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Anomaly Network Intrusion Detection Using Hidden Markov Model

Chia-Mei Chen, Dah-Jyh Guan, Yu-Zhi Huang, Ya-Hui Ou
2016 International Journal of Innovative Computing, Information and Control  
This study defines a sequence of attack states corresponding to the attack stages and the proposed detection system adopts a stated-based classification model, Hidden Markov Model, for detecting such advanced  ...  A state-based classification model is suitable for detecting the attacks composed of a sequence of attack stages.  ...  Hidden Markov Model.  ... 
doi:10.24507/ijicic.12.02.569 fatcat:th5oo2z2ojcnfhyvlyeih3fzta

Tracking Activities in Complex Settings Using Smart Environment Technologies

Geetika Singla, Diane J Cook, Maureen Schmitter-Edgecombe
2009 International journal of biosciences, psychiatry, and technology (IJBSPT)  
To evaluate the accuracy of our recognition algorithms we assess them using real data collected from participants performing activities in our on-campus smart apartment testbed.  ...  We use the Viterbi algorithm [14] to identify this sequence of hidden states. In our implementation of a hidden Markov model, we treat every activity as a hidden state.  ...  In our study, we make use of three probabilistic models to represent and recognize activities based on observed sensor sequences: a naïve Bayes classifier, a Markov chain model, and a hidden Markov model  ... 
pmid:20019890 pmcid:PMC2794487 fatcat:t7ldwgay5nganalaryhaehyzoy

Occlusion-adaptive fusion for gait-based motion recognition

S.L. Dockstader
2003 Sixth International Conference of Information Fusion, 2003. Proceedings of the  
This paper presents a new architecture for motion-and video-based event recognition using the fusion of multiple hidden Markov models (HMM) with a Bayesian belief network.  ...  We demonstrate the effectiveness of our approach on numerous multi-view video sequences of complex human motion.  ...  The confusion matrices in Tables 3 and 4 identify specific error modes associated with recognition for both the Bayesian-Markov and coupled hidden Markov models.  ... 
doi:10.1109/icif.2003.177458 fatcat:ncd5llb7jfdcdavsiotr74aipa

Process Discovery Using Classification Tree Hidden Semi-Markov Model

Yihuang Kang, Vladimir Zadorozhny
2016 2016 IEEE 17th International Conference on Information Reuse and Integration (IRI)  
Specifically, we propose a probabilistic process model that combines hidden semi-Markov model and classification trees learning.  ...  These systems are generating massive amount of event sequence logs that may help us understand underlying phenomenon.  ...  In this paper, we use a specialization of the Markov Model-Hidden Markov model (HMM) [8] . Markov models are simple probabilistic models used to cope with the temporal sequences.  ... 
doi:10.1109/iri.2016.55 dblp:conf/iri/KangZ16 fatcat:d5lvaridgjdvbd6ggfaejtbhby

Detection of the Curves based on Lateral Acceleration using Hidden Markov Models

Roza Maghsood, Pär Johannesson
2013 Procedia Engineering  
The method is based on hidden Markov models (HMMs) which are probabilistic models that can be used to recognize patterns in time series data.  ...  The idea here is to consider the current driving event as the hidden state and the lateral acceleration signal as the observed sequence.  ...  Thomas Svensson for his useful ideas and helpful suggestions in this study.  ... 
doi:10.1016/j.proeng.2013.12.096 fatcat:wclsdlzljfaphmxa3oop725rry

Activity Recognition Using Hierarchical Hidden Markov Models on Streaming Sensor Data

Parviz Asghari, Ehsan Nazerfard
2018 2018 9th International Symposium on Telecommunications (IST)  
The present study tries to use online hierarchical hidden Markov model to detect an activity on the stream of sensor data which can predict the activity in the environment with any sensor event.  ...  of our proposed method test on two different datasets of smart homes in the real world showed that one dataset has improved 4% and reached (59%) while the results reached 64.6% for the other data by using  ...  In the first stage, the activities were clustered by a hidden Markov model and then the activities of each cluster were classified by using a separate hidden Markov model.  ... 
doi:10.1109/istel.2018.8661053 dblp:conf/istel/AsghariN18 fatcat:ljpd4sxa4zczlhh2i4z23zspqe

Automatic Segmentation For Music Classification Using Competitive Hidden Markov Models

Eloi Batlle, Pedro Cano
2000 Zenodo  
Competitive Hidden Markov Models have proved to be specially well suited for this kind of situations.  ...  This audio segmentation is done using competitive hidden Markov models as the main classification engine and, thus, no previous classified or hand-labeled data is needed.  ... 
doi:10.5281/zenodo.1416764 fatcat:equyg6cil5e5zpyflxqkrcuzv4

Recognizing independent and joint activities among multiple residents in smart environments

Geetika Singla, Diane J. Cook, Maureen Schmitter-Edgecombe
2009 Journal of Ambient Intelligence and Humanized Computing  
To evaluate the accuracy of our recognition algorithms we assess them using real data collected from participants performing activities in our on-campus smart apartment testbed.  ...  Specifically, we design a hidden Markov model to determine an activity that most likely corresponds to an observed sequence of sensor events.  ...  The challenge here is to identify the sequence of activities (i.e., the sequence of visited hidden states) that corresponds to a sequence of sensor events (i.e., the observable states).  ... 
doi:10.1007/s12652-009-0007-1 pmid:20975986 pmcid:PMC2958106 fatcat:uh5lqcdpl5cnffaldyd3az3i5y

Spatiotemporal pattern recognition using hidden Markov models

Kenneth H. Fielding, Dennis W. Ruck, Steven K. Rogers, Byron M. Welsh, Mark E. Oxley, Su-Shing Chen
1993 Neural and Stochastic Methods in Image and Signal Processing II  
This dissertation uses the Hidden Markov Model as a spatio-temporal pattern recognition algorithm to identify 3D objects contained in 2D image sequences.  ...  Dewitt (19) used range profiles obtained from high resolution radar and the Hidden Markov Model technique to identify aircraft.  ...  The Hidden Markov Model technique is used as a spatio-temporal classification algorithm to identify 3D objects by the temporal changes in observedshp features.  ... 
doi:10.1117/12.162031 fatcat:v364gvdlwzgd5lxsooyi6z2fbi

Spatio-temporal pattern recognition using hidden Markov models

K.H. Fielding, D.W. Ruck
1995 IEEE Transactions on Aerospace and Electronic Systems  
This dissertation uses the Hidden Markov Model as a spatio-temporal pattern recognition algorithm to identify 3D objects contained in 2D image sequences.  ...  Dewitt (19) used range profiles obtained from high resolution radar and the Hidden Markov Model technique to identify aircraft.  ...  The Hidden Markov Model technique is used as a spatio-temporal classification algorithm to identify 3D objects by the temporal changes in observedshp features.  ... 
doi:10.1109/7.464351 fatcat:gumq3pefbzdzloclq2ile5uapq

Challenges in Transition to m Commerce in Rural India

Nishi Malhotra, Pankaj Shah, Saravanan S.
2017 International Journal of Computer Applications  
Hidden Markov Model is an approach to study the temporal sequence of behavior in channel migration and channel choice.  ...  An descriptive study to evaluate various kinds of models for different kinds of data distribution is aimed at identifying the best kind of Hidden Markov Model for studying the issue of channel migration  ...  sequence O and the state of states in the Hidden Markov Model.  ... 
doi:10.5120/ijca2017915387 fatcat:wg2lwtqisrhupcbampwigsotpi

ISEC 2016 - Estimating Seasonal Behavior States from Bio-Logging Sensor Data [article]

Josh London, Devin Johnson, Brett McClintock, Paul Conn, Michael Cameron, Peter Boveng
2016 Figshare  
Hidden Markov models (HMM) are commonly used to estimate behavior states (e.g., foraging, resting, transit) from telemetry data.  ...  To address this, we applied a multivariate hidden semi-Markov model and specified the transition matrix for the states to mimic the sequential timing of seasons.  ...  Hidden Semi Markov Models for Multiple Observation Sequences: Hidden Semi-Markov models allow an arbitrary sojourn distribution Animal Movement Model R package crawl Devin S.  ... 
doi:10.6084/m9.figshare.3468680 fatcat:zfdt266y6ndaxn2jksxhbczwbe

Toward a Machine Learning Framework for Understanding Affective Tutorial Interaction [chapter]

Joseph F. Grafsgaard, Kristy Elizabeth Boyer, James C. Lester
2012 Lecture Notes in Computer Science  
Hidden Markov modeling, in particular, is useful for encoding patterns in sequential data.  ...  This paper presents a descriptive hidden Markov model built upon facial expression data and tutorial dialogue within a task-oriented human-human tutoring corpus.  ...  Hidden Markov Modeling and Discussion A hidden Markov model (HMM) is defined by an initial probability distribution across hidden states, transition probabilities between hidden states, and emission probabilities  ... 
doi:10.1007/978-3-642-30950-2_7 fatcat:t7ekyagit5dxxmxaer3zumfehi

Detecting anomalous events at railway level crossings

Hajananth Nallaivarothayan, David Ryan, Simon Denman, Sridha Sridharan, Clinton Fookes, Andry Rakotonirainy
2013 Proceedings of the Institution of mechanical engineers. Part F, journal of rail and rapid transit  
In this work we propose using a Semi-2D Hidden Markov Model (HMM), Full Two Dimensional Hidden Markov Model and Spatial Hidden Markov Model to model the normal activities of people.  ...  The proposed approaches are evaluated using the publicly available UCSD datasets and we demonstrate improved performance using a Semi-2D Hidden Markov Model compared to other state of the art methods.  ...  They are the Semi Two Dimensional Hidden Markov Model, Full Two Dimensional Hidden Markov Model and Spatial HMM.  ... 
doi:10.1177/0954409713501296 fatcat:uv3zyi3c4zdf7hxhwasji2f2kq

Detection and Classification of Intrusions using Fusion Probability of HMM

Hemlata Sukhwani, Shwaita Kodesia, Sanjay Sharma
2014 International Journal of Computer Applications  
Here in this paper an efficient technique of identifying intrusions is implemented using hidden markov model and then classification of these intrusions is done.  ...  The methodology sis applied on KDDCup 99 dataset where the dataset is first clustered using K-means algorithms and then a number of attributes is selected which are used for the detection of intrusion  ...  INTRUSION DETECTION USING HIDDEN MARKOV MODEL In HMM, the probability with which a given sequence is generated from a model can be calculated using forwardbackward procedure and an optimal model can also  ... 
doi:10.5120/18127-9213 fatcat:idqkqypmojcv3cjtxnhugvrmpu
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