Malicious data classification in packet data network through hybrid meta deep learning

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.

Detalhes bibliográficos
Principais autores:	Tapu, Sakib Uddin, Alam Shopnil, Samira Afrin, Tamanna, Rabeya Bosri
Outros Autores:	Alam, Md. Golam Rabiul
Formato:	Tese
Idioma:	English
Publicado em:	Brac University 2023
Assuntos:	Reinforcement learning A2C PPO Meta-learning Few-shot-learning Siamese-network Prototypical-network Intrusion-detection Malicious-data-classification CSE-CIC-IDS2017 CSE-CIC-IDS2018 System safety. Machine learning
Acesso em linha:	http://hdl.handle.net/10361/19352

id	10361-19352
record_format	dspace
spelling	10361-193522023-08-08T21:02:02Z Malicious data classification in packet data network through hybrid meta deep learning Tapu, Sakib Uddin Alam Shopnil, Samira Afrin Tamanna, Rabeya Bosri Alam, Md. Golam Rabiul Department of Computer Science and Engineering, Brac University Reinforcement learning A2C PPO Meta-learning Few-shot-learning Siamese-network Prototypical-network Intrusion-detection Malicious-data-classification CSE-CIC-IDS2017 CSE-CIC-IDS2018 System safety. Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 50-52). Advancements in wireless network technology have provided a powerful tool to boost productivity and serve a strong communication which overcomes the limitations of wired networks. However, because of using wireless networks, security is an increasing concern among the community. At the time of our study, we are in the era of 5G networks. Although we are in the 5th generation of telecommunication we are still struggling with security. The upcoming generation, 6G, aims to solve the security concerns by providing a secure and trust networking system. In our study, we aim to integrate AI and more advanced infrastructure which will provide a tremendous solution in this regard. In order to deal with this issue we primarily aim to come up with a solution that provides a reliable intrusion detection system in spite of being trained with a small amount of data. In our study, we aim to integrate AI and more advanced infrastructure which will provide a tremendous solution in this regard. Thus, we employed a trusted networking system based on AI. Here, at first we primarily focused on Reinforcement Learning (RL) to classify the network data coming from the untrusted packet data networks (PDN), whether it is malicious or not. Another existing problem is people currently rely on machine learning techniques to create a trustworthy networking system. However, it hinders the development of getting a reliable network as the number of real publicly available malicious data is not sufficient to train a model properly and in real life people are not very keen to share these data as they are sensitive. Therefore, we propose a novel idea of hybrid meta learning in the detection of malicious packet data. We use a combination of Siamese and Prototypical network where Siamese network is used for binary classification and Prototypical network is used for multi class classification. As both approaches are based on meta learning techniques, it requires a very small amount of data. By utilizing this characteristic of meta learning, we were able to train our model with just 3000 data samples and achieve more than 90% accuracy for both meta learning tactics. Lastly we provide a comprehensive study on the given RL methods and hybrid meta learning and share our future thoughts. The purpose of our study is to provide a secure and trustworthy network domain which enhances the communication between end users. Sakib Uddin Tapu Samira Afrin Alam Shopnil Rabeya Bosri Tamanna B. Computer Science and Engineering 2023-08-08T05:26:35Z 2023-08-08T05:26:35Z 2023 2023-01 Thesis ID: 18301271 ID: 18301076 ID: 18301188 http://hdl.handle.net/10361/19352 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 52 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Reinforcement learning A2C PPO Meta-learning Few-shot-learning Siamese-network Prototypical-network Intrusion-detection Malicious-data-classification CSE-CIC-IDS2017 CSE-CIC-IDS2018 System safety. Machine learning
spellingShingle	Reinforcement learning A2C PPO Meta-learning Few-shot-learning Siamese-network Prototypical-network Intrusion-detection Malicious-data-classification CSE-CIC-IDS2017 CSE-CIC-IDS2018 System safety. Machine learning Tapu, Sakib Uddin Alam Shopnil, Samira Afrin Tamanna, Rabeya Bosri Malicious data classification in packet data network through hybrid meta deep learning
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
author2	Alam, Md. Golam Rabiul
author_facet	Alam, Md. Golam Rabiul Tapu, Sakib Uddin Alam Shopnil, Samira Afrin Tamanna, Rabeya Bosri
format	Thesis
author	Tapu, Sakib Uddin Alam Shopnil, Samira Afrin Tamanna, Rabeya Bosri
author_sort	Tapu, Sakib Uddin
title	Malicious data classification in packet data network through hybrid meta deep learning
title_short	Malicious data classification in packet data network through hybrid meta deep learning
title_full	Malicious data classification in packet data network through hybrid meta deep learning
title_fullStr	Malicious data classification in packet data network through hybrid meta deep learning
title_full_unstemmed	Malicious data classification in packet data network through hybrid meta deep learning
title_sort	malicious data classification in packet data network through hybrid meta deep learning
publisher	Brac University
publishDate	2023
url	http://hdl.handle.net/10361/19352
work_keys_str_mv	AT tapusakibuddin maliciousdataclassificationinpacketdatanetworkthroughhybridmetadeeplearning AT alamshopnilsamiraafrin maliciousdataclassificationinpacketdatanetworkthroughhybridmetadeeplearning AT tamannarabeyabosri maliciousdataclassificationinpacketdatanetworkthroughhybridmetadeeplearning
_version_	1814307657622749184

Malicious data classification in packet data network through hybrid meta deep learning

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