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Title
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تحليل Big Data براي رديابي حملات شبكه
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Type
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Presentation
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Keywords
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Big data, Detection, HDFS, MapReduce, NSL-KDD, KNN, SVM, LDA
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Abstract
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One of the main challenges associated with analysis of big data is automatic detection systems that classify network
traffic data. The aim of this paper is to consider design and implementation of intrusion detection systems (IDS) using
several classification algorithms for big data analysis. Big data analysis techniques can extract information from a variety
of sources to detect future unknown attacks. We use classification algorithms with MapReduce framework for mining IDS
in Apache HTTP server on a Linux system. So that, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and
Linear Discriminant Analyses (LDA) classifiers are implied on NSL-KDD Dataset and compared them with some wellknown
existing techniques for IDS. The results show that the average efficiency is high. The Minimum efficiency reporting
value is 95% and maximum 97% by changing the parameters in the proposed model.
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Researchers
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gholamreza Ahmadi (Second researcher)
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