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A Robust Anomaly Detection Technique Using Combined Statistical Methods

Joseph Ndong, Kavé Salamatian
2011 2011 Ninth Annual Communication Networks and Services Research Conference  
Parametric anomaly detection is generally a three steps process where, in the first step a model of normal behavior is calibrated and thereafter, the obtained model is used in order to reduce the entropy  ...  The second step generates an innovation process that is used in the third step to make a decision on the existence or not of an anomaly in the observed data.  ...  To detect traffic anomalies, one typically seeks to characterize or build a model of normal behavior.  ... 
doi:10.1109/cnsr.2011.23 dblp:conf/cnsr/NdongS11 fatcat:zuhygvr2x5d45mmdbqt7ifawvi

A Survey Paper on Fraud Analysis and Report Visualization in Card System

Shipra Rathore, Deepak Kumar
2015 International Journal of Computer Applications  
There are many techniques like ANIDS Distributed Data mining WSDL, Parallel granular neural networks, Hidden Markov Model, Clustering and Bayes classification, Cost model are used.  ...  These techniques use data mining to detect frauds by the use of datasets like KDD99, KDD cup, NSL KDD. In this paper a study on different techniques and evaluate each technique on the best parameters.  ...  Abhinav Shrivastava, Amlan Kundu, Shamik Sural, proposed a paper titled "Credit Card Fraud Detection Using Hidden Markov Model" [6] presented HMM based fraud detection technique which is based on the  ... 
doi:10.5120/ijca2015906923 fatcat:nbkjw24lgfajjnhrxfnzmkosru

Multi-Granularity Tracking with Modularlized Components for Unsupervised Vehicles Anomaly Detection

Yingying Li, Jie Wu, Xue Bai, Xipeng Yang, Xiao Tan, Guanbin Li, Shilei Wen, Hongwu Zhang, Errui Ding
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
Anomaly detection on road traffic is a fundamental computer vision task and plays a critical role in video structure analysis and urban traffic analysis.  ...  The modularized framework consists of a detection module, a background modeling module, a mask extraction module, and a multi-granularity tracking algorithm.  ...  To deal with the above challenges, we tackle the traffic anomaly detection problem based on vehicle detection and tracking.  ... 
doi:10.1109/cvprw50498.2020.00301 dblp:conf/cvpr/LiWBYTLWZD20 fatcat:k7fcewehlbaz3inuxybnuojc3e

TRASMIL: A local anomaly detection framework based on trajectory segmentation and multi-instance learning

Wanqi Yang, Yang Gao, Longbing Cao
2013 Computer Vision and Image Understanding  
Secondly, the segmented sub-trajectories are modeled by a sequence learning model.  ...  In this paper, an accurate and flexible threephase framework TRASMIL is proposed for local anomaly detection based on TRAjectory Segmentation and Multi-Instance Learning.  ...  Based on the above analysis, local anomaly detection based on trajectory segmentation has a similar nature to multi-instance learning.  ... 
doi:10.1016/j.cviu.2012.08.010 fatcat:r7fisyqwarhdrpio73p4mmniwq

Anomaly Detection and Attribution in Network

2020 International Journal of Engineering and Advanced Technology  
The second algorithm detects anomalies through GLRT on aggregated flow transformation a compact low-dimensional representation of raw traffic flows.  ...  The first is based on the system for cross-entropy (CE), which identifies flow anomalies and labels flow anomalies.  ...  The first is based on a crossentropy ( CE) system, which identifies flow anomalies and attributes.  ... 
doi:10.35940/ijeat.c6335.029320 fatcat:ejvub3lp6remvoyp2uyrntqmuu

Anomaly Detection and Attribution in Network

K Viswak Raj, Dept of Computer Science and Engineering, SRM Institute of Science and Technology Chennai. India, M Mukesh, J. Kalaivani, Dept of Computer Science and Engineering, SRM Institute of Science and Technology Chennai. India, Dept of Computer Science and Engineering, SRM Institute of Science and Technology Chennai. India
2020 International Journal of Engineering and Advanced Technology  
The second algorithm detects anomalies through GLRT on aggregated flow transformation a compact low-dimensional representation of raw traffic flows.  ...  The first is based on the system for cross-entropy (CE), which identifies flow anomalies and labels flow anomalies.  ...  The first is based on a crossentropy ( CE) system, which identifies flow anomalies and attributes.  ... 
doi:10.35940/ijeat.c6335.049420 fatcat:76bacmjohfcmhirf7h33r2squm

Web news mining in an evolving framework

José Antonio Iglesias, Alexandra Tiemblo, Agapito Ledezma, Araceli Sanchis
2016 Information Fusion  
This paper presents DECT, a scalable time-variant variable-order Markov model. DECT digests terabytes of user session data and yields user behavior patterns through time.  ...  Our implementation is being open-sourced and we deploy DECT on top of Yahoo! infrastructure. We demonstrate the benefits of DECT with anomaly detection and ad click rate prediction applications.  ...  Given a pathp, a higher-order Markov model can be trained to predict the last transition ofp based on previously visited sites inp.  ... 
doi:10.1016/j.inffus.2015.07.004 fatcat:7qs4dfnd6zbnpi62azwmantmcq

On achieving good operating points on an ROC plane using stochastic anomaly score prediction

Muhammad Qasim Ali, Hassan Khan, Ali Sajjad, Syed Ali Khayam
2009 Proceedings of the 16th ACM conference on Computer and communications security - CCS '09  
The proposed adaptive thresholding module is incorporated into six prominent network-and host-based Anomaly Detection Systems (ADSs).  ...  We model the observed correlation structure using Markov chains and then use this model to predict and adapt an IDS' classification threshold.  ...  Based on the decaying correlation structure present in anomaly scores, we argue that a stochastic model of a few previous scores can be used to accurately predict future scores.  ... 
doi:10.1145/1653662.1653700 dblp:conf/ccs/AliKSK09 fatcat:xp4akadkd5eftaqzhzd7e36rb4

Image-based Insider Threat Detection via Geometric Transformation [article]

Dongyang Li, Lin Yang, Hongguang Zhang, Xiaolei Wang, Linru Ma, Junchao Xiao
2021 arXiv   pre-print
By applying multiple geometric transformations on these behavior grayscale images, IGT constructs a self-labelled dataset and then train a behavior classifier to detect anomaly in self-supervised manner  ...  In this paper, we propose a novel insider threat detection method, Image-based Insider Threat Detector via Geometric Transformation (IGT), which converts the unsupervised anomaly detection into supervised  ...  This research was supported by a research grant from the National Science Foundation of China under Grant No. 61772271, and the Natural Science Foundation of Jiangsu under Grant No. SBK2020043435.  ... 
arXiv:2108.10567v1 fatcat:w2lxxy4wxbhmrgmh5ahdv2g5ei

Image-Based Insider Threat Detection via Geometric Transformation

Dongyang Li, Lin Yang, Hongguang Zhang, Xiaolei Wang, Linru Ma, Junchao Xiao, Abdallah Meraoumia
2021 Security and Communication Networks  
By applying multiple geometric transformations on these behavior grayscale images, IGT constructs a self-labelled dataset and then trains a behavior classifier to detect anomaly in a self-supervised manner  ...  In this paper, we propose a novel insider threat detection method, Image-based Insider Threat Detector via Geometric Transformation (IGT), which converts the unsupervised anomaly detection into supervised  ...  On the basis of these grayscale images, the anomaly detection procedure can construct and train a geometric transformation-based classification model.  ... 
doi:10.1155/2021/1777536 fatcat:ocd5vx2b7bfjligjgq6ggscope

A Survey on Prevention Approaches for Denial of Sleep Attacks in Wireless Networks

G. Mahalakshmi, P. Subathra
2014 Journal of Emerging Technologies in Web Intelligence  
Index Terms-Anomaly detection, denial of sleep attack, frequency hopping, sleep deprivation.  ...  Most of the existing approaches to detect denial of sleep attack involve lot of overhead, which lead to poor throughput.  ...  Section 4 provides conclusion and future enhancement II PREVENTION APPROACHES FOR DENIAL OF SLEEP ATTACK Absorbing Markov Chain (AMC) Model A mathematical model based on Absorbing Markov Chain (AMC)  ... 
doi:10.4304/jetwi.6.1.106-110 fatcat:wwf3kk2yebehnd546emashobqm

A Sharper Sense of Self: Probabilistic Reasoning of Program Behaviors for Anomaly Detection with Context Sensitivity

Kui Xu, Ke Tian, Danfeng Yao, Barbara G. Ryder
2016 2016 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)  
We describe a matrix representation and clustering-based solution for model reduction, specifically reducing the number of hidden states in a special hidden Markov model whose parameters are initialized  ...  Program anomaly detection models legitimate behaviors of complex software and detects deviations during execution.  ...  ACKNOWLEDGEMENTS The authors would like to thank the anonymous reviewers for their insightful comments on the work. This work has been supported by ONR grant N00014-13-1-0016.  ... 
doi:10.1109/dsn.2016.49 dblp:conf/dsn/XuTYR16 fatcat:v7dyrgneinhrrog5rjcwuyzncq

An improved Hidden Markov Model for anomaly detection using frequent common patterns

Afroza Sultana, Abdelwahab Hamou-Lhadj, Mario Couture
2012 2012 IEEE International Conference on Communications (ICC)  
The models can later be used as a baseline for online detection of abnormal behavior. Perhaps the most popular techniques are the ones based on the use of Hidden Markov Models (HMM).  ...  In other words, we build models based on extracting the largest n-grams (patterns) in the traces instead of taking each trace event on its own.  ...  HIDDEN MARKOV MODELS A Hidden Markov Model (HMM) is a double stochastic model [23] .  ... 
doi:10.1109/icc.2012.6364527 dblp:conf/icc/SultanaHC12 fatcat:r4j6l6mdpjcs5kpgtvyh5cbzwi

2019 Index IEEE Transactions on Big Data Vol. 5

2020 IEEE Transactions on Big Data  
AlMahmoud, A., +, TBData Sept. 2019 293-304 Hidden Markov models Detecting Anomalous Behavior in Cloud Servers by Nested-Arc Hidden SEMI-Markov Model with State Summarization.  ...  ., +, TBData Sept. 2019 330-342 Detecting Anomalous Behavior in Cloud Servers by Nested-Arc Hidden SEMI-Markov Model with State Summarization.  ...  T Task analysis A  ... 
doi:10.1109/tbdata.2020.2975953 fatcat:aeai72ddszachltlggv3u5dpru

Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques

Shirin Enshaeifar, Ahmed Zoha, Andreas Markides, Severin Skillman, Sahr Thomas Acton, Tarek Elsaleh, Masoud Hassanpour, Alireza Ahrabian, Mark Kenny, Stuart Klein, Helen Rostill, Ramin Nilforooshan (+2 others)
2018 PLoS ONE  
PLoS ONE 13(5): e0195605. https://doi.org/10.  ...  We have also developed a hierarchical information fusion approach for detecting agitation, irritability and aggression.  ...  A full list of the contributors and partners is available at https://www.sabp.nhs.uk/tihm. The data collected in the TIHM project will be available upon request and subject to ethics approval.  ... 
doi:10.1371/journal.pone.0195605 pmid:29723236 pmcid:PMC5933790 fatcat:4aoirhmezzetpbujqnq6atmf5m
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