Deep learning methods in network intrusion detection: A survey and an objective comparison

Gamage, Sunanda; Samarabandu, Jagath

doi:10.1016/j.jnca.2020.102767

Cited by 227 publications

(110 citation statements)

References 29 publications

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“…A number of other modern network intrusion detection data sets are publicly available now, such as CIC-IDS2017 48 and CIC-IDS2018. 49 Thus, more studies, such as the work of Gamage and Samarabandu, 50 are required to investigate the performance of deep learning models and baseline models on a wider range of data sets.…”

Section: Cnn For Anomaly-based Network Intrusion Detectionmentioning

confidence: 99%

A Convolutional Neural Network for Improved Anomaly-Based Network Intrusion Detection

Al-Turaiki

Altwaijry

2021

Big Data

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Cybersecurity protects and recovers computer systems and networks from cyber attacks. The importance of cybersecurity is growing commensurately with people's increasing reliance on technology. An anomaly detection-based network intrusion detection system is essential to any security framework within a computer network. In this article, we propose two models based on deep learning to address the binary and multiclass classification of network attacks. We use a convolutional neural network architecture for our models. In addition, a hybrid two-step preprocessing approach is proposed to generate meaningful features. The proposed approach combines dimensionality reduction and feature engineering using deep feature synthesis. The performance of our models is evaluated using two benchmark data sets, namely the network security laboratory-knowledge discovery in databases data set and the University of New South Wales Network Based 2015 data set. The performance is compared with similar deep learning approaches in the literature, as well as state-of-the-art classification models. Experimental results show that our models achieve good performance in terms of accuracy and recall, outperforming similar models in the literature.

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Section: Cnn For Anomaly-based Network Intrusion Detectionmentioning

confidence: 99%

A Convolutional Neural Network for Improved Anomaly-Based Network Intrusion Detection

Al-Turaiki

Altwaijry

2021

Big Data

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“…The detection performance can be further improved by considering practical context. A survey on the application of deep learning in IDS can refer to Reference [37].…”

Section: Related Workmentioning

confidence: 99%

Enhancing intrusion detection with feature selection and neural network

2021

Int J Intell Syst

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Intrusion detection systems are widely implemented to protect computer networks from threats. To identify unknown attacks, many machine learning algorithms like neural networks have been explored for anomaly based detection. However, in real-world applications, the performance of classifiers might be fluctuant with different data sets, while one main reason is due to some redundant or ineffective features. To mitigate this issue, this study investigates some feature selection methods and introduces an ensemble of Neural Networks and Random Forest to improve the detection performance. In particular, we design an intelligent system that can choose an appropriate algorithm in an adaptive way. In the evaluation, we study the feasibility of our approach with KDD99 data set and evaluate its practical performance with a real data set collected from a Honeynet environment. The experimental results indicate that as compared with similar approaches, our approach can overall provide a better result, through identifying important and closely related features.

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“…Some papers [47] used outdated datasets to evaluate the IDS system using machine learning such as KDD Cup 1999, NSL-KDD, and ISCX2012. These datasets are obsolete, with a huge number of redundant occurrences compared to the rapid development of new types of network technologies and introduction of newer cybersecurity attacks.…”

Section: Related Work For Cse-cic-ids2018 Datasetmentioning

confidence: 99%

Machine-Learning-Enabled Intrusion Detection System for Cellular Connected UAV Networks

et al. 2021

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The recent development and adoption of unmanned aerial vehicles (UAVs) is due to its wide variety of applications in public and private sector from parcel delivery to wildlife conservation. The integration of UAVs, 5G, and satellite technologies has prompted telecommunication networks to evolve to provide higher-quality and more stable service to remote areas. However, security concerns with UAVs are growing as UAV nodes are becoming attractive targets for cyberattacks due to enormously growing volumes and poor and weak inbuilt security. In this paper, we propose a UAV- and satellite-based 5G-network security model that can harness machine learning to effectively detect of vulnerabilities and cyberattacks. The solution is divided into two main parts: the model creation for intrusion detection using various machine learning (ML) algorithms and the implementation of ML-based model into terrestrial or satellite gateways. The system identifies various attack types using realistic CSE-CIC IDS-2018 network datasets published by Canadian Establishment for Cybersecurity (CIC). It consists of seven different types of new and contemporary attack types. This paper demonstrates that ML algorithms can be used to classify benign or malicious packets in UAV networks to enhance security. Finally, the tested ML algorithms are compared for effectiveness in terms of accuracy rate, precision, recall, F1-score, and false-negative rate. The decision tree algorithm performed well by obtaining a maximum accuracy rate of 99.99% and a minimum false negative rate of 0% in detecting various attacks as compared to all other types of ML classifiers.

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Deep learning methods in network intrusion detection: A survey and an objective comparison

Cited by 227 publications

References 29 publications

A Convolutional Neural Network for Improved Anomaly-Based Network Intrusion Detection

A Convolutional Neural Network for Improved Anomaly-Based Network Intrusion Detection

Enhancing intrusion detection with feature selection and neural network

Machine-Learning-Enabled Intrusion Detection System for Cellular Connected UAV Networks

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