DGM: a data generative model to improve minority class presence in anomaly detection domain

Dlamini, Gcinizwe; Fahim, Muhammad

doi:10.1007/s00521-021-05993-w

Cited by 37 publications

(12 citation statements)

References 18 publications

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“…In particular, all the experimented models were observed to have relatively low detection rates for the DoS class, even in the LSTM-based model, which is suitable for detecting temporally correlated attacks. Regarding these results, we infer that the domain space between classes is heavily overlapping [34], resulting in low detection rates for some classes.…”

Section: Experiments On the Unsw-nb15 Datasetmentioning

confidence: 89%

“…Thereafter, along with the development of various GAN models, studies have been conducted to apply appropriate GAN models for specific purposes. Li et al [32] and Lee et al [33] utilized the Wasserstein divergence-based GAN model to generate the synthetic data, and Dlamini et al [34] proposed a conditional GAN-based anomaly detection model to improve the classification performance in the minority classes. By focusing on specific industrial environments, Li et al [35] and Alabugin et al [36] proposed LSTM-GAN and bidirectional GAN-based anomaly detection models, respectively.…”

Section: Related Workmentioning

confidence: 99%

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An Enhanced AI-Based Network Intrusion Detection System Using Generative Adversarial Networks

Park

Lee

Kim

et al. 2023

IEEE Internet Things J.

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As communication technology advances, various and heterogeneous data are communicated in distributed environments through network systems. Meanwhile, along with the development of communication technology, the attack surface has expanded, and concerns regarding network security have increased. Accordingly, to deal with potential threats, research on Network Intrusion Detection Systems (NIDS) has been actively conducted. Among the various NIDS technologies, recently interest is focused on artificial intelligence(AI)-based anomaly detection systems, and various models have been proposed to improve the performance of NIDS. However, there still exists the problem of data imbalance, in which AI models cannot sufficiently learn malicious behavior and thus fail to detect network threats accurately. In this study, we propose a novel AI-based network intrusion detection system that can efficiently resolve the data imbalance problem and improve the performance of the previous systems. To address the aforementioned problem, we leveraged a state-of-the-art generative model that could generate plausible synthetic data for minor attack traffic. In particular, we focused on the reconstruction error and Wasserstein distancebased generative adversarial networks, and autoencoder-driven deep learning models. To demonstrate the effectiveness of our system, we performed comprehensive evaluations over various datasets and demonstrated that the proposed systems significantly outperformed the previous AI-based NIDS.

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Section: Experiments On the Unsw-nb15 Datasetmentioning

confidence: 89%

Section: Related Workmentioning

confidence: 99%

An Enhanced AI-Based Network Intrusion Detection System Using Generative Adversarial Networks

Park

Lee

Kim

et al. 2023

IEEE Internet Things J.

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“…While the model proposed by Koroniotis et al (2017) achieved 93.23% accuracy using DT classifier. In addition, none of the studies listed in Table 1 have resolved the class imbalance problem of the UNSW-NB15 dataset as there are many studies ( Al-Daweri et al, 2020 ; Ahmad et al, 2021 ; Bagui & Li, 2021 ; Dlamini & Fahim, 2021 ) that have highlighted this issue. We addressed the class imbalance problem by applying SMOTE that improved the performance of the classifiers and achieved good results.…”

Section: Discussionmentioning

confidence: 99%

Network intrusion detection using oversampling technique and machine learning algorithms

Ahmed¹,

Hameed²,

Bawany³

2022

PeerJ Computer Science

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The expeditious growth of the World Wide Web and the rampant flow of network traffic have resulted in a continuous increase of network security threats. Cyber attackers seek to exploit vulnerabilities in network architecture to steal valuable information or disrupt computer resources. Network Intrusion Detection System (NIDS) is used to effectively detect various attacks, thus providing timely protection to network resources from these attacks. To implement NIDS, a stream of supervised and unsupervised machine learning approaches is applied to detect irregularities in network traffic and to address network security issues. Such NIDSs are trained using various datasets that include attack traces. However, due to the advancement in modern-day attacks, these systems are unable to detect the emerging threats. Therefore, NIDS needs to be trained and developed with a modern comprehensive dataset which contains contemporary common and attack activities. This paper presents a framework in which different machine learning classification schemes are employed to detect various types of network attack categories. Five machine learning algorithms: Random Forest, Decision Tree, Logistic Regression, K-Nearest Neighbors and Artificial Neural Networks, are used for attack detection. This study uses a dataset published by the University of New South Wales (UNSW-NB15), a relatively new dataset that contains a large amount of network traffic data with nine categories of network attacks. The results show that the classification models achieved the highest accuracy of 89.29% by applying the Random Forest algorithm. Further improvement in the accuracy of classification models is observed when Synthetic Minority Oversampling Technique (SMOTE) is applied to address the class imbalance problem. After applying the SMOTE, the Random Forest classifier showed an accuracy of 95.1% with 24 selected features from the Principal Component Analysis method.

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“…Compared with NSL-KDD, UNSW-NB15 is a large but extremely imbalanced dataset, which contains more smaller minority classes. To verify the effectiveness of data augmentation of our DB-CGAN in imbalanced classification tasks, some classical and state of the art data augmentation methods, including SMOTE [18], ADASYN [19], DGM-SPOCU [27], ECGAN [26], and MENGNETO [28], are chosen for comparison evaluations, which are addressed as follows. the distribution when generating minority data, aiming to reduce the learning bias from the original imbalanced data and shift the classification decision boundary for those classes that are harder to learn.…”

Section: A Dataset and Experiments Designmentioning

confidence: 99%

Distribution Bias Aware Collaborative Generative Adversarial Network for Imbalanced Deep Learning in Industrial IoT

Zhou

et al. 2023

IEEE Trans. Ind. Inf.

108

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The impact of Internet of Things (IoT) has become increasingly significant in smart manufacturing, while Deep Generative Model (DGM) is viewed as a promising learning technique to work with large amount of continuously generated industrial big data in facilitating modern industrial applications. However, it is still challenging to handle the imbalanced data when using conventional Generative Adversarial Network (GAN) based learning strategies. In this study, we propose a Distribution Bias aware Collaborative Generative Adversarial Network (DB-CGAN) model for imbalanced deep learning in industrial IoT, especially to solve limitations caused by distribution bias issue between the generated data and original data, via a more robust data augmentation. An integrated data augmentation framework is constructed by introducing a complementary classifier into the basic GAN model. Specifically, a conditional generator with random labels is designed and trained adversarially with the classifier to effectively enhance augmentation of the number of data samples in minority classes, while a weight sharing scheme is newly designed between two separated feature extractors, enabling the collaborative adversarial training among generator, discriminator, and classifier. An augmentation algorithm is then developed for intelligent anomaly detection in imbalanced learning, which can significantly improve the classification accuracy based on the correction of distribution bias using the rebalanced data. Compared with five baseline methods, experiment evaluations based on two real-world imbalanced datasets demonstrate the outstanding performance of our proposed model in tackling the distribution bias issue for multi-class classification in imbalanced learning for industrial IoT applications.

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DGM: a data generative model to improve minority class presence in anomaly detection domain

Cited by 37 publications

References 18 publications

An Enhanced AI-Based Network Intrusion Detection System Using Generative Adversarial Networks

An Enhanced AI-Based Network Intrusion Detection System Using Generative Adversarial Networks

Network intrusion detection using oversampling technique and machine learning algorithms

Distribution Bias Aware Collaborative Generative Adversarial Network for Imbalanced Deep Learning in Industrial IoT

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