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

      , , ,

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

      Cyber Security Research

      10th-12th September 2019

      Deep Learning, intrusion detection, Cyber Security, machine learning

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          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.

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          Most cited references 28

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

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          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.
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                Author and article information

                September 2019
                September 2019
                : 126-136
                Department of Computer Science

                Guelma University, Algeria
                School of Computer Science and Informatics

                De Montfort University, UK
                © Mohamed Amine Ferrag et al. Published by BCS Learning and Development Ltd. 6th International Symposium for ICS & SCADA Cyber Security Research 2019

                This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit

                6th International Symposium for ICS & SCADA Cyber Security Research 2019
                Athens, Greece
                10th-12th September 2019
                Electronic Workshops in Computing (eWiC)
                Cyber Security Research
                Product Information: 1477-9358BCS Learning & Development
                Self URI (journal page):
                Electronic Workshops in Computing


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