Ahmed, Abdulghani Ali (2019) Botnet Detection Using a Feed-Forward Backpropagation Artificial Neural Network. In: Computational Intelligence in Information Systems: Proceedings of the Computational Intelligence in Information Systems Conference (CIIS 2018) , 16-18 November 2018 , Brunei. pp. 24-35., 888. ISBN 978-3-030-03302-6
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Abstract
Botnet represent a critical threat to computer networks because their behavior allows hackers to take control of many computers simultaneously. Botnets take over the device of their victim and performs malicious activities on its system. Although many solutions have been developed to address the detection of Botnet in real time, these solutions are still prone to several problems that may critically affect the efficiency and capability of identifying and preventing Botnet attacks. The current work proposes a technique to detect Botnet attacks using a feed-forward backpropagation artificial neural network. The proposed technique aims to detect Botnet zero-day attack in real time. This technique applies a backpropagation algorithm to the CTU-13 dataset to train and evaluate the Botnet detection classifier. It is implemented and tested in various neural network designs with different hidden layers. Results demonstrate that the proposed technique is promising in terms of accuracy and efficiency of Botnet detection.
| Item Type: | Conference or Workshop Item (Lecture) |
|---|---|
| Additional Information: | Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 888) |
| Uncontrolled Keywords: | Botnet; Feed-forward; Artificial Neural Network; Backpropagation |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Faculty/Division: | Faculty of Computer System And Software Engineering |
| Depositing User: | Noorul Farina Arifin |
| Date Deposited: | 10 May 2021 03:23 |
| Last Modified: | 10 May 2021 03:23 |
| URI: | http://umpir.ump.edu.my/id/eprint/22501 |
| Download Statistic: | View Download Statistics |
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