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Intrusion Detection Using Big Data and Deep Learning Techniques

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dc.contributor.author Faker, Osama
dc.contributor.author Doğdu, Erdoğan
dc.date.accessioned 2020-02-28T12:18:23Z
dc.date.available 2020-02-28T12:18:23Z
dc.date.issued 2019
dc.identifier.citation Faker, Osama; Dogdu, Erdogan, "Intrusion Detection Using Big Data and Deep Learning Techniques", Proceedings of the 2019 Annual ACM Southeast Conference (ACMSE 2019), pp. 86-93, (2019). tr_TR
dc.identifier.uri http://hdl.handle.net/20.500.12416/2564
dc.description.abstract In this paper, Big Data and Deep Learning Techniques are integrated to improve the performance of intrusion detection systems. Three classifiers are used to classify network traffic datasets, and these are Deep Feed-Forward Neural Network (DNN) and two ensemble techniques, Random Forest and Gradient Boosting Tree (GBT). To select the most relevant attributes from the datasets, we use a homogeneity metric to evaluate features. Two recently published datasets UNSW NB15 and CICIDS2017 are used to evaluate the proposed method. 5-fold cross validation is used in this work to evaluate the machine learning models. We implemented the method using the distributed computing environment Apache Spark, integrated with Keras Deep Learning Library to implement the deep learning technique while the ensemble techniques are implemented using Apache Spark Machine Learning Library. The results show a high accuracy with DNN for binary and multiclass classification on UNSW NB15 dataset with accuracies at 99.16% for binary classification and 97.01% for multiclass classification. While GBT classifier achieved the best accuracy for binary classification with the CICIDS2017 dataset at 99.99%, for multiclass classification DNN has the highest accuracy with 99.56%. tr_TR
dc.language.iso eng tr_TR
dc.publisher Assoc Computing Machinery tr_TR
dc.relation.isversionof 10.1145/3299815.3314439 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Intrusion Detection System tr_TR
dc.subject Big Data tr_TR
dc.subject Machine Learning tr_TR
dc.subject Artificial Neural Networks tr_TR
dc.subject Deep Learning tr_TR
dc.subject Ensemble Techniques tr_TR
dc.subject Feature Selection tr_TR
dc.title Intrusion Detection Using Big Data and Deep Learning Techniques tr_TR
dc.type conferenceObject tr_TR
dc.relation.journal Proceedings of the 2019 Annual ACM Southeast Conference (ACMSE 2019) tr_TR
dc.identifier.startpage 86 tr_TR
dc.identifier.endpage 93 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü tr_TR


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