In this paper, we propose an Autoencoder-based network anomaly detection method. Autoencoder is able to capture the non-linear correlations between features so as to increase the detection accuracy. We also apply the Convolutional Autoencoder (CAE) here to perform the dimensionality reduction. As...
Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network 发表会议:2019 KDD 1 Movation 1.由于以下原因,作者希望可以直接使用多元时间序列在实体级别检测实体异常,而不是使用单变量时间序列在度量级别检测实体异常。 1)在实践中,与每个构成指标相... ...
In this paper a novel semi-supervised approach for anomaly detection in supercomputers is proposed, based on a type of neural network called autoencoder. The approach learns the normal state of the supercomputer nodes and after the training phase can be used to discern anomalous conditions from no...
At present, there are a number of network traffic anomaly detection methods based on machine learning. Many of them pay attention to business features and adopt the approach of traditional machine learning. Meanwhile, some researchers are inclined to use neural networks to extract deep features for...
Special Lecture on IE, SNU Data Mining Center 2015·Jinwon An,Sungzoon Cho· We propose an anomaly detection method using the reconstruction probability from the variational autoencoder. The reconstruction probability is a probabilistic measure that takes into account the variability of the distribution...
Anomaly detection is a problem with roots dating back over 30 years. The NSL-KDD dataset has become the convention for testing and comparing new or improved models in this domain. In the field of network intrusion detection, the UNSW-NB15 dataset has recently gained significant attention over ...
Pytorch implementation of GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection - munhouiani/GEE
In response to this challenge, this work introduces an anomaly detection method based on a Variational Autoencoder (VAE) improved with the assistance of a Generative Adversarial Network (GAN). This proposed method introduces adversarial generation ideas into the VAE framework and uses only normal ...
where,fis a posterior probability function that uses deep neural network to perform a non-linear transformation withzparameters. The exact computation of the posterior\(p_\theta (z|x)\)in this model is not mathematically feasible. Instead, a distribution\(q_\phi (z|x)\)[37] is used to ...
Besides taking into account this variability without accidentally rejecting messages from Alice (thus incurring in a false alarm), Bob must be able also to correctly identify messages coming from possible attackers in the network (who are trying to impersonate Alice and induce a missed detection). ...