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.

Highlights

  • The major target of cyber attacks is a country’s Critical National Infrastructure (CNI) such as ports, hospitals, water, gas or electricity producers, which use and rely upon Supervisory Control and Data Acquisitions(SCADA) and Industrial Control Systems (ICS) to manage their production

  • According to Dewa and Maglaras (2016), data mining and its core feature which is knowledge discovery can significantly help in creating Data mining based intrusion detection systems (IDS) that can achieve higher accuracy to novel types of intrusion and demonstrate more robust behaviour compared to traditional IDSs

  • We present all datasets used by the deep learning techniques papers applied to cyber security intrusion detection

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Summary

INTRODUCTION

The major target of cyber attacks is a country’s Critical National Infrastructure (CNI) such as ports, hospitals, water, gas or electricity producers, which use and rely upon Supervisory Control and Data Acquisitions(SCADA) and Industrial Control Systems (ICS) to manage their production. Except from regulations and policies that may be used to tackle cyber attacks to CNIs specific practical measures need to be taken in order for these regulations to be effective Maglaras et al (2018). We review the deep learning techniques papers applied to cyber security intrusion detection. We present all datasets used by the deep learning techniques papers applied to cyber security intrusion detection.

A REVIEW OF INTRUSION DETECTION SYSTEMS BASED ON DEEP LEARNING TECHNIQUES
PUBLIC DATASETS
DEEP LEARNING APPROACHES
Deep discriminative models
Performance metrics
Results
CONCLUSION

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