Autoencoder-based network anomaly detection

Chen, Zhaomin; Yeo, Chai Kiat; Lee, Bu-Sung; Lau, Chiew Tong

doi:10.1109/wts.2018.8363930

Cited by 238 publications

(120 citation statements)

References 13 publications

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“…The structure of the self-supervised voltage sag source identification method based on CNN is shown in Figure 4. weights (or vectors) to solve the projection of input data on weights, and then to get coding [35,36]. The basic structure is shown in Figure 3.…”

Section: Self-supervised Voltage Sag Source Identification Methods Basmentioning

confidence: 99%

Self-Supervised Voltage Sag Source Identification Method Based on CNN

Mei

Zhang

et al. 2019

Energies

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A self-supervised voltage sag source identification method based on a convolution neural network is proposed in this study. In addition, a self-supervised CNN (Convolutional Neural Networks) voltage sag source identification model is constructed on the basis of the convolution neural network and AutoEncoder. The convolution layer and pool layer in CNN are used to extract the voltage sag characteristics, and the self-supervised network training process is realized based on the principle of AE. In the constructed mode, features which reflect the data characteristics are used rather than artificial features, thus improving the accuracy of practical application. It is unnecessary to input a lot of correct labels before the self-supervised training process. The model can meet the requirements of sag source identification on timeliness, practicability, diversity, and versatility in the context of modern big data. In this study, three-phase asymmetric sag sources in sag sources are classified into more detailed categories according to different fault phases. Therefore, the proposed method can not only identify the voltage sag source, but also accurately determine the specific fault phase. Finally, the optimal parameters of the model are recognized through a case study, and a self-supervised CNN model is established based on the data type of voltage sag. This model extracts features and identifies sag sources through the measured sag data. The superiority of the proposed method is verified by a comparison.

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Section: Self-supervised Voltage Sag Source Identification Methods Basmentioning

confidence: 99%

Self-Supervised Voltage Sag Source Identification Method Based on CNN

Mei

Zhang

et al. 2019

Energies

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“…Algorithms such as recurrent neural networks (RNN) and 1-D convolutional neural networks (CNNs), have been shown to provide state-of-the-art results on challenging activity recognition tasks with little or no data feature engineering, instea d using feature learning on raw data [21]. In deep learning algorithms the Auto Encoder (AE) is an unsupervised way of learning the features of the training data [22]. It is a type of a neural network whose prime function is to reconstruct the input data as an output.…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection and Classification of Physiological Signals in IoT- Based Healthcare Framework

Nawaz

Ahmed

2021

Preprint

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Physiological signals retrieve the information from sensors implanted or attached to the human body. These signals are vital data sources that can assist in predicting the disease well before time and thus proper treatment can be made possible. With the addition of Internet of Things in healthcare, real-time data collection and pre-processing for signal analysis has reduced burden of in-person appointments and decision making on healthcare. Recently, Deep learning-based algorithms have been implemented by researchers for recognition, realization and prediction of diseases by extracting and analyzing the important features. In this research real-time 1-D timeseries data of on-body non-invasive bio-medical sensors have been acquired and pre-processed and analyzed for anomaly detection. Feature engineered parameters of large and diverse dataset have been used to train the data to make the anomaly detection system more reliable. For comprehensive real-time monitoring the implemented system uses wavelet time scattering features for classification and deep learning based autoencoder for anomaly detection of time series signals for assisting the clinical diagnosis of cardiovascular and muscular activity. In this research, an implementation of IoT based healthcare system using bio-medical sensors has been presented. This paper also aims to provide the analysis of cloud data acquired through bio-medical sensors using signal analysis techniques for anomaly detection and timeseries classification has been done for the disease prognosis in real-time. Wavelet time scattering based signals classification accuracy of 99.88% is achieved. In real time signals anomaly detection, 98% accuracy is achieved. The average Mean Absolute Error loss of 0.0072 for normal signals and 0.078 is achieved for anomaly signals.

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“…[Sakurada and Yairi, 2014] show that the non-linearity of autoencoders allows for better detection of anomalies than linear PCA, whilst being computationally cheaper than kernel PCA. Autoencoder variants are also commonly used, including recurrent [Chauhan and Vig, 2015;Malhotra et al, 2015], convolutional [Chen et al, 2018], denoising [Feng and Han, 2015] and variational models [An, 2015]. They have also been used just for feature learning, with a separate prediction network, such as a Gaussian Mixture Model [Bo et al, 2018], using the learnt features to make predictions.…”

Section: Related Workmentioning

confidence: 99%

Robustness of Autoencoders for Anomaly Detection Under Adversarial Impact

Goodge

Hooi

et al. 2020

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

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Detecting anomalies is an important task in a wide variety of applications and domains. Deep learning methods have achieved state-of-the-art performance in anomaly detection in recent years; unsupervised methods being particularly popular. However, deep learning methods can be fragile to small perturbations in the input data. This can be exploited by an adversary to deliberately hinder model performance; an adversarial attack. This phenomena has been widely studied in the context of supervised image classification since its discovery, however such studies for an anomaly detection setting are sorely lacking. Moreover, the plethora of defense mechanisms that have been proposed are often not applicable to unsupervised anomaly detection models. In this work, we study the effect of adversarial attacks on the performance of anomaly-detecting autoencoders using real data from a Cyber physical system (CPS) testbed with intervals of controlled, physical attacks as anomalies. An adversary would attempt to disguise these points as normal through adversarial perturbations. To combat this, we propose the Approximate Projection Autoencoder (APAE), which incorporates two defenses against such attacks into a general autoencoder. One of these involves a novel technique to improve robustness under adversarial impact by optimising latent representations for better reconstruction outputs.

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Autoencoder-based network anomaly detection

Cited by 238 publications

References 13 publications

Self-Supervised Voltage Sag Source Identification Method Based on CNN

Self-Supervised Voltage Sag Source Identification Method Based on CNN

Anomaly Detection and Classification of Physiological Signals in IoT- Based Healthcare Framework

Robustness of Autoencoders for Anomaly Detection Under Adversarial Impact

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