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An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks [article]

Hiba Tabbaa, Samir Ifzarne, Imad Hafidi
2022 arXiv   pre-print
A huge number of sensing devices collect and/or generate numerous sensory data throughout time for a wide range of fields and applications.  ...  WSN applications are extremely critical, it is essential to build reliable solutions that involve fast and continuous mechanisms for online data stream analysis enabling the detection of attacks and intrusions  ...  There are few works addressing anomaly detection in streaming data for embedded systems.  ... 
arXiv:2204.13814v1 fatcat:gtydljd3jnaelau2uv3osnh5e4

Online Multivariate Anomaly Detection and Localization for High-dimensional Settings [article]

Mahsa Mozaffari, Yasin Yilmaz
2020 arXiv   pre-print
This paper considers the real-time detection of anomalies in high-dimensional systems.  ...  The proposed method follows a nonparametric, i.e., data-driven, and semi-supervised approach, i.e., trains only on nominal data. Thus, it is applicable to a wide range of applications and data types.  ...  accumulates it over time for reliable detection.  ... 
arXiv:1905.07107v2 fatcat:ovcl32g27fhtxlugpci25i64au

Improving Outliers Detection in Data Streams using LiCS and Voting

Fatima-Zahra Benjelloun, Ahmed Oussous, Amine Bennani, Samir Belfkih, Ayoub Ait Lahcen
2019 Journal of King Saud University: Computer and Information Sciences  
However, detecting outliers in data streams rises many challenges such as high-dimensionality, dynamic data distribution and unpredictable relationships.  ...  This is by adding a layer called LiCS that classifies online the K-nearestneighbors (Knn) of each node based on their evolutionary status.  ...  So, algorithms should reduce the number of passes over data for fast queries.  ... 
doi:10.1016/j.jksuci.2019.08.003 fatcat:gdc5zizmsfedtacprlwu7hji2m

Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques

Milan Flach, Fabian Gans, Alexander Brenning, Joachim Denzler, Markus Reichstein, Erik Rodner, Sebastian Bathiany, Paul Bodesheim, Yanira Guanche, Sebastian Sippel, Miguel D. Mahecha
2017 Earth System Dynamics  
Although many algorithms have been proposed for detecting anomalies in multivariate data, only a few have been investigated in the context of Earth system science applications.  ...  Our aim is to identify suitable workflows for automatically detecting anomalous patterns in multivariate Earth system data streams.  ...  We can show that, on average over different anomaly types and data properties, three multivariate anomaly detection algorithms (KDE, REC, KNN-Gamma) outperform univariate extreme event detection as well  ... 
doi:10.5194/esd-8-677-2017 fatcat:kskjrh7wsre7jfvef3rxey4p3e

Multivariate Anomaly Detection for Earth Observations: A Comparison of Algorithms and Feature Extraction Techniques

Milan Flach, Fabian Gans, Alexander Brenning, Joachim Denzler, Markus Reichstein, Erik Rodner, Sebastian Bathiany, Paul Bodesheim, Yanira Guanche, Sebastian Sippel, Miguel D. Mahecha
2016 Earth System Dynamics Discussions  
Our aim is to identify suitable workflows for automatically detecting anomalous patterns in multivariate Earth system data streams.  ...  Although many algorithms have been proposed for detecting anomalies in multivariate data, only few have been investigated in the context of Earth system science applications.  ...  We can show that, on average over different anomaly types and data properties, three multivariate anomaly detection algorithms (KDE, REC, KNN-Gamma) outperform univariate extreme event detection as well  ... 
doi:10.5194/esd-2016-51 fatcat:btjfmnf525a3vfw3v64hf25ovu

A Modular and Unified Framework for Detecting and Localizing Video Anomalies [article]

Keval Doshi, Yasin Yilmaz
2021 arXiv   pre-print
plug-and-play architecture, a sequential anomaly detector, a mathematical framework for selecting the detection threshold, and a suitable performance metric for real-time anomalous event detection in  ...  Motivated by these research gaps, we propose a modular and unified approach to the online video anomaly detection and localization problem, called MOVAD, which consists of a novel transfer learning based  ...  The anomaly evidence scores (i.e., kNN distances) from streaming video frames provide an informative time series data which typically takes large values when the anomalous event starts.  ... 
arXiv:2103.11299v1 fatcat:cqip5pyg5bfzfgiocrmrr252p4

Arrays of (locality-sensitive) Count Estimators (ACE): High-Speed Anomaly Detection via Cache Lookups [article]

Chen Luo, Anshumali Shrivastava
2017 arXiv   pre-print
Even being a well-studied topic, existing techniques for unsupervised anomaly detection require storing significant amounts of data, which is prohibitive from memory and latency perspective.  ...  These tiny 4MB arrays of counts are sufficient for unsupervised anomaly detection.  ...  Challenge 1: High-Speed Drifting Data Many streaming applications demand fast-response and real-time inference from dynamic and drifting high volumes of sensor data over time.  ... 
arXiv:1706.06664v1 fatcat:touxonk5kbf5nlxyfkbo46cjnu

Sliding Window-Based Fault Detection From High-Dimensional Data Streams

Liangwei Zhang, Jing Lin, Ramin Karim
2016 IEEE Transactions on Systems, Man & Cybernetics. Systems  
An angle-based subspace anomaly detection approach is proposed to detect low-dimensional subspace faults from high-dimensional datasets.  ...  To this purpose, this paper presents an approach to fault detection from nonstationary highdimensional data streams.  ...  ACKNOWLEDGMENT The authors would like to thank the editor, the Associate Editors, and the referees for their constructive comments and suggestions that greatly improved the content of this paper.  ... 
doi:10.1109/tsmc.2016.2585566 fatcat:ktqljevhsrcu7nbyjbuwv7hmsy

Deep Nearest Neighbor Anomaly Detection [article]

Liron Bergman and Niv Cohen and Yedid Hoshen
2020 arXiv   pre-print
Nearest neighbors is a successful and long-standing technique for anomaly detection. Significant progress has been recently achieved by self-supervised deep methods (e.g. RotNet).  ...  The simple nearest-neighbor based-approach is experimentally shown to outperform self-supervised methods in: accuracy, few shot generalization, training time and noise robustness while making fewer assumptions  ...  Deep Nearest-Neighbors for Image Anomaly Detection We investigate a simple K nearest-neighbors (kNN) based method for image anomaly detection. We denote this method, Deep Nearest-Neighbors (DN2).  ... 
arXiv:2002.10445v1 fatcat:65yody4wgvf6vhqlk3nl2ntzne

Loda: Lightweight on-line detector of anomalies

Tomáš Pevný
2015 Machine Learning  
The simplicity of the proposed ensemble system (to be called Loda) is particularly useful in domains where a large number of samples need to be processed in real-time or in domains where the data stream  ...  We compare Loda to several state of the art anomaly detectors in two settings: batch training and on-line training on data streams.  ...  Stationary data Loda has been compared to the following anomaly detectors chosen to represent different approaches to anomaly detection: PCA based anomaly detector (Shyu et al. 2003) , 1-SVM (Schölkopf  ... 
doi:10.1007/s10994-015-5521-0 fatcat:dqcmr7ovzjg4dp7tvbiv3vp7ki

A framework for automated anomaly detection in high frequency water-quality data from in situ sensors [article]

Catherine Leigh, Omar Alsibai, Rob J. Hyndman, Sevvandi Kandanaarachchi, Olivia C. King, James M. McGree, Catherine Neelamraju, Jennifer Strauss, Priyanga Dilini Talagala, Ryan S. Turner, Kerrie Mengersen, Erin E. Peterson
2019 arXiv   pre-print
We present a framework for automated anomaly detection in high-frequency water-quality data from in situ sensors, using turbidity, conductivity and river level data.  ...  Our framework is applicable to other types of high frequency time-series data and anomaly detection applications.  ...  A repository of the water-quality data from the in situ sensors used herein and the code used to implement the regression-based anomaly detection methods are provided in the Supplementary materials.  ... 
arXiv:1810.13076v2 fatcat:54xz4xk5i5et5p2fragwzabjye

Anomaly Detection on Data Streams for Smart Agriculture

Juliet Chebet Moso, Stéphane Cormier, Cyril de Runz, Hacène Fouchal, John Mwangi Wandeto
2021 Agriculture  
They generate many spatial, temporal, and time-series data streams that, when analysed, can reveal several issues on farm productivity and efficiency.  ...  The second is dedicated to crop data where we study the link between crop state (damaged or not) and detected anomalies.  ...  In this study, we performed unsupervised anomaly detection in obtained data streams from movement tracks of combine harvesters during wheat harvest, and data for crop damage recorded by farmers over a  ... 
doi:10.3390/agriculture11111083 fatcat:zrjl5ptp35apfnpefikcpl4xq4

Arrays of (locality-sensitive) Count Estimators (ACE)

Chen Luo, Anshumali Shrivastava
2018 Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18  
Even being a well-studied topic, existing techniques for unsupervised anomaly detection require storing significant amounts of data, which is prohibitive from memory, latency and privacy perspectives,  ...  Anomaly detection is one of the frequent and important subroutines deployed in large-scale data processing applications.  ...  There is a third category of anomaly detection algorithms over a sliding window in data streams [44] .  ... 
doi:10.1145/3178876.3186056 dblp:conf/www/LuoS18 fatcat:ysyaoli5kjhezimx6jvh5xzb6i

Application of Machine Learning Approaches in Intrusion Detection System: A Survey

Nutan Farah, Md. Avishek, Faisal Muhammad, Abdur Rahman, Musharrat Rafni, Dewan Md.
2015 International Journal of Advanced Research in Artificial Intelligence (IJARAI)  
Over the past years, many studies have been conducted on the intrusion detection system.  ...  Intrusion detection system is used to identify unauthorized access and unusual attacks over the secured networks.  ...  Decision Tree  Madam id for intrusion detection using data mining.  A cost-sensitive decision tree approach for fraud detection.  Data mining-based intrusion detectors.  ... 
doi:10.14569/ijarai.2015.040302 fatcat:v7a66oibcrhczn47azmlnl4q3q

TagFall: Towards Unobstructive Fine-Grained Fall Detection based on UHF Passive RFID Tags

Wenjie Ruan, Lina Yao, Quan Z. Sheng, Nickolas Falkner, Xue Li, Tao Gu
2015 Proceedings of the 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services  
Once a fall event is detected, we first segment a fix-length RSSI data stream generated by the fall and then utilize Dynamic Time Warping (DTW) based k Nearest Neighbors (kNN) to distinguish the falling  ...  We first augment the Angle-based Outlier Detection Method (ABOD) to classify normal actions (e.g., standing, sitting, lying and walking) and detect a fall event.  ...  Thus, different to the traditional classification or distance-based anomaly detection methods, our proposed method relaxes the requirement of tuning parameters that is time-consuming and sensitive to different  ... 
doi:10.4108/eai.22-7-2015.2260072 dblp:conf/mobiquitous/RuanYSFLG15 fatcat:atn5jrajxvgsbcbkz4vlwuyzbu
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