Effective network intrusion detection via representation learning: A Denoising AutoEncoder approach

Lopes, Ivandro Ortet; Zou, Deqing; Abdulqadder, Ihsan H.; Ruambo, Francis Aidan; Yuan, Bin; Jin, Hai

doi:10.1016/j.comcom.2022.07.027

Cited by 23 publications

(5 citation statements)

References 32 publications

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“…An anomaly detection model can be used to detect a fraudulent transaction or any highly imbalanced supervised tasks (Chandola et al 2009). AEs can be used in supervised (Alsadhan 2023), unsupervised (Lopes et al 2022), and semi-supervised (Akcay et al 2018;Ruff et al 2019) anomaly detection tasks.…”

Section: Anomaly Detectionmentioning

confidence: 99%

“…If the reconstruction error is above the threshold, the input data is classified as anomalous . This approach combines the feature learning capabilities of AEs with the discriminative power of supervised classifiers, enhancing the accuracy of anomaly detection in real-world applications, including fraud detection (Alsadhan 2023;Debener et al 2023;Fanai and Abbasimehr 2023), network security (Ghorbani and Fakhrahmad 2022;Lopes et al 2022), and fault detection Ying et al 2023) in industrial processes.…”

Section: Anomaly Detectionmentioning

confidence: 99%

“…Now, if a sample data of another target class is passed through the AE network, it results in comparatively larger reconstruction loss, a threshold value of reconstruction loss (anomaly score) can be decided, larger than that can be considered an anomaly (Sakurada and Yairi 2014). This inherent ability to capture complex data representations without labeled anomalies makes AEs effective in detecting anomalies, whether in cyber-security for identifying network intrusions (Lopes et al 2022;An et al 2022;Lewandowski and Paffenroth 2022), in manufacturing for spotting defects (Papananias et al 2023;Sudo et al 2021), or in finance for fraud detection (Du et al 2022;Jiang et al 2023;Kennedy et al 2023). The versatility of AEs and their capacity to adapt to diverse data types contribute to their widespread use in unsupervised anomaly detection scenarios, enhancing system security and reliability.…”

Section: Anomaly Detectionmentioning

confidence: 99%

See 2 more Smart Citations

Autoencoders and their applications in machine learning: a survey

Berahmand,

Daneshfar,

Salehi

et al. 2024

Artif Intell Rev

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Autoencoders have become a hot researched topic in unsupervised learning due to their ability to learn data features and act as a dimensionality reduction method. With rapid evolution of autoencoder methods, there has yet to be a complete study that provides a full autoencoders roadmap for both stimulating technical improvements and orienting research newbies to autoencoders. In this paper, we present a comprehensive survey of autoencoders, starting with an explanation of the principle of conventional autoencoder and their primary development process. We then provide a taxonomy of autoencoders based on their structures and principles and thoroughly analyze and discuss the related models. Furthermore, we review the applications of autoencoders in various fields, including machine vision, natural language processing, complex network, recommender system, speech process, anomaly detection, and others. Lastly, we summarize the limitations of current autoencoder algorithms and discuss the future directions of the field.

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Section: Anomaly Detectionmentioning

confidence: 99%

Section: Anomaly Detectionmentioning

confidence: 99%

Section: Anomaly Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Autoencoders and their applications in machine learning: a survey

Berahmand,

Daneshfar,

Salehi

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

“…Papers [26] utilized a multilayer stacked denoising autoencoder (DAE) model in malicious software classification and can handle the case of 0-day or unknown attacks. Lopes et al [27] implemented a DAE to compress the feature dimensionality, and then used a deep neural network (DNN) classifier for intrusion detection on the CICIDS2018 dataset, achieving above 99.6% on many metrics. An et al [28] utilized a variational autoencoder (VAE) model to reduce the noise in the network traffic information, enhancing the performance of network traffic classification.…”

Section: A Automatic Feature Extractionmentioning

confidence: 99%

Research on Dung Beetle Optimization Based Stacked Sparse Autoencoder for Network Situation Element Extraction

Yang,

Zhao

2024

IEEE Access

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Network security situation awareness enables networks to actively and effectively defend against network attacks, relying on the extraction of network situation elements as an initial and decisive step. In existing studies, the stacked sparse autoencoder (SSAE) has been employed to extract features from unlabeled network flows. However, obtaining the optimal hyperparameter combination is challenging due to its numerous hyperparameters. To address this issue, we propose a novel approach named DBO-SSAE that leverages dung beetle optimization (DBO) to select the optimal hyperparameters for SSAE automatically. Applied to the well-known UNSW-NB15 dataset, our model yields an optimal feature subset, which is evaluated across various binary classifiers with different metrics. Experimental results demonstrate that our approach improves accuracy and F1-measure by 0.2% to 1.5%, while reducing the false negative rate (FNR) and false positive rate (FPR) by 0.06% to 7%, surpassing other feature extraction methods on the same classifier for the UNSW-NB15 dataset. Particularly, in conjunction with a lightweight bidirectional long short-term memory (BiLSTM), our model achieves metrics of 98.84% accuracy, 98.96% F1-measure, 1.86% FNR, and 0.6% FPR. This study could provide novel insights into the effective representation of network situation elements and lay the groundwork for a high-efficiency intrusion detection system.

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“…Moreover, the lowdimensional data extracted by AE is prone to overfitting, which means that it cannot effectively improve the multi-classification accuracy of the detection model. The Denoising Autoencoder (DAE) [43,44] is an improved version of AE. By injecting noise at the input end of the autoencoder, it can enhance the generalization effect of the data after dimensionality reduction and to some extent increase the robustness of the detection model.…”

Section: Feature Dimension Reduction Module Based On Mdsaementioning

confidence: 99%

Machine Learning-Based Intrusion Detection for Rare-Class Network Attacks

Yang,

Gu,

Yan

2023

Electronics

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Due to the severe imbalance in the quantities of normal samples and attack samples, as well as among different types of attack samples, intrusion detection systems suffer from low detection rates for rare-class attack data. In this paper, we propose a geometric synthetic minority oversampling technique based on the optimized kernel density estimation algorithm. This method can generate diverse rare-class attack data by learning the distribution of rare-class attack data while maintaining similarity with the original sample features. Meanwhile, the balanced data is input to a feature extraction module built upon multiple denoising autoencoders, reducing information redundancy in high-dimensional data and improving the detection performance for unknown attacks. Subsequently, a soft-voting ensemble learning technique is utilized for multi-class anomaly detection on the balanced and dimensionally reduced data. Finally, an intrusion detection system is constructed based on data preprocessing, imbalance handling, feature extraction, and anomaly detection modules. The performance of the system was evaluated using two datasets, NSL-KDD and N-BaIoT, achieving 86.39% and 99.94% multiclassification accuracy, respectively. Through ablation experiments and comparison with the baseline model, it is found that the inherent limitations of a single machine-learning model directly affect the accuracy of the intrusion detection system, while the superiority of the proposed multi-module model in detecting unknown attacks and rare classes of attack traffic is demonstrated.

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Effective network intrusion detection via representation learning: A Denoising AutoEncoder approach

Cited by 23 publications

References 32 publications

Autoencoders and their applications in machine learning: a survey

Autoencoders and their applications in machine learning: a survey

Research on Dung Beetle Optimization Based Stacked Sparse Autoencoder for Network Situation Element Extraction

Machine Learning-Based Intrusion Detection for Rare-Class Network Attacks

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