Intrusion Detection System using Apache Spark Analytic System
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2019, Vol 21, Issue 1
Abstract
In this study, an Intrusion Detection System (IDS) is proposed based on the use of machine learning and distributed computing. The proposed system uses classification techniques that are implemented in the built-in machine learning library in Apache Spark distributed computing framework. As the use of distributed computing allows the proposed method to provide rapid predictions for the packets flowing in the network, two classifiers are cascaded in order to combine their decisions for more accurate decisions. The Multi-Layer Perceptron (MLP) classifier is used as a binary classifier, where the output of this classifier only indicates whether the packet is a normal or attack packet. Packets predicted to be normal by this classifier are allowed through the network. However, packets predicted as attacks are classified again using the random forest classifier, which provides the state of the packet and the type of the attack as its output. If the packet is classified as a normal packet, it is also allowed to the network, otherwise it is filtered out. The results show that he proposed methodology has been able to improve the performance of IDS to 99.12%, which outperforms the state-of-the-art systems in the literature.
Authors and Affiliations
Asst. Prof. Dr. Sefer KURNAZ
Keywords
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- EP ID EP441069
- DOI 10.9790/0661-2101013237.
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