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 intrus
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). ...
Network anomaly detection using federated deep autoencoding Gaussian mixture model J. Huang et al. Fed-smae: federated-learning based time series anomaly detection with shared memory augmented autoencoder Y. Lu et al. A swarm anomaly detection model for iot uavs based on a multi-modal denoising...
In this paper, a novel, efficient, and effective unsupervised anomaly detection model for WBANs is developed using the autoencoder convolutional neural network (CNN) technique. Due to their ability to reconstruct data in a completely unsupervised manner using reconstruction error, autoencoders hold ...
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...
Owing to these security concerns, this study introduces a Bi-directional sparse Attention-recurrent Auto Encoder (BSAR-AE) based Intrusion Detection System (IDS) in VANET. The system uses the Tuna Swarm optimizer (TSO) with the enhancement through the implementation of Deep Neural Network (DNN) ...
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...
In this paper we propose and empirically evaluate a novel network based anomaly detection method which extracts behavior snapshots of the network and uses deep autoencoders to detect anomalous network traffic emanating from compromised IoT devices. To evaluate our method, we infected nine commercial ...
Pytorch implementation of GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection - munhouiani/GEE
Therefore, unsupervised learning- based anomaly detection methods rely solely on normal data to train deep neural network models, leading to superior performance compared to traditional methods in anomaly detection. In this study, we aimed to develop an earthquake warning system using an onboard ...