Exploring the intersection of machine learning and explainable artificial intelligence: An analysis and validation of ML models through XAI for intrusion detection
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
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| Langue: | English |
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Brac University
2023
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| Accès en ligne: | http://hdl.handle.net/10361/19351 |
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10361-19351 |
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10361-193512023-08-08T21:02:01Z Exploring the intersection of machine learning and explainable artificial intelligence: An analysis and validation of ML models through XAI for intrusion detection Rahman, Masroor Navid, Reshad Karim Hossain Bhuyain, Md Muballigh Hasan, Farnaz Fawad Nup, Naima Ahmed Hossain, Dr. Muhammad Iqbal Department of Computer Science and Engineering, Brac University Machine learning Explainable Artificial Intelligence (XAI) ToN-IoT Windows OS Data analysis Intrusion detection Neural networks (Computer science) Artificial intelligence 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 41-43). The use of machine learning models has greatly enhanced the capability to rec ognize patterns and draw conclusions. However, due to their black-box nature, it can be difficult to comprehend the factors that affect their decisions. XAI methods offer transparency into these models and aid in enhancing comprehension, exami nation, and trust in their outcomes. In this paper, we present a study on the use of machine learning (ML) models for intrusion detection in Windows 10 Operating systems using the ToN-IoT dataset. We investigate the performance of different ML models including tree-based models such as Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and K-Nearest Neighbors (KNN) in detecting these attacks. Furthermore, we use Explainable Artificial Intelligence (XAI) techniques to understand how the attacks influence the processes in the Windows 10 systems and how they can be identified and prevented. Our study highlights the importance of using XAI techniques to make ML models more interpretable and trustworthy in high-stakes applications such as intrusion detection. We believe that this work can contribute to the development of more robust and secure operating systems. Masroor Rahman Reshad Karim Navid Md Muballigh Hossain Bhuyain Farnaz Fawad Hasan Naima Ahmed Nup B. Computer Science and Engineering 2023-08-08T05:20:49Z 2023-08-08T05:20:49Z 2023 2023-01 Thesis ID: 19101213 ID: 19101225 ID: 19101289 ID: 19101579 ID: 19101430 http://hdl.handle.net/10361/19351 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. 43 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Machine learning Explainable Artificial Intelligence (XAI) ToN-IoT Windows OS Data analysis Intrusion detection Neural networks (Computer science) Artificial intelligence Machine learning |
| spellingShingle |
Machine learning Explainable Artificial Intelligence (XAI) ToN-IoT Windows OS Data analysis Intrusion detection Neural networks (Computer science) Artificial intelligence Machine learning Rahman, Masroor Navid, Reshad Karim Hossain Bhuyain, Md Muballigh Hasan, Farnaz Fawad Nup, Naima Ahmed Exploring the intersection of machine learning and explainable artificial intelligence: An analysis and validation of ML models through XAI for intrusion detection |
| 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 |
Hossain, Dr. Muhammad Iqbal |
| author_facet |
Hossain, Dr. Muhammad Iqbal Rahman, Masroor Navid, Reshad Karim Hossain Bhuyain, Md Muballigh Hasan, Farnaz Fawad Nup, Naima Ahmed |
| format |
Thesis |
| author |
Rahman, Masroor Navid, Reshad Karim Hossain Bhuyain, Md Muballigh Hasan, Farnaz Fawad Nup, Naima Ahmed |
| author_sort |
Rahman, Masroor |
| title |
Exploring the intersection of machine learning and explainable artificial intelligence: An analysis and validation of ML models through XAI for intrusion detection |
| title_short |
Exploring the intersection of machine learning and explainable artificial intelligence: An analysis and validation of ML models through XAI for intrusion detection |
| title_full |
Exploring the intersection of machine learning and explainable artificial intelligence: An analysis and validation of ML models through XAI for intrusion detection |
| title_fullStr |
Exploring the intersection of machine learning and explainable artificial intelligence: An analysis and validation of ML models through XAI for intrusion detection |
| title_full_unstemmed |
Exploring the intersection of machine learning and explainable artificial intelligence: An analysis and validation of ML models through XAI for intrusion detection |
| title_sort |
exploring the intersection of machine learning and explainable artificial intelligence: an analysis and validation of ml models through xai for intrusion detection |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/19351 |
| work_keys_str_mv |
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