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Hierarchical Probabilistic Models for Group Anomaly Detection

Liang Xiong, Barnabas Poczos, Jeff Schneider, Andrew Connolly, Jake VanderPlas
In this paper, we propose two hierarchical probabilistic models for detecting such group anomalies.  ...  The experimental results show that the proposed models are effective in detecting group anomalies.  ...  For the AP performances, Discussion and Conclusions In this paper we investigated how to use hierarchical probabilistic models for the group anomaly detection problem.  ... 
doi:10.1184/r1/6475763.v1 fatcat:soixmgtsvbeqnltz2uc4nj32bq

Deep Abnormality Detection in Video Data

Hung Vu
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
Deep abnormality detection is a deep generative network that is an unsupervised probabilistic framework to model the normality and learn feature representation automatically.  ...  Most anomaly detection systems are only able to satisfy some of these challenges. In this work, we propose a deep abnormality detection system to handle all of them simultaneously.  ...  Anthony Trevors and Professor Svetha Venkatesh, for their instructions and advice.  ... 
doi:10.24963/ijcai.2017/768 dblp:conf/ijcai/Vu17 fatcat:emskff4pzbgzzagekjvzkkvsa4

Global Behaviour Inference using Probabilistic Latent Semantic Analysis

J. Li, S. Gong, T. Xiang
2008 Procedings of the British Machine Vision Conference 2008  
To model behavioural correlations globally, we investigate both a probabilistic Latent Semantic Analysis (pLSA) model and a two-stage hierarchical pLSA model for global behaviour inference and anomaly  ...  We present a novel framework for inferring global behaviour patterns through modelling behaviour correlations in a wide-area scene and detecting any anomaly in behaviours occurring both locally and globally  ...  The abnormality scores computed using Eqn. (13) and (16) were used for anomaly detection for the pLSA and hierarchical pLSA respectively.  ... 
doi:10.5244/c.22.20 dblp:conf/bmvc/LiGX08 fatcat:2pu3mpzrszaflor3qhtt5cqr2i

Discovering motion patterns in traffic videos using improved Group Sparse Topical Coding

Parvin Ahmadi, Soroosh Khoram, Mohsen Joneidi, Iman Gholampour, Mahmoud Tabandeh
2014 7'th International Symposium on Telecommunications (IST'2014)  
Furthermore, based on the two-level structure, either activity anomalies or traffic phase anomalies can be detected, which cannot be achieved by the one-level structure.  ...  One of the most recent and successful unsupervised methods for complex traffic scene analysis is based on topic models.  ...  This behavioral context was further used for video based complex behavior recognition and anomaly detection.  ... 
doi:10.1109/istel.2014.7000726 fatcat:vzh3pba6ybadtlmabcf7z6uhau


2019 Intelligent Data Analysis  
As for the KDDCUP99 data, their model achieved a very high detection. And finally the third group of articles are about enabling techniques in IDA.  ...  Their models detection rate of the Complex Malicious Response Injection (CMRI) attack category reached 95.5%, while the cyber anomaly detection algorithms based on machine learning could not detect any  ... 
doi:10.3233/ida-190006 fatcat:fh66shjh6bh6rlnhl45fxqarqm

Smart-energy group anomaly based behavioral abnormality detection

Alam, Roy, Petruska, Zemp
2016 2016 IEEE Wireless Health (WH)  
We employ hierarchical probabilistic model-based group anomaly detection [7] to interpret the anomalous behavior and therefore, detect potential tendency towards behavioral abnormality.  ...  Monitoring behavioral abnormality of individuals living independently in their own homes is a key issue for building sustainable healthcare models in smart environments.  ...  HIERARCHICAL GROUP ANOMALY DETECTION FRAMEWORK We employ the Mixture of Gaussian Mixture Model (MGMM) model [7] for group anomaly detection that extends Gaussian Latent Dirichlet Allocation (GLDA) algorithm  ... 
doi:10.1109/wh.2016.7764554 dblp:conf/wh/AlamRPZ16 fatcat:2gdhqnqwezav7bxexw4viyhg24


Serhii Toliupa, Mykola Brailovskyi, Ivan Parkhomenko
2018 Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska  
After investigating the hidden Markov model, probabilistic estimates are used as threshold values for identifying network anomalies in test data. Detection of attacks using Bayesian networks.  ...  Consider the possibility of using a group of data mining methods for designing a network attack detection system.  ... 
doi:10.5604/01.3001.0012.8022 fatcat:oratwmrbx5culgot4mq7n5kqle

A Formal Framework for Program Anomaly Detection [chapter]

Xiaokui Shu, Danfeng Yao, Barbara G. Ryder
2015 Lecture Notes in Computer Science  
Control-flow based metrics, such as average branching factor, are developed for evaluating specific groups of program anomaly detection methods [59] .  ...  We also point out some potential modeling features for future program anomaly detection evolution. should not (e.g., an attack).  ...  Sekar, David Evans and Dongyan Xu for their feedback on this work. The authors would like to thank anonymous reviewers for their comments on stochastic languages.  ... 
doi:10.1007/978-3-319-26362-5_13 fatcat:4qcygxknqjh45meygssx346vnm


2017 International Journal of Recent Trends in Engineering and Research  
For anomaly measurements, we formulate the abnormal event detection as a matching problem, which is more robust than statistic model based methods, especially when the training data set is of limited size  ...  Video anomaly detection plays a critical role for intelligent video surveillance. We present on abnormal video event detection system that considers both spatial and temporal contexts.  ...  Since crowds frequently contain distinct sub-entities, for example,vehicles or groups of people moving in different directions, anomaly detection requires modeling multiple video components of different  ... 
doi:10.23883/ijrter.conf.20170331.039.35scm fatcat:ngxzajqri5c4nlrxkpsjzrjws4

Out Lier Detection and Clustering Analysis in Data Stream Classification

2016 International Journal of Science and Research (IJSR)  
For the emerging topic detection purpose to propose a new method in the area of streaming data. Here, Dynamic Threshold Optimization algorithm is used to detect anomalies in streaming data.  ...  Here, we applied Hierarchical text clustering methods like C-Mean, Hierarchical Agglomerative clustering, and Single linkage algorithms are used for clustering and classification of the dataset.  ...  Related Work For topic detection, a finite mixture model is used in early days. Finite mixture model [6] is a weighted average of a number of probabilistic approaches.  ... 
doi:10.21275/v5i6.nov164400 fatcat:zgb6pdktovbivjsmbnsab23drm

Anomaly Detection in Crowded Scene by Pedestrians Behaviour Extraction using Long Short Term Method: A Comprehensive Study

Anupam Dey, Fahad Mohammad, Saleque Ahmed, Raiyan Sharif, A.F.M. Saifuddin Saif
2019 International Journal of Education and Management Engineering  
Based on the observation of previous research in three aspects, i.e. review based on methods, frameworks and critical existing results analysis, this research propose a framework for anomaly detection  ...  The significance of the proposed comprehensive review to create crowd administration procedures and help the development of the group or people, to maintain a strategic distance from the group calamities  ...  Group Behavior model depends on agglomerative hierarchical clustering. For this reason, the calculation time in this process for detecting a small group takes up to four minutes.  ... 
doi:10.5815/ijeme.2019.01.05 fatcat:mqjgyb2ff5aibhbernawighyke

Anomaly Topic and Emerging Topics Discovery Using Social Media

Yogita P. Shewale, Harshal Kumar R. Khairnar
2019 Helix  
With proposed ATD approach group anomalies are detected. Some techniques used all features for anomaly detection which get fail.  ...  Several approaches for anomaly detection have been proposed which is only capable of detecting individual anomaly. It is very time consuming and infeasible task.  ...  But it can only detect individual anomaly from huge data input which is infeasible task. Hence, proposed approach mainly aims to discover group anomaly.  ... 
doi:10.29042/2019-4947-4955 fatcat:ltuhxj4tvjbjxckc755jsbc4a4

Localized anomaly detection via hierarchical integrated activity discovery

Thiyagarajan Chockalingam, Remi Emonet, Jean-Marc Odobez
2013 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance  
With the increasing number and variety of camera installations, unsupervised methods that learn typical activities have become popular for anomaly detection.  ...  the detected anomalies.  ...  PLSM has been used for anomaly detection in surveillance video in [4] .  ... 
doi:10.1109/avss.2013.6636615 dblp:conf/avss/ChockalingamEO13 fatcat:yg7c7zvpqrgpjjpod4hpcy7ake

Group Anomaly Detection: Past Notions, Present Insights, and Future Prospects

Aqeel Feroze, Ali Daud, Tehmina Amjad, Malik Khizar Hayat
2021 SN Computer Science  
The authors decided to survey existing group anomaly detection techniques because there is a need to consider group anomalies for mitigation of risks, prevention of malicious collaborative activities,  ...  Towards the end, we have provided various applications of group anomaly detection and the research challenges that group anomaly detection presents to the scientific community and enlisted some of the  ...  Following the hierarchical probabilistic model [27] discussed in the above activity-based section, the group latent anomaly detection (GLAD) model [18] addressed the issue for mutually heterogeneous  ... 
doi:10.1007/s42979-021-00603-x fatcat:oyjzthza7vbhnakpm3t2ko6ctq

Anomaly Detection System for Water Networks in Northern Ethiopia Using Bayesian Inference

Zaid Tashman, Christoph Gorder, Sonali Parthasarathy, Mohamad M. Nasr-Azadani, Rachel Webre
2020 Sustainability  
To detect anomalies, we need a model of normal water usage behavior first.  ...  In this work, we present a novel two-level anomaly detection system aimed to detect malfunctioning remote sensored water hand-pumps, allowing for a proactive approach to pump maintenance.  ...  To the best of our knowledge, such a hierarchical anomaly detection system has not been developed for water networks.  ... 
doi:10.3390/su12072897 fatcat:emlgd6z7hjas3iisdiow6wxy6i
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