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Diwe, Loveth Ukamaka (2023) Detection of FTP and SSH Bruteforce Attacks using Deep Belief Network Model. Masters thesis, Dublin, National College of Ireland.
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Abstract
Cyber-attacks on computers and internet have been on a major increase since every organization has to communicate and run business operations on the network. The security awareness and protections implored by individuals and organizations daily is still not enough to fully eradicate the constant attacks. Bruteforce attack is a major high-level attack on the network connected via protocols like FTP, SSH or via the WEB. Brute Force attacks use methods like credential stuffing, DNS spoofing, multiple trials, and different consistent methods to access the targeted system or network. This attack type can be unpredictable and is often a gateway to many other attacks once successful hence, the need for a robust detection method arises. In this research, I investigated the application of Deep Belief Network (DBN) to detect Bruteforce attacks using the CSE-CICIDS2018 dataset. For additional performance comparison, we also trained three other algorithms: Decision Tree, Random Forest, and Logistic Regression. Experiment results showed that DBN model achieved a higher accuracy score of 99.3% while Random Forest had accuracy of 98.2%, Decision Tree 85.8% and Logistic Regression 99.2%.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Khan, Imran UNSPECIFIED |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Algebra > Algorithms > Computer algorithms Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security |
| Divisions: | School of Computing > Master of Science in Cyber Security |
| Depositing User: | Tamara Malone |
| Date Deposited: | 22 Oct 2024 10:53 |
| Last Modified: | 22 Oct 2024 10:53 |
| URI: | https://norma.ncirl.ie/id/eprint/7117 |
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