Demystifying machine learning models for IOT attack detection with explainable AI

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.

Sonraí bibleagrafaíochta
Príomhchruthaitheoirí:	Muna, Rabeya Khatun, Maliha, Homaira Tasnim, Hasan, Mahedi
Rannpháirtithe:	Hossain, Muhammad Iqbal
Formáid:	Tráchtas
Teanga:	English
Foilsithe / Cruthaithe:	Brac University 2021
Ábhair:	Xg-boost Explainable AI XAI Lime Shap ELI5 IoT attack SMOTE PCA Machine learning Internet of things
Rochtain ar líne:	http://hdl.handle.net/10361/15553

id	10361-15553
record_format	dspace
spelling	10361-155532022-01-26T10:21:49Z Demystifying machine learning models for IOT attack detection with explainable AI Muna, Rabeya Khatun Maliha, Homaira Tasnim Hasan, Mahedi Hossain, Muhammad Iqbal Alam, Md. Golam Rabiul Department of Computer Science and Engineering, Brac University Xg-boost Explainable AI XAI Lime Shap ELI5 IoT attack SMOTE PCA Machine learning Internet of things This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 43-44). Internet of things (IoT) dramatically is changing our lives with its newly invented devices and applications which leads to various emerging cybersecurity challenges or threats. The rapid growth of IoT arouses security and privacy issues that need more attention to ensure the safety of human personal data, saving from serious damages. Over the year, several techniques have been conducted to establish IoT attack detection model so that it can detect attacks e ciently. Unfortunately, it is di cult to identify a good model that can detect both binary and multi-type attacks with accurately. The prediction result of models provides very little knowledge to the users or experts how the model classify attacks for detection which can not be understood through a simple output. Thus, it is getting necessary to understand the reasons behind the prediction to make people trust on the model by providing the insight view of the model. In the paper, we have introduced Explainable Arti cial Intelligent on our proposed model for making the model faithful enough and human understandable, by explaining the strategy of the detection model for predicting the attacks and representing the features or properties in uence of respective prediction. For this, we have establish an IoT attack detection model by using Xg-boost classi er on a dataset, name, IoT Intrusion Dataset[11], that supports both binary and multi-class classi cation to classify the attacks for detection. We have also used Explainable AI tools, named, Shap, Lime, and ELI5 to validate the performance of the model through analyzing the property of the established model by representing each feature's contribution and action of the model, for each prediction to give a clear idea how e cient the model is, for detecting the IoT attacks. Rabeya Khatun Muna Homaira Tasnim Maliha Mahedi Hasan B. Computer Science 2021-10-26T06:57:50Z 2021-10-26T06:57:50Z 2021 2021-09 Thesis ID 21341056 ID 18101455 ID 17101544 http://hdl.handle.net/10361/15553 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. 44 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Xg-boost Explainable AI XAI Lime Shap ELI5 IoT attack SMOTE PCA Machine learning Internet of things
spellingShingle	Xg-boost Explainable AI XAI Lime Shap ELI5 IoT attack SMOTE PCA Machine learning Internet of things Muna, Rabeya Khatun Maliha, Homaira Tasnim Hasan, Mahedi Demystifying machine learning models for IOT attack detection with explainable AI
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
author2	Hossain, Muhammad Iqbal
author_facet	Hossain, Muhammad Iqbal Muna, Rabeya Khatun Maliha, Homaira Tasnim Hasan, Mahedi
format	Thesis
author	Muna, Rabeya Khatun Maliha, Homaira Tasnim Hasan, Mahedi
author_sort	Muna, Rabeya Khatun
title	Demystifying machine learning models for IOT attack detection with explainable AI
title_short	Demystifying machine learning models for IOT attack detection with explainable AI
title_full	Demystifying machine learning models for IOT attack detection with explainable AI
title_fullStr	Demystifying machine learning models for IOT attack detection with explainable AI
title_full_unstemmed	Demystifying machine learning models for IOT attack detection with explainable AI
title_sort	demystifying machine learning models for iot attack detection with explainable ai
publisher	Brac University
publishDate	2021
url	http://hdl.handle.net/10361/15553
work_keys_str_mv	AT munarabeyakhatun demystifyingmachinelearningmodelsforiotattackdetectionwithexplainableai AT malihahomairatasnim demystifyingmachinelearningmodelsforiotattackdetectionwithexplainableai AT hasanmahedi demystifyingmachinelearningmodelsforiotattackdetectionwithexplainableai
_version_	1814309499588050944

Demystifying machine learning models for IOT attack detection with explainable AI

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