An Intrusion Detection System Based on Convolutional Neural Network for Imbalanced Network Traffic

Zhang, Xiaoxuan; Ran, Jing; Mi, Jize

doi:10.1109/iccsnt47585.2019.8962490

Cited by 52 publications

(25 citation statements)

References 13 publications

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“…Apart from using a single under-sampling or over-sampling method, two resampling methods can be combined. For example, SMOTE-ENN ( Zhang et al, 2019 ), ENN is an under-sampling method focusing on eliminating noise samples, which is added to the pipeline after SMOTE to obtain cleaner combined samples. For each combined sample, its nearest-neighbors are computed according to the Euclidean distance.…”

Section: Methodsmentioning

confidence: 99%

“…In over-sampling methods, the synthetic minority over-sampling technique (SMOTE) ( Demidova and Klyueva, 2017 ) can add new minority class examples, but the deleted information of majority samples may contain representative information of the majority class. Then, the improved SMOTE which combines with edited nearest neighbors (SMOTE-ENN) algorithm ( Zhang et al, 2019 ), is used in the K-nearest neighbor (KNN) method to classify the sampled dataset, by the theory of over-sampling and under-sampling.…”

Section: Introductionmentioning

confidence: 99%

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Identification of Orphan Genes in Unbalanced Datasets Based on Ensemble Learning

et al. 2020

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Orphan genes are associated with regulatory patterns, but experimental methods for identifying orphan genes are both time-consuming and expensive. Designing an accurate and robust classification model to detect orphan and non-orphan genes in unbalanced distribution datasets poses a particularly huge challenge. Synthetic minority over-sampling algorithms (SMOTE) are selected in a preliminary step to deal with unbalanced gene datasets. To identify orphan genes in balanced and unbalanced Arabidopsis thaliana gene datasets, SMOTE algorithms were then combined with traditional and advanced ensemble classified algorithms respectively, using Support Vector Machine, Random Forest (RF), AdaBoost (adaptive boosting), GBDT (gradient boosting decision tree), and XGBoost (extreme gradient boosting). After comparing the performance of these ensemble models, SMOTE algorithms with XGBoost achieved an F1 score of 0.94 with the balanced A. thaliana gene datasets, but a lower score with the unbalanced datasets. The proposed ensemble method combines different balanced data algorithms including Borderline SMOTE (BSMOTE), Adaptive Synthetic Sampling (ADSYN), SMOTE-Tomek, and SMOTE-ENN with the XGBoost model separately. The performances of the SMOTE-ENN-XGBoost model, which combined over-sampling and under-sampling algorithms with XGBoost, achieved higher predictive accuracy than the other balanced algorithms with XGBoost models. Thus, SMOTE-ENN-XGBoost provides a theoretical basis for developing evaluation criteria for identifying orphan genes in unbalanced and biological datasets.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of Orphan Genes in Unbalanced Datasets Based on Ensemble Learning

et al. 2020

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“…The evaluated dataset was NSL-KDD dataset. IDS based on CNN was proposed in paper [21]. To balance the network traffic, before the training of CNN, an algorithm called synthetic minority oversampling technique with edited nearest neighbours (SMOTE-ENN) was applied on the NSL-KDD dataset.…”

Section: Related Workmentioning

confidence: 99%

Hybridized Wrapper Filter Using Deep Neural Network for Intrusion Detection

Venkateswaran¹,

Umadevi²

2022

Computer Systems Science and Engineering

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Huge data over the cloud computing and big data are processed over the network. The data may be stored, send, altered and communicated over the network between the source and destination. Once data send by source to destination, before reaching the destination data may be attacked by any intruders over the network. The network has numerous routers and devices to connect to internet. Intruders may attack any were in the network and breaks the original data, secrets. Detection of attack in the network became interesting task for many researchers. There are many intrusion detection feature selection algorithm has been suggested which lags on performance and accuracy. In our article we propose new IDS feature selection algorithm with higher accuracy and performance in detecting the intruders. The combination of wrapper filtering method using Pearson correlation with recursion function is used to eliminate the unwanted features. This feature extraction process clearly extracts the attacked data. Then the deep neural network is used for detecting intruders attack over the data in the network. This hybrid machine learning algorithm in feature extraction process helps to find attacked information using recursive function. Performance of proposed method is compared with existing solution. The traditional feature selection in IDS such as differential equation (DE), Gain ratio (GR), symmetrical uncertainty (SU) and artificial bee colony (ABC) has less accuracy than proposed PCRFE. The experimented results are shown that our proposed PCRFE-CDNN gives 99% of accuracy in IDS feature selection process and 98% in sensitivity.

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“…The hidden layers of a CNN consist of complex layers that convolve with multiplication or other product. The input CNN requires numeric [109]. Furthermore, CNN is used to extract dealing with more complex features to perform the task with better accuracy [110].…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Deep Learning Approaches for Intrusion Detection

Salih

Ameen

Zeebaree

et al. 2021

AJRCoS

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Recently, computer networks faced a big challenge, which is that various malicious attacks are growing daily. Intrusion detection is one of the leading research problems in network and computer security. This paper investigates and presents Deep Learning (DL) techniques for improving the Intrusion Detection System (IDS). Moreover, it provides a detailed comparison with evaluating performance, deep learning algorithms for detecting attacks, feature learning, and datasets used to identify the advantages of employing in enhancing network intrusion detection.

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An Intrusion Detection System Based on Convolutional Neural Network for Imbalanced Network Traffic

Cited by 52 publications

References 13 publications

Identification of Orphan Genes in Unbalanced Datasets Based on Ensemble Learning

Identification of Orphan Genes in Unbalanced Datasets Based on Ensemble Learning

Hybridized Wrapper Filter Using Deep Neural Network for Intrusion Detection

Deep Learning Approaches for Intrusion Detection

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