadays, solutions in the area of cyber criminology have been presented to accelerate discovery and to reduce the amount of crime events. Due to the scientific and mathematical knowledge, neural network models have attracted the attention of crime sociologists. In this paper, KDD database, which includes different cybercrimes and their happening features, is used for classification and detection of cybercrimes. And cybercrime prediction is done using multilayer perceptron (MLP) neural networks. In this model, the complex relationships between data related to crime is investigated and cybercrimes and attacks are included in 5 groups, including normal attacks, DOS, U2L, R2L and probing, with 42 cyber attack types are classified. Finally, a response which reveals that if there is an attack or not and attack type detection (if possible) is performed. Simulation results show that the proposed intelligent system can be efficient and useful for detecting and forecasting cybercrimes.

Keywords:cybercrime,neural network, artificial intelligence, information technology, classification

 

آخرین بروز رسانی در دوشنبه, 08 خرداد 1396 ساعت 07:49
تالیفات مقالات Design of a neural network model for classification, detection and prediction of cybercrime types