Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.689485
Title: Improving intrusion detection systems using data mining techniques
Author: Almutairi, Abdulrazaq Z.
ISNI:       0000 0004 5919 1569
Awarding Body: Loughborough University
Current Institution: Loughborough University
Date of Award: 2016
Availability of Full Text:
Access through EThOS:
Access through Institution:
Abstract:
Recent surveys and studies have shown that cyber-attacks have caused a lot of damage to organisations, governments, and individuals around the world. Although developments are constantly occurring in the computer security field, cyber-attacks still cause damage as they are developed and evolved by hackers. This research looked at some industrial challenges in the intrusion detection area. The research identified two main challenges; the first one is that signature-based intrusion detection systems such as SNORT lack the capability of detecting attacks with new signatures without human intervention. The other challenge is related to multi-stage attack detection, it has been found that signature-based is not efficient in this area. The novelty in this research is presented through developing methodologies tackling the mentioned challenges. The first challenge was handled by developing a multi-layer classification methodology. The first layer is based on decision tree, while the second layer is a hybrid module that uses two data mining techniques; neural network, and fuzzy logic. The second layer will try to detect new attacks in case the first one fails to detect. This system detects attacks with new signatures, and then updates the SNORT signature holder automatically, without any human intervention. The obtained results have shown that a high detection rate has been obtained with attacks having new signatures. However, it has been found that the false positive rate needs to be lowered. The second challenge was approached by evaluating IP information using fuzzy logic. This approach looks at the identity of participants in the traffic, rather than the sequence and contents of the traffic. The results have shown that this approach can help in predicting attacks at very early stages in some scenarios. However, it has been found that combining this approach with a different approach that looks at the sequence and contents of the traffic, such as event- correlation, will achieve a better performance than each approach individually.
Supervisor: Not available Sponsor: Kuwait Embassy
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.689485  DOI: Not available
Share: