Anomaly Detection in Cyber Security with Graph-Based LSTM in Log Analysis

Basit Öğe Kaydı
dc.contributor.authorAlaca, Y.
dc.contributor.authorÇelik, Y.
dc.contributor.authorGoel, S.
dc.date.accessioned2024-09-29T16:16:06Z
dc.date.available2024-09-29T16:16:06Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractIntrusion detection systems utilize the analysis of log data to effectively detect anomalies. However, detecting anomalies quickly and effectively in large and heterogeneous log data can be challenging. To address this difficulty, this study proposes the GLSTM (Graph-based Long Short-Term Memory) framework, a graph-based deep learning model that analyzes log data to detect cyber-attacks rapidly and effectively. The framework involves standardizing the complex and diverse log data, training this data on an artificial intelligence model, and detecting anomalies. Initially, the complex and diverse log data is transformed into graph data using Node2Vec, enabling efficient and rapid analysis on the artificial intelligence model. Subsequently, these graph data are trained using LSTM (Long Short-Term Memory), Bi-LSTM, and GRU(Gated Recurrent Unit) deep learning algorithms. The proposed framework is tested using Hadoop’s HDFS dataset, collected from different systems and heterogeneous sources, as well as the BGL and IMDB datasets. Experimental results on the selected datasets demonstrate high levels of success. © 2023 Akif AKGUL. All rights reserveden_US
dc.identifier.doi10.51537/chaos.1348302
dc.identifier.endpage197en_US
dc.identifier.issn2687-4539
dc.identifier.scopus2-s2.0-85176349924en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage188en_US
dc.identifier.trdizinid1210303en_US
dc.identifier.urihttps://doi.org/10.51537/chaos.1348302
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1210303
dc.identifier.urihttps://hdl.handle.net/20.500.14619/8859
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherAkif AKGULen_US
dc.relation.ispartofChaos Theory and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAnomaly detectionen_US
dc.subjectCyber securityen_US
dc.subjectDeep learningen_US
dc.subjectGraphen_US
dc.subjectHDFSen_US
dc.subjectNode2Vecen_US
dc.titleAnomaly Detection in Cyber Security with Graph-Based LSTM in Log Analysisen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu
TR-Dizin İndeksli Yayınlar Koleksiyonu