A convolutional neural network based model with improved activation function and optimizer for effective intrusion detection and classification
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020.
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Brac University
2021
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| Acceso en liña: | http://hdl.handle.net/10361/14733 |
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10361-14733 |
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10361-147332022-01-26T10:18:12Z A convolutional neural network based model with improved activation function and optimizer for effective intrusion detection and classification Kabir, Solaiman Sakib, Sadman Hossain, Md. Akib Islam, Safi Hossain, Muhammad Iqbal Department of Computer Science and Engineering, Brac University Intrusion Detection System (IDS) Multiclass classi fication CNN DNN Machine learning. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020. Cataloged from PDF version of thesis. Includes bibliographical references (pages 37-39). In today's world, technological advancements have entangled our nancial, social and many more other aspects of lives to the internet or some network. Moreover, with the development of IoT technologies, it has spread over to our transportation, home-appliances and more devices. It is also a security risk because all of our sensitive and private knowledge on the Internet is exposed to a growing amount of cyber-attacks. An Intrusion Detection System can identify a cyber-attack while it is ongoing or prior to it. We are conscious of the evolving Machine Learning and Deep Learning developments, the most sophisticated multi-functional methods created by humans that can be utilized to overcome this issue. Alongside identi fication, precise classi cation of intrusion is of considerable signi ficance for the administrator to take decisive actions. In this study, we have used the dataset CIC-IDS-2018 that is the biggest and most recent labeled dataset of intrusions. This dataset comprises of six varieties of attacks. Our thesis proposes a CNN Model with mish activation function and Ranger optimizer. The model reaches an accuracy of 0.989 that is the highest in multiclass classification with this dataset. Solaiman Kabir Sadman Sakib Md. Akib Hossain Safi Islam B. Computer Science 2021-07-05T09:13:51Z 2021-07-05T09:13:51Z 2020 2020-04 Thesis ID 16301042 ID 16101124 ID 16301028 ID 16341006 http://hdl.handle.net/10361/14733 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. 39 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Intrusion Detection System (IDS) Multiclass classi fication CNN DNN Machine learning. |
| spellingShingle |
Intrusion Detection System (IDS) Multiclass classi fication CNN DNN Machine learning. Kabir, Solaiman Sakib, Sadman Hossain, Md. Akib Islam, Safi A convolutional neural network based model with improved activation function and optimizer for effective intrusion detection and classification |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020. |
| author2 |
Hossain, Muhammad Iqbal |
| author_facet |
Hossain, Muhammad Iqbal Kabir, Solaiman Sakib, Sadman Hossain, Md. Akib Islam, Safi |
| format |
Thesis |
| author |
Kabir, Solaiman Sakib, Sadman Hossain, Md. Akib Islam, Safi |
| author_sort |
Kabir, Solaiman |
| title |
A convolutional neural network based model with improved activation function and optimizer for effective intrusion detection and classification |
| title_short |
A convolutional neural network based model with improved activation function and optimizer for effective intrusion detection and classification |
| title_full |
A convolutional neural network based model with improved activation function and optimizer for effective intrusion detection and classification |
| title_fullStr |
A convolutional neural network based model with improved activation function and optimizer for effective intrusion detection and classification |
| title_full_unstemmed |
A convolutional neural network based model with improved activation function and optimizer for effective intrusion detection and classification |
| title_sort |
convolutional neural network based model with improved activation function and optimizer for effective intrusion detection and classification |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/14733 |
| work_keys_str_mv |
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1814308616392409088 |