86
views
0
recommends
+1 Recommend
1 collections
    1
    shares
      scite_
       
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Case Study on the Application of Deep Learning to Network Intruder Detection

      research-article
      Bookmark

            Abstract

            Deep learning has seen considerable success in several application sectors. Unfortunately, little research has been done on its efficacy in the context of network intrusion detection. This article includes case studies that use deep learning to identify network anomalies both supervised and unsupervised. It has been demonstrated that deep neural networks (DNNs) outperform current machine learning-based intrusion detection systems in the presence of shifting IP addresses. We also demonstrate how auto encoders can support network anomaly detection.

            Content

            Author and article information

            Journal
            10.54878/EJPSS
            Emirati Journal of Policing and Security Studies
            EJPSS
            Emirates Scholar
            03 August 2023
            : 2
            : 1
            : 4-12
            Affiliations
            [1 ]University of Technology and Applied Sciences Salalah, Oman
            Author notes
            Correspondence: Chithik Raja Mohamed ( chithik43@ 123456gmail.com )
            Article
            10.54878/EJPSS.298
            9f8dbd83-b465-4d7e-b53f-8e83be8b9e7b
            ©2023Emirates Scholar

            This is an open access article published by Emirates Scholar and distributed under the Creative Commons Attribution License 4.0 (CC BY).

            History

            Social policy & Welfare,International & Comparative law,Criminology,Information systems & theory,Penology & Police science
            Deep Neural Network, threshold, Host-based intruder, Deep Learning, Anomaly Detection, Intruder Detection System, Auto encoder

            Comments

            Excellent research 

            2023-08-04 15:40 UTC
            +1

            Comment on this article