RO  EN
IMCS/Publications/CSJM/Issues/CSJM v.29, n.2 (86), 2021/

Cybercrime Detection Using Semi-Supervised Neural Network

Authors: Abbas Karimi, Saber Abbasabadei, Javad Akbari Torkestani, Faraneh Zarafshan
Keywords: cybercrime, intrusion detection, neural network, semi-supervised classification.

Abstract

Nowadays, artificial intelligence is widely used in various fields and industries. Cybercrime is a concern of these days, and artificial intelligence is used to detect this type of crime. Crime detection systems generally detect the crime by training from the related data over a period of time, but sometimes some samples in a dataset may have no label. Therefore, in this paper, a method based on semi-supervised neural network is presented regarding crime types detection. As the neural network is a supervised classification system, therefore, this paper presents a pseudo-label method for neural network optimization and develops it to semi-supervised classification. In the proposed method, firstly the dataset is divided into two sections, labelled and unlabelled, and then the trained section is used to estimate the labelling of the unlabelled samples based on pseudo-labels. The results indicate that the proposed method improves the accuracy, Precision and Recall up to 99.83%, 99.83% and 99.83%, respectively.

Abbas Karimi
Assistant Professor, Department of Computer Engineering,
Islamic Azad University, Arak Branch,
Arak, Markazi Provience, Iran.
E-mail:

Saber Abbasabadei
PhD student, Department of Computer Engineering,
Islamic Azad University, Arak Branch,
Arak, Markazi Provience, Iran.
E-mail:

Javad Akbari Torkestani
Associate Professor, Department of Computer Engineering,
Islamic Azad University, Arak Branch,
Arak, Markazi Provience, Iran.
E-mail:

Faraneh Zarafshan
Assistant Professor, Department of Computer Engineering,
Islamic Azad University, Arak Branch,
Arak, Markazi Provience, Iran.
E-mail:



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