Classification and Explanation of Different Internet of Things (IoT) Network Attacks using Machine Learning, Deep Learning and XAI

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.

Détails bibliographiques
Auteurs principaux:	Tasnim, Anika, Hossain, Nigah, Tabassum, Sabrina, Parvin, Nazia
Autres auteurs:	Hossain, Dr. Muhammad Iqbal
Format:	Thèse
Langue:	en_US
Publié:	Brac University 2022
Sujets:	Machine Learning Prediction Decision tree Random Forest XGBoost Adaboost XAI Model Internet of things Cognitive learning theory (Deep learning)
Accès en ligne:	http://hdl.handle.net/10361/17642

id	10361-17642
record_format	dspace
spelling	10361-176422022-12-13T21:01:44Z Classification and Explanation of Different Internet of Things (IoT) Network Attacks using Machine Learning, Deep Learning and XAI Tasnim, Anika Hossain, Nigah Tabassum, Sabrina Parvin, Nazia Hossain, Dr. Muhammad Iqbal Rahman, Rafeed Department of Computer Science and Engineering, Brac University Machine Learning Prediction Decision tree Random Forest XGBoost Adaboost XAI Model Internet of things Machine Learning Cognitive learning theory (Deep learning) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 33-34). The internet of things is one of today’s most revolutionary technologies. Because of its pervasiveness, increasing network connection capacity, and diversity of linked items, the internet of things (IoT) is adaptable and versatile. The most common problem impeding IoT growth is insufficient security measures. The threat of data breaches is always there since smart gadgets gather and transmit sensitive informa tion that, if disclosed, might have severe consequences. Modern advances in Artificial Intelligence are providing new Machine Learning and Deep Learning approaches to address more complex issues with greater model performance. This predictive capac ity, however, comes at the cost of growing complexity, which can make these models hard to understand and interpret. Though these models give highly precise results, an explanation is required in order to comprehend and accept the model’s decisions. Here comes XAI which emphasizes a variety of ways for breaking the black-box nature of Machine Learning and Deep Learning models as well as delivering human level explanations.In this article, to identify and classify IoT network attacks, we have analyzed six machine learning and deep learning approaches: Decision Tree, Random Forest, AdaBoost, XGBoost, ANN, and MLP. Accuracy, Precision, Recall, F1-Score, and Confusion Matrix are some of the metrics we have used to evaluate our models. We have achieved fairly impressive results (above 96%) in binary clas sification for all the techniques. When all of the classifiers were analyzed, Decision Tree and Random Forest outperformed all others (above 99%) for both binary and multiclass classification. Adaboost and ANN, on the other hand, perform badly for multiclass classification. We have also applied Undersampling, Oversampling, and SMOTE techniques on a dataset to reduce data skewness and to evaluate multiple ML and DL algorithms.We have used LIME, SHAP, and ELI5 approaches to inter pret and explain our models. The feasibility of the techniques suggested in this work is demonstrated in the IoT/IIoT dataset of TON_IoT datasets, which incorporate data obtained from telemetry datasets of IoT and IIoT sensors. Anika Tasnim Nigah Hossain Sabrina Tabassum Nazia Parvin B. Computer Science and Engineering 2022-12-13T04:47:28Z 2022-12-13T04:47:28Z 2022 2022-05 Thesis ID: 18301047 ID: 18101204 ID: 18301135 ID: 18301140 http://hdl.handle.net/10361/17642 en_US 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. 34 Pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	en_US
topic	Machine Learning Prediction Decision tree Random Forest XGBoost Adaboost XAI Model Internet of things Machine Learning Cognitive learning theory (Deep learning)
spellingShingle	Machine Learning Prediction Decision tree Random Forest XGBoost Adaboost XAI Model Internet of things Machine Learning Cognitive learning theory (Deep learning) Tasnim, Anika Hossain, Nigah Tabassum, Sabrina Parvin, Nazia Classification and Explanation of Different Internet of Things (IoT) Network Attacks using Machine Learning, Deep Learning and XAI
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
author2	Hossain, Dr. Muhammad Iqbal
author_facet	Hossain, Dr. Muhammad Iqbal Tasnim, Anika Hossain, Nigah Tabassum, Sabrina Parvin, Nazia
format	Thesis
author	Tasnim, Anika Hossain, Nigah Tabassum, Sabrina Parvin, Nazia
author_sort	Tasnim, Anika
title	Classification and Explanation of Different Internet of Things (IoT) Network Attacks using Machine Learning, Deep Learning and XAI
title_short	Classification and Explanation of Different Internet of Things (IoT) Network Attacks using Machine Learning, Deep Learning and XAI
title_full	Classification and Explanation of Different Internet of Things (IoT) Network Attacks using Machine Learning, Deep Learning and XAI
title_fullStr	Classification and Explanation of Different Internet of Things (IoT) Network Attacks using Machine Learning, Deep Learning and XAI
title_full_unstemmed	Classification and Explanation of Different Internet of Things (IoT) Network Attacks using Machine Learning, Deep Learning and XAI
title_sort	classification and explanation of different internet of things (iot) network attacks using machine learning, deep learning and xai
publisher	Brac University
publishDate	2022
url	http://hdl.handle.net/10361/17642
work_keys_str_mv	AT tasnimanika classificationandexplanationofdifferentinternetofthingsiotnetworkattacksusingmachinelearningdeeplearningandxai AT hossainnigah classificationandexplanationofdifferentinternetofthingsiotnetworkattacksusingmachinelearningdeeplearningandxai AT tabassumsabrina classificationandexplanationofdifferentinternetofthingsiotnetworkattacksusingmachinelearningdeeplearningandxai AT parvinnazia classificationandexplanationofdifferentinternetofthingsiotnetworkattacksusingmachinelearningdeeplearningandxai
_version_	1814309057293451264

Classification and Explanation of Different Internet of Things (IoT) Network Attacks using Machine Learning, Deep Learning and XAI

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