Data reduction and real time processing applied to intrusion detection
Keywords:
data reduction, feature selection, instance selection, intrusion detectionAbstract
Introduction: Intrusion detection is a crucial task for identifying malicious activities in computer systems. However, the datasets used to train classifiers are often large, which can impact the efficiency of the process. Therefore, it is necessary to reduce the size of these datasets without compromising the effectiveness of the classifiers.
Objective: To present a hybrid algorithm that efficiently reduces the dataset used in intrusion detection without significantly affecting classifier performance.
Methods: The proposed algorithm combines two approaches: attribute selection and instance selection. It is applied sequentially to achieve optimal data reduction without significantly impacting effectiveness during classification.
Results: The proposed algorithm outperforms state-of-the-art algorithms in terms of efficiency and effectiveness. Furthermore, its application in intrusion detection scenarios has a significant impact, accelerating the detection process without compromising result quality.
Conclusions: It is provided a practical and effective solution for intrusion detection, especially in real-time data processing environments.
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