A new distributed anomaly detection approach for log IDS management based on deep learning

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dc.authorid0000-0002-6048-7645en_US
dc.authorid0000-0002-1846-6090en_US
dc.authorid0000-0001-8166-1211en_US
dc.authorid0000-0002-0233-064Xen_US
dc.contributor.authorKoca, Murat
dc.contributor.authorAydın, Muhammed Ali
dc.contributor.authorSertbaş, Ahmet
dc.contributor.authorZaim, Abdül Halim
dc.date.accessioned2021-11-09T11:03:32Z
dc.date.available2021-11-09T11:03:32Z
dc.date.issued2021en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractAbstract: Today, with the rapid increase of data, the security of big data has become more important than ever for managers. However, traditional infrastructure systems cannot cope with increasingly big data that is created like an avalanche. In addition, as the existing database systems increase licensing costs per transaction, organizations using information technologies are shifting to free and open source solutions. For this reason, we propose an anomaly attack detection model on Apache Hadoop distributed file system (HDFS), which stands out in open source big data analytics, and Apache Spark, which stands out with its speed performance in analysis to reduce the costs of organizations. This model consists of four stages: the first of which is to store instant data on HDFS in a distributed manner. In the second stage, the log data generated in the network traffic are analyzed by taking the data on Apache Spark and including the log data created by HDFS. In the third stage, the data preprocessing stage and with the CUDA parallel programming in the TensorFlow library, we apply our deep learning (cuDNN) method to the distributed anomaly detection with the computational support of GPUs. In the last stage, the generated alarms are recorded on HDFS again. We conducted comparative experiments with the approach we propose to detect cyberattack anomalies in log data management with the classification methods used in machine learning. The results obtained in these experiments appear to provide a promising gain in performance evaluation metrics compared to the other available methods.en_US
dc.identifier.doi10.3906/elk-2102-89en_US
dc.identifier.endpage2501en_US
dc.identifier.issue29en_US
dc.identifier.scopus2-s2.0-85117066882en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage2486en_US
dc.identifier.trdizinid524516en_US
dc.identifier.urihttps://hdl.handle.net/11467/5087
dc.identifier.urihttps://doi.org/10.3906/elk-2102-89
dc.identifier.wosWOS:000703608400002en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherTübitaken_US
dc.relation.ispartofTurkish Journal of Electrical Engineering & Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBig Dataen_US
dc.subjectDeep Learningen_US
dc.subjectCyber Securityen_US
dc.subjectLog IDSen_US
dc.subjectSparken_US
dc.subjectCUDAen_US
dc.titleA new distributed anomaly detection approach for log IDS management based on deep learningen_US
dc.typeArticleen_US

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