A Security Model for the Classification of Suspicious Data Using Machine Learning Techniques

Main Article Content

Boussi Grace Odette, Himanshu Gupta, Syed Akhter Hossain

Abstract

Cybercrime first emerged in 1981 and gained significant attention in the 20th century. The proliferation of technology and our increasing reliance on the internet have been major factors contributing to the growth of cybercrime. Different countries face varying types and levels of cyber-attacks, with developing countries often dealing with different types of attacks compared to developed countries. The response to cybercrime is usually based on the resources and technological capabilities available in each country. For example, sophisticated attacks involving machine learning may not be common in countries with limited technological advancements. Despite the variations in technology and resources, cybercrime remains a costly issue worldwide, projected to reach around 8 trillion by 2023. Preventing and combating cybercrime has become crucial in our society. Machine learning techniques, such as convolutional neural networks (CNN), recurrent neural networks (RNN), and more, have gained popularity in the fight against cybercrime. Researchers and authors have made significant contributions in protecting and predicting cybercrime. Nowadays, many corporations implement cyber defense strategies based on machine learning to safeguard their data. In this study, we utilized five different machine learning algorithms, including CNN, LSTM, RNN, GRU, and MLP DNN, to address cybercrime. The models were trained and tested using the InSDN public dataset. Each model provided different levels of trained and test accuracy percentages.

Article Details

How to Cite
Boussi Grace Odette, et al. (2023). A Security Model for the Classification of Suspicious Data Using Machine Learning Techniques. International Journal on Recent and Innovation Trends in Computing and Communication, 11(10), 17–22. https://doi.org/10.17762/ijritcc.v11i10.8459
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Articles
Author Biography

Boussi Grace Odette, Himanshu Gupta, Syed Akhter Hossain

Boussi Grace Odette1, Himanshu Gupta2, Syed Akhter Hossain3

1Ph.D. Scholar, AIIT

Amity University

Noida, India

graceboussi@gmail.com

2Professor, AIIT

Amity University

Noida, India

hgupta@amity.edu

3Professor, Computer Science and Engineering Department

University of Liberal Arts

Dhaka, Bangladesh

aktarhossain@daffodilvarsity.edu.bd

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https://aseados.ucd.ie/datasets/SDN/