2020
DOI: 10.1109/access.2020.3019931
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Implementing a Deep Learning Model for Intrusion Detection on Apache Spark Platform

Abstract: 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 I… Show more

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Cited by 46 publications
(23 citation statements)
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“…So it is an overall accuracy. The algorithms used on the NSL-KDD dataset by other models include MLP [6], DNN [7], CNN [9], Deep-MLP [37], STL-IDS [38] and DIS-IDS [39]. Figure 14 shows that the accuracy of the 5-classification of most models is approximately 80% on the KDD test set.…”
Section: The Results Of Multi-target Anomaly Classificationmentioning
confidence: 99%
“…So it is an overall accuracy. The algorithms used on the NSL-KDD dataset by other models include MLP [6], DNN [7], CNN [9], Deep-MLP [37], STL-IDS [38] and DIS-IDS [39]. Figure 14 shows that the accuracy of the 5-classification of most models is approximately 80% on the KDD test set.…”
Section: The Results Of Multi-target Anomaly Classificationmentioning
confidence: 99%
“…Choosing the right metric is essential during the models' evaluation because different metrics are proposed to evaluate different problems and application models [51]. Several measurements are appropriate for a classification model, but the most commonly applied one is the confusion matrix [52], [53]. A confusion matrix is a statistical measurement used in machine learning classification algorithms performance for finding the accuracy of the model.…”
Section: Evaluation Performance Appropriate Metricsmentioning
confidence: 99%
“…It provides a series of high-level components, including Spark streaming for real-time computing, Spark SQL for structured data processing, GraphX for graph computing, and MLlib for machine learning [ 3 ]. These components are applied by application developers to various fields, such as feature extraction [ 4 ], intrusion detection [ 5 ], and community discovery [ 6 ], and maintain good performance.…”
Section: Introductionmentioning
confidence: 99%