Security related self-protected networks: Autonomous threat detection and response (ATDR)
| dc.contributor.advisor | Bagula, Bigomokero | |
| dc.contributor.author | Havenga, Wessel Johannes Jacobus | |
| dc.date.accessioned | 2022-03-02T12:29:40Z | |
| dc.date.accessioned | 2024-10-30T14:01:00Z | |
| dc.date.available | 2022-03-02T12:29:40Z | |
| dc.date.available | 2024-10-30T14:01:00Z | |
| dc.date.issued | 2021 | |
| dc.description | >Magister Scientiae - MSc | en_US |
| dc.description.abstract | Cybersecurity defense tools, techniques and methodologies are constantly faced with increasing challenges including the evolution of highly intelligent and powerful new-generation threats. The main challenges posed by these modern digital multi-vector attacks is their ability to adapt with machine learning. Research shows that many existing defense systems fail to provide adequate protection against these latest threats. Hence, there is an ever-growing need for self-learning technologies that can autonomously adjust according to the behaviour and patterns of the offensive actors and systems. The accuracy and effectiveness of existing methods are dependent on decision making and manual input by human experts. This dependence causes 1) administration overhead, 2) variable and potentially limited accuracy and 3) delayed response time. | en_US |
| dc.identifier.uri | https://hdl.handle.net/10566/16992 | |
| dc.language.iso | en | en_US |
| dc.publisher | University of Western Cape | en_US |
| dc.rights.holder | University of Western Cape | en_US |
| dc.subject | Denial of service attacks | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | Neural networking | en_US |
| dc.subject | Self-protected networks | en_US |
| dc.subject | Anomaly detection | en_US |
| dc.subject | Cybersecurity | en_US |
| dc.title | Security related self-protected networks: Autonomous threat detection and response (ATDR) | en_US |