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Flow-based Anomaly Detection [article]

Łukasz Maziarka, Marek Śmieja, Marcin Sendera, Łukasz Struski, Jacek Tabor, Przemysław Spurek
2020 arXiv   pre-print
We propose OneFlow - a flow-based one-class classifier for anomaly (outliers) detection that finds a minimal volume bounding region.  ...  Experiments show that the proposed model outperforms related methods on real-world anomaly detection problems.  ...  Recent research on anomaly detection is dominated by methods based on deep learning.  ... 
arXiv:2010.03002v2 fatcat:4wjue5yuunewbpalw5yxx56oyy

Flow-based SVDD for anomaly detection [article]

Marcin Sendera, Marek Śmieja, Łukasz Maziarka, Łukasz Struski, Przemysław Spurek, Jacek Tabor
2021 arXiv   pre-print
We propose FlowSVDD -- a flow-based one-class classifier for anomaly/outliers detection that realizes a well-known SVDD principle using deep learning tools.  ...  Contrary to other approaches to deep SVDD, the proposed model is instantiated using flow-based models, which naturally prevents from collapsing of bounding hypersphere into a single point.  ...  Observe that, unlike the density-based flow models, FlowSVDD does not transform data into Gaussian distribution in a latent space. Benchmark data for anomaly detection.  ... 
arXiv:2108.04907v1 fatcat:lyye2mtn7jeafjisft4medb4rq

Rule-Based Anomaly Detection on IP Flows

N. Duffield, P. Haffner, B. Krishnamurthy, H. Ringberg
2009 IEEE INFOCOM 2009 - The 28th Conference on Computer Communications  
Since ISPs already collect flow statistics ubiquitously, can we use it for detecting the same anomalies as the packet based rules in spite of aggregation and absence of payload information?  ...  Rule-based packet classification is a powerful method for identifying traffic anomalies, with network security as a key application area.  ...  Signature-based Detection on IP Flows An intrusion detection system that could inspect every network packet would be ideal, but is impractical.  ... 
doi:10.1109/infcom.2009.5061947 dblp:conf/infocom/DuffieldHKR09 fatcat:qkrtvhcv3fbrdar3kmscten2v4

Flow Graph Anomaly Detection Based on Unsupervised Learning

Zhengqiang Yang, Junwei Tian, Ning Li, Yugen Yi
2022 Mobile Information Systems  
In this paper, a flow graph anomaly detection framework based on unsupervised learning is proposed. Compared with traditional anomaly detection, graph anomaly detection faces some problems.  ...  Thirdly, due to the lack of sufficient training labeled data in most cases, anomaly detection models can only use unsupervised learning methods.  ...  This paper proposes a composite framework for flow graph anomaly detection based on unsupervised learning.  ... 
doi:10.1155/2022/4194714 fatcat:toa2wlbqgbh2fi6u4whpnffriq

A versatile anomaly detection method for medical images with a flow-based generative model in semi-supervision setting [article]

H. Shibata
2020 arXiv   pre-print
In this study, we present an anomaly detection method based on two trained flow-based generative models.  ...  Therefore, an all-purpose anomaly detection method that can detect virtually all types of lesions/diseases in a given image is strongly desired.  ...  Conclusions We proposed an anomaly detection method based on flow-based generative models with applicability to both CXRs and BCTs.  ... 
arXiv:2001.07847v3 fatcat:34fdvsls3nhxndymis523ak33u

Performance of Flow-based Anomaly Detection in Sampled Traffic

Zahra Jadidi, Vallipuram Muthukkumarasamy, Elankayer Sithirasenan, Kalvinder Singh
2016 Journal of Networks  
First, we optimize an artificial neural network (ANN)-based classifier to detect anomalies in flow traffic.  ...  Therefore, the detection rate of the flow-based anomaly detector is improved by about 5% using our algorithm.  ...  ACKNOWLEDGMENT The authors would like to thank CAIDA for the two datasets used to generate flow-based CAIDA datasets. They are also grateful to UNSW@ADFA for providing the flow-based DARPA datasets.  ... 
doi:10.4304/jnw.10.9.512-520 fatcat:3quwyosumvg6bhpvyfhkjpuz3a

ANOMALY-BASED INTRUSION DETECTION FROM NETWORK FLOW FEATURES USING VARIATIONAL AUTOENCODER

Sultan Zavrak, Murat Iskefiyeli
2020 IEEE Access  
The flow chart of AE-based anomaly detection algorithm can be illustrated as in Figure 2 . B.  ...  In AE based anomaly detection, anomaly scores are generated using REs.  ... 
doi:10.1109/access.2020.3001350 fatcat:32q7g36r2ncy3ka2ksaf56h62a

Unsupervised Video Anomaly Detection via Normalizing Flows with Implicit Latent Features [article]

MyeongAh Cho, Taeoh Kim, Woo Jin Kim, Suhwan Cho, Sangyoun Lee
2022 arXiv   pre-print
anomalies using out of distribution detection.  ...  Most existing methods use an autoencoder (AE) to learn to reconstruct normal videos; they then detect anomalies based on their failure to reconstruct the appearance of abnormal scenes.  ...  Many anomaly detection algorithms based on frame reconstruction that harness the powerful representation ability of deep convolutional networks have been proposed.  ... 
arXiv:2010.07524v3 fatcat:vpsebog6dncmtnrjzbkb47bao4

Towards Zero-Shot Flow-Based Cyber-Security Anomaly Detection Framework

Mikołaj Komisarek, Rafał Kozik, Marek Pawlicki, Michał Choraś
2022 Applied Sciences  
Network flow-based cyber anomaly detection is a difficult and complex task. Although several approaches to tackling this problem have been suggested, many research topics remain open.  ...  There is a limited number of papers which tackle transfer learning in the context of flow-based network anomaly detection, and the proposed approaches are mostly evaluated on outdated datasets.  ...  To the best of the authors' knowledge, there is a limited number of papers which tackle flow-based network anomaly detection in the context of transfer learning, and these are mostly used on outdated datasets  ... 
doi:10.3390/app12199636 fatcat:7eb6ml3ctvfkbnu6kpz4jr7n3m

Network Flow Anomaly Detection Based on Improved Echo State Network

Mingzhong Chen, Bin Qiu, Jie Ji, Pierre-Martin Tardif
2022 Wireless Communications and Mobile Computing  
Aiming at the problems existing in the current network flow anomaly detection, a network flow prediction model based on echo state network of double loop reserve pool is designed, which solves the problem  ...  Then, an anomaly detection method based on dynamic threshold is proposed, which takes the difference between the predicted value and the real value as the basis for judging the occurrence of anomalies.  ...  Therefore, in order to ensure the nonlinearity and realtime performance of network flow prediction, this paper designs a network flow anomaly detection method based on ESN of double loop reserve pool (  ... 
doi:10.1155/2022/4252766 fatcat:bi6lqskzrjgdpcfazlgpskjriq

Self-Diagnosis of Multiphase Flow Meters through Machine Learning-Based Anomaly Detection

Tommaso Barbariol, Enrico Feltresi, Gian Antonio Susto
2020 Energies  
In this work, we propose an Anomaly Detection approach, based on unsupervised Machine Learning algorithms, that enables the metrology system to detect outliers and to provide a statistical level of confidence  ...  The proposed approach, called AD4MPFM (Anomaly Detection for Multiphase Flow Meters), is designed for embedded implementation and for multivariate time-series data streams.  ...  Detecting known and unknown faults in automotive systems using ensemble-based anomaly detection. Knowl. Based Syst. 2017, 123, 163–173.  ... 
doi:10.3390/en13123136 fatcat:tvyweqdua5fqjafgs6sqacvxxa

Anomaly Detection Based Latency-Aware Energy Consumption Optimization For IoT Data-Flow Services

Yuansheng Luo, Wenjia Li, Shi Qiu
2019 Sensors  
A lightweight anomaly detection method is proposed to evaluate the reliability of nodes. Then the node reliability is input into the optimization algorithm to estimate the task latency.  ...  A block coordinate descend-based max-flow algorithm is proposed to solve this problem. Based on the real-life datasets, the numerical simulation is carried out.  ...  Anomaly Detection Based Latency Awareness According to the study by Pereira et al.  ... 
doi:10.3390/s20010122 pmid:31878140 pmcid:PMC6983123 fatcat:zxis23h2znfizhyrcx3vafytq4

Effects of Machine Learning Approach in Flow-Based Anomaly Detection on Software-Defined Networking

Samrat Kumar Dey, Md. Mahbubur Rahman
2019 Symmetry  
Hence, flow-based anomaly detection system in OpenFlow Controller can secure SDN to a great extent.  ...  In this research, we investigated two different approaches of flow-based intrusion detection system in OpenFlow Controller.  ...  In recent years, flow-based anomaly detection classifications have been extensively investigated.  ... 
doi:10.3390/sym12010007 fatcat:62jwglqmzrfoxiqg27wh7fe6ai

Flow-based anomaly intrusion detection system using two neural network stages

Yousef Abuadlla, Goran Kvascev, Slavko Gajin, Zoran Jovanovic
2014 Computer Science and Information Systems  
Flow-based intrusion detection systems are one of these approaches that rely on aggregated flow statistics of network traffic.  ...  In this paper, an intrusion detection system using two neural network stages based on flow-data is proposed for detecting and classifying attacks in network traffic.  ...  Flow-based IDSs analyze these flows data to detect anomaly and alarm possible attacks.  ... 
doi:10.2298/csis130415035a fatcat:zijmgi26mnbploh7pky7m6wssi

Normalizing Flow-Based Probability Distribution Representation Detector for Hyperspectral Anomaly Detection

Xiaorun Li, Shaoqi Yu, Shuhan Chen, Liaoying Zhao
2022 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Due to the powerful reconstruction ability, deep learning based hyperspectral anomaly detection methods have been prevalent in recent years.  ...  To address the issue, we propose a normalizing flowbased probability distribution representation detector (NF-PDRD) for hyperspectral anomaly detection in this article, which clarifies the capability of  ...  By dividing the pixels into several clusters, the cluster-based anomaly detection method detects the anomalies based on each cluster [14] .  ... 
doi:10.1109/jstars.2022.3182538 fatcat:44ywhf7huneqnm2coe3awboaiu
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