Classification of Attack Types for Intrusion Detection Systems Using a Machine Learning Algorithm

Park, Kinam; Song, Youngrok; Cheong, Yun-Gyung

doi:10.1109/bigdataservice.2018.00050

Cited by 65 publications

(30 citation statements)

References 2 publications

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“…Researchers have also implemented random forest classifier on the IDS dataset sample. Such approaches have helped in the experimentation of datasets and analysing effects of malicious attacks considering various perspectives and dimensions [8].…”

Section: Related Workmentioning

confidence: 99%

A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU

et al. 2020

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The enormous popularity of the internet across all spheres of human life has introduced various risks of malicious attacks in the network. The activities performed over the network could be effortlessly proliferated, which has led to the emergence of intrusion detection systems. The patterns of the attacks are also dynamic, which necessitates efficient classification and prediction of cyber attacks. In this paper we propose a hybrid principal component analysis (PCA)-firefly based machine learning model to classify intrusion detection system (IDS) datasets. The dataset used in the study is collected from Kaggle. The model first performs One-Hot encoding for the transformation of the IDS datasets. The hybrid PCA-firefly algorithm is then used for dimensionality reduction. The XGBoost algorithm is implemented on the reduced dataset for classification. A comprehensive evaluation of the model is conducted with the state of the art machine learning approaches to justify the superiority of our proposed approach. The experimental results confirm the fact that the proposed model performs better than the existing machine learning models.

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Section: Related Workmentioning

confidence: 99%

A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU

et al. 2020

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“…Random forest (RF) [28], a leading machine learning algorithm, was used to check the performance of NIDS after solving sparse class problems. The means of learning several models in machine learning to predict better values than a single model using the predictions of those models is called ensemble learning, and a prime example is RF.…”

Section: Classificationmentioning

confidence: 99%

AE-CGAN Model based High Performance Network Intrusion Detection System

Lee

Park

2019

Applied Sciences

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In this paper, a high-performance network intrusion detection system based on deep learning is proposed for situations in which there are significant imbalances between normal and abnormal traffic. Based on the unsupervised learning models autoencoder (AE) and the generative adversarial networks (GAN) model during deep learning, the study aim is to solve the imbalance of data and intrusion detection of high performance. The AE-CGAN (autoencoder-conditional GAN) model is proposed to improve the performance of intrusion detection. This model oversamples rare classes based on the GAN model in order to solve the performance degradation caused by data imbalance after processing the characteristics of the data to a lower level using the autoencoder model. To measure the performance of the AE-CGAN model, data is classified using random forest (RF), a typical machine learning classification algorithm. In this experiment, we used the canadian institute for cybersecurity intrusion detection system (CICIDS)2017 dataset, the latest public dataset of network intrusion detection system (NIDS), and compared the three models to confirm efficacy of the proposed model. We compared the performance of three types of models. These included single-RF, a classification model using only a classification algorithm, AE-RF which is processed by classifying data features, and the AE-CGAN model which is classified after solving the data feature processing and data imbalance. Experimental results showed that the performance of the AE-CGAN model proposed in this paper was the highest. In particular, when the data were unbalanced, the performances of recall and F1 score, which are more accurate performance indicators, were 93.29% and 95.38%, respectively. The AE-CGAN model showed much better performance.

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“…Park et al [Park et al 2018] evaluated the performance of detecting different types of attacks on Kyoto 2006+ dataset. They performed a selection of features manually, based on the recommendation of other works.…”

Section: Related Workmentioning

confidence: 99%

“…Machine learning approaches have been susscessfully employed in the design of network intrusion detection systems as presented in the works of Park et al [Park et al 2018], Hodo et al [Hodo et al 2018], Shafaraldin et al [Sharafaldin et al 2018], Ultimura and Costa [Utimura and Costa 2018], and Biswa [Biswa 2018].…”

Section: Machine Learning On Network Intrusion Detectionmentioning

confidence: 99%

Network Intrusion Detection Systems Design: A Machine Learning Approach

Neto

Gomes

2019

Anais Do XXXVII Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos (SBRC 2019)

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With the increasing popularization of computer network-based technologies, security has become a daily concern, and intrusion detection systems (IDS) play an essential role in the supervision of computer networks. An employed approach to combat network intrusions is the development of intrusion detection systems via machine learning techniques. The intrusion detection performance of these systems depends highly on the quality of the IDS dataset used in their design and the decision making for the most suitable machine learning algorithm becomes a difficult task. The proposed paper focuses on evaluate and accurate the model of intrusion detection system of different machine learning algorithms on two resampling techniques using the new CICIDS2017 dataset where Decision Trees, MLPs, and Random Forests on Stratified 10-Fold gives high stability in results with Precision, Recall, and F1-Scores of 98% and 99% with low execution times.

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Classification of Attack Types for Intrusion Detection Systems Using a Machine Learning Algorithm

Cited by 65 publications

References 2 publications

A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU

A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU

AE-CGAN Model based High Performance Network Intrusion Detection System

Network Intrusion Detection Systems Design: A Machine Learning Approach

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