Blog
About
Search
Advanced search

2,491
views
28
references
 Top references
0
cited by
 Most cited
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
1 collections
    8
    shares
      316
      similar
       All similar
      • Record: found
      • Abstract: found
      • Conference Proceedings: found
      Is Open Access

      Deep Learning Techniques for Cyber Security Intrusion Detection : A Detailed Analysis

      Author(s): , , ,

      Publication date (Print):

      Conference name: 6th International Symposium for ICS & SCADA Cyber Security Research 2019 (ICS-CSR)

      Conference theme: Cyber Security Research

      Conference date: 10th-12th September 2019

      Keywords: Deep Learning, intrusion detection, Cyber Security, machine learning

      Read this article at

      ScienceOpen
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          In this study, we present a detailed analysis of deep learning techniques for intrusion detection. Specifically, we analyze seven deep learning models, including, deep neural networks, recurrent neural networks, convolutional neural networks, restricted Boltzmann machine, deep belief networks, deep Boltzmann machines, and deep autoencoders. For each deep learning model, we study the performance of the model in binary classification and multiclass classification. We use the CSE-CIC-IDS 2018 dataset and TensorFlow system as the benchmark dataset and software library in intrusion detection experiments. In addition, we use the most important performance indicators, namely, accuracy, detection rate, and false alarm rate for evaluating the efficiency of several methods.

          Related collections

          Most cited references 28

          • Record: found
          • Abstract: found
          • Article: not found

          Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication.

           H Jaeger,  H HAAS (2004)
          We present a method for learning nonlinear systems, echo state networks (ESNs). ESNs employ artificial recurrent neural networks in a way that has recently been proposed independently as a learning mechanism in biological brains. The learning method is computationally efficient and easy to use. On a benchmark task of predicting a chaotic time series, accuracy is improved by a factor of 2400 over previous techniques. The potential for engineering applications is illustrated by equalizing a communication channel, where the signal error rate is improved by two orders of magnitude.
          Article 0 comments Cited 368 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Recent advances in convolutional neural networks

             Jianfei Cai,  Bing Shuai,  Ting Ting Liu … (2018)
            Article 0 comments Cited 143 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              A survey of deep neural network architectures and their applications

               Weibo Liu,  Fuad Alsaadi,  Yurong Liu … (2017)
              Article 0 comments Cited 127 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Contributors
                Mohamed Amine Ferrag
                Leandros Maglaras
                Helge Janicke
                Richard Smith
                Conference
                Publication date: September 2019
                Publication date (Print): September 2019
                Pages: 126-136
                Affiliations
                Department of Computer Science

                Guelma University, Algeria
                School of Computer Science and Informatics

                De Montfort University, UK
                Article
                DOI: 10.14236/ewic/icscsr19.16
                Copyright statement: © Mohamed Amine Ferrag et al. Published by BCS Learning and Development Ltd. 6th International Symposium for ICS & SCADA Cyber Security Research 2019
                License:

                This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                Conference name: 6th International Symposium for ICS & SCADA Cyber Security Research 2019
                Conference acronym: ICS-CSR
                Conference number: 6
                Conference location: Athens, Greece
                Conference date: 10th-12th September 2019
                Conference sponsor: Electronic Workshops in Computing (eWiC)
                Conference theme: Cyber Security Research
                Product
                Product Information: 1477-9358BCS Learning & Development
                Self URI (journal page): https://ewic.bcs.org/
                Categories
                Series title: Electronic Workshops in Computing

                Data availability:

                ScienceOpen disciplines: Applied computer science, Computer science, Security & Cryptology, Graphics & Multimedia design, General computer science, Human-computer-interaction

                Keywords: intrusion detection, Deep Learning, machine learning, Cyber Security

                Comments

                Comment on this article