• Home
  • About
  • Policies
  • Contact
    • Türkçe
    • English
  • English 
    • Türkçe
    • English
  • Login
Advanced Search
View Item 
  •   Home
  • Fen Edebiyat Fakültesi / Faculty of Letters and Sciences
  • Matematik - Bilgisayar / Mathematics and Computer Science
  • Bildiriler, Kongreler ve Sempozyumlar / Declarations, Congresses and Symposiums
  • View Item
  •   Home
  • Fen Edebiyat Fakültesi / Faculty of Letters and Sciences
  • Matematik - Bilgisayar / Mathematics and Computer Science
  • Bildiriler, Kongreler ve Sempozyumlar / Declarations, Congresses and Symposiums
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Neural network based intrusion detection systems with different training functions

Thumbnail
Author
Karataş, Gözde
Şahingöz, Özgür Koray
Type
conferenceObject
Date
2018
Language
en_US
Metadata
Show full item record
Abstract
In the last decades, due to the improvements in networking techniques and the increased use of the Internet, the digital communications entered all of the activities in the global marketplace. Parallel to these enhancements the attempts of hackers for intruding the networks are also increased. They tried to make unauthorized access to the networks for making some modifications in their data or to increase the network traffic for making a denial of service attack. Although a firewall seems as a good tool for preventing this type of attacks, intrusion detection systems (IDSs) are also preferred especially for detecting the attack within the network system. In the last few years, the performance of the IDS is increased with the help of machine learning algorithms whose effects depend on the used training/learning algorithm. Mainly it is really hard to know which learning algorithm can be the fastest one according to the problem type. The algorithm selection depends on lots of factors such as the size of data sets, number of nodes network design, the targeted error rate, the complexity of the problem, etc. In this paper, it is aimed to compare different network training function in a multi-layered artificial neural network which is designed for constructing an effective intrusion detection system. The experimental results are depicted in the paper by explaining the efficiency of the algorithms according to their true-positive detection rates and speed of the execution.
Subject
network security
intrusion detection system
neural networks
training functions
Newton Method
Algorithm
URI
https://hdl.handle.net/11413/2310
Collections
  • Bildiriler, Kongreler ve Sempozyumlar / Declarations, Congresses and Symposiums [85]
  • Scopus Publications [724]
  • WoS Publications [1016]

İstanbul Kültür University

Hakkında |Politika | Kütüphane | İletişim | Send Feedback | Admin

Istanbul Kültür University, Ataköy Campus E5 Karayolu Üzeri Bakırköy 34158, İstanbul / TURKEY
Copyright © İstanbul Kültür University

Creative Commons Lisansı
IKU Institutional Repository, Creative Commons Alıntı-GayriTicari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.

Designed by  UNIREPOS

İKU Kütüphane


Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsTypeLanguageBy PublisherRightsPubmedScopusWoSThis CollectionBy Issue DateAuthorsTitlesSubjectsTypeLanguageBy PublisherRightsPubmedScopusWoS

My Account

Login

İstanbul Kültür University

Hakkında |Politika | Kütüphane | İletişim | Send Feedback | Admin

Istanbul Kültür University, Ataköy Campus E5 Karayolu Üzeri Bakırköy 34158, İstanbul / TURKEY
Copyright © İstanbul Kültür University

Creative Commons Lisansı
IKU Institutional Repository, Creative Commons Alıntı-GayriTicari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.

Designed by  UNIREPOS