Enhanced classification of network traffic data captured by intrusion prevention systems
PhD thesis
Aljoufi, R. 2023. Enhanced classification of network traffic data captured by intrusion prevention systems. PhD thesis Middlesex University Computer Science
Type | PhD thesis |
---|---|
Title | Enhanced classification of network traffic data captured by intrusion prevention systems |
Authors | Aljoufi, R. |
Abstract | A common practice in modern computer networks is the deployment of Intrusion Prevention Systems (IPSs) for the purpose of identifying security threats. Such systems provide alerts on suspicious activities based on a predefined set of rules. These alerts almost always contain high percentages of false positives and false negatives, which may impede the efficacy of their use. Therefore, with the presence of high numbers of false positives and false negatives, the analysis of network traffic data can be ineffective for decision makers which normally require concise, and preferably, visual forms to base their decisions upon. Machine learning techniques can help extract useful information from large datasets. Combined with visualisation, classification could provide a solution to false alerts and text-based outputs of IPSs. |
Sustainable Development Goals | 9 Industry, innovation and infrastructure |
Middlesex University Theme | Creativity, Culture & Enterprise |
Department name | Computer Science |
Institution name | Middlesex University |
Publisher | Middlesex University Research Repository |
Publication dates | |
05 Apr 2023 | |
Publication process dates | |
Deposited | 05 Apr 2023 |
Accepted | 06 Mar 2023 |
Output status | Published |
Accepted author manuscript | |
Language | English |
https://repository.mdx.ac.uk/item/8q563
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