Show simple item record

dc.contributor.authorSahingoz, Ozgur Koray
dc.contributor.authorÇebi, Cem Berke
dc.contributor.authorBulut, Fatma Sena
dc.contributor.authorFırat, Hazal
dc.contributor.authorKaratas, Gözde
dc.date.accessioned2020-01-03T11:16:00Z
dc.date.available2020-01-03T11:16:00Z
dc.date.issued2020-01
dc.identifier.urihttps://hdl.handle.net/11413/5976
dc.description.abstractIn recent years, there is a growing trend of internetization which is a relatively new word for our global economy that aims to connect each market sectors (or even devices) by using the global network architecture as the Internet. Although this connectivity enables great opportunities in the marketplace, it results in many security vulnerabilities for admins of the computer networks. Firewalls and Antivirus systems are preferred as the first line of a defense mechanism; they are not sufficient to protect the systems from all type of attacks. Intrusion Detection Systems (IDSs), which can train themselves and improve their knowledge base, can be used as an extra line of the defense mechanism of the network. Due to its dynamic structure, IDSs are one of the most preferred solution models to protect the networks against attacks. Traditionally, standard machine learning methods are preferred for training the system. However, in recent years, there is a growing trend to transfer these standard machine learning-based systems to the deep learning models. Therefore, in this paper, IDSs with four different deep learning models are proposed, and their performance is compared. The experimental results showed that proposed models result in very high and acceptable accuracy rates with KDD Cup 99 Dataset.
dc.language.isoen_UStr_TR
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectCyber Security
dc.subjectIntrusion Detection Systems
dc.subjectDeep Learning
dc.subjectBiRNN
dc.subjectBİLSTM
dc.subjectCNN-LSTM
dc.subjectGRU
dc.subjectKDDCup99
dc.titleDeep learning based security management of information systems: A comparative study
dc.typeconferenceObjecttr_TR
dc.contributor.authorID214903tr_TR
dc.relation.journal6th International Conference on Information Management and Industrial Engineering (ICII 2020)tr_TR
dc.identifier.scopus2-s2.0-85087916948


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivs 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States