690 research outputs found
Improving intrusion detection systems using data mining techniques
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
Intrusion Detection System by Fuzzy Interpolation
Network intrusion detection systems identify malicious connections and thus help protect networks from attacks. Various data-driven approaches have been used in the development of network intrusion detection systems, which usually lead to either very complex systems or poor generalization ability due to the complexity of this challenge. This paper proposes a data-driven network intrusion detection system using fuzzy interpolation in an effort to address the aforementioned limitations. In particular, the developed system equipped with a sparse rule base not only guarantees the online performance of intrusion detection, but also allows the generation of security alerts from situations which are not directly covered by the existing knowledge base. The proposed system has been applied to a well-known data set for system validation and evaluation with competitive results generated
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