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Implementing a Deep Learning Model for Intrusion Detection on Apache Spark Platform
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DOI：10.1109/ACCESS.2020.3019931.png)
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Abstract
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Internet evolution produced a connected world with a massive amount of data. This connectivity advantage came with the price of more complex and advanced attacks. Intrusion Detection System (IDS) is an essential component for security in modern networks. The IDS methodology is either signature-based detection or anomaly behavior detection. Recently, researchers adopted Deep Learning (DL) because it has a better performance than traditional machine learning algorithms. The use of DL to produce a model for the IDS may take a long time because of computation complexity and a large number of hyperparameters. Different DL models for IDS on Apache Spark have been implemented in this article. This article uses the famous Network Security Lab - Knowledge Discovery and Data Mining (NSL-KDD) dataset and presents a computation delay comparison between Apache Spark and regular implementation. Moreover, an enhanced model is used to improve attack detection accuracy.
Keywords:
Training
Machine learning
Sparks
Computational modeling
Intrusion detection
Support vector machines
Intrusion detection
bigdata
Hadoop
apache spark
deep learning
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