scholarly journals The Application of Baum-Welch Algorithm in Multistep Attack

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yanxue Zhang ◽  
Dongmei Zhao ◽  
Jinxing Liu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Simulation Experiments ◽  
Forward Algorithm ◽  
Better Than

The biggest difficulty of hidden Markov model applied to multistep attack is the determination of observations. Now the research of the determination of observations is still lacking, and it shows a certain degree of subjectivity. In this regard, we integrate the attack intentions and hidden Markov model (HMM) and support a method to forecasting multistep attack based on hidden Markov model. Firstly, we train the existing hidden Markov model(s) by the Baum-Welch algorithm of HMM. Then we recognize the alert belonging to attack scenarios with the Forward algorithm of HMM. Finally, we forecast the next possible attack sequence with the Viterbi algorithm of HMM. The results of simulation experiments show that the hidden Markov models which have been trained are better than the untrained in recognition and prediction.

scholarly journals Log-Viterbi algorithm applied on second-order hidden Markov model for human activity recognition

2018 ◽  
Vol 14 (4) ◽  
pp. 155014771877254 ◽  
Author(s):  
Yang Sung-Hyun ◽  
Keshav Thapa ◽  
M Humayun Kabir ◽  
Lee Hee-Chan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Activity Recognition ◽  
Human Activities ◽  
Time Complexity ◽  
Viterbi Algorithm ◽  
Hidden Markov ◽  
Elderly Care ◽  
Second Order ◽  
Assistive Living

Recognition of human activities is getting into the limelight among researchers in the field of pervasive computing, ambient intelligence, robotic, and monitoring such as assistive living, elderly care, and health care. Many platforms, models, and algorithms have been developed and implemented to recognize the human activities. However, existing approaches suffer from low-activity accuracy and high time complexity. Therefore, we proposed probabilistic log-Viterbi algorithm on second-order hidden Markov model that facilitates our algorithm by reducing the time complexity with increased accuracy. Second-order hidden Markov model is efficient relevance between previous two activities, current activity, and current observation that incorporate more information into recognition procedure. The log-Viterbi algorithm converts the products of a large number of probabilities into additions and finds the most likely activity from observation sequence under given model. Therefore, this approach maximizes the probability of activity recognition with improved accuracy and reduced time complexity. We compared our proposed algorithm among other famous probabilistic models such as Naïve Bayes, condition random field, hidden Markov model, and hidden semi-Markov model using three datasets in the smart home environment. The recognition possibility of our proposed method is significantly better in accuracy and time complexity than early proposed method. Moreover, this improved algorithm for activity recognition is much effective for almost all the dynamic environments such as assistive living, elderly care, healthcare applications, and home automation.

scholarly journals Map representation using hidden markov models for mobile robot localization

2018 ◽  
Vol 161 ◽  
pp. 03011
Author(s):  
Jesus Savage ◽  
Oscar Fuentes ◽  
Luis Contreras ◽  
Marco Negrete
Keyword(s):  
Mobile Robot ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Robot Localization ◽  
Localization System ◽  
Mobile Robot Localization

This paper describes a map representation and localization system for a mobile robot based on Hidden Markov Models. These models are used not only to find a region where a mobile robot is, but also they find the orientation that it has. It is shown that an estimation of the region where the robot is located can be found using the Viterbi algorithm with quantized laser readings, i.e. symbol observations, of a Hidden Markov Model.

scholarly journals Hidden Markov Model

d'CARTESIAN ◽  
2015 ◽  
Vol 4 (1) ◽  
pp. 86 ◽  
Author(s):  
Kezia Tumilaar ◽  
Yohanes Langi ◽  
Altien Rindengan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Learning Problem ◽  
Model Learning ◽  
Observation Sequence ◽  
Evaluation Problem ◽  
Hidden States

Hidden Markov Models (HMM) is a stochastic model and is essentially an extension of Markov Chain. In Hidden Markov Model (HMM)  there are two types states: the observable states and the hidden states. The purpose of this research are to understand how hidden Markov model (HMM) and to understand how the solution of three basic problems on Hidden Markov Model (HMM) which consist of evaluation problem, decoding problem and learning problem.  The result of the research is hidden Markov model can be defined as . The evaluation problem or to compute probability of the observation sequence given the model P(O|) can solved  by Forward-Backward algorithm, the decoding problem or to choose hidden state sequence which is optimal can solved by Viterbi algorithm and learning problem or to estimate hidden Markov model parameter  to maximize P(O|)  can solved by Baum – Welch algorithm. From description above Hidden Markov Model  with state 3  can describe behavior  from the case studies. Key  words: Decoding Problem, Evaluation Problem, Hidden Markov Model, Learning Problem

Hidden Markov Processes: Basic Properties

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Markov Chain ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Hidden Markov ◽  
Basic Properties ◽  
Deterministic Function ◽  
Hidden Markov Processes ◽  
Chain Type

This chapter considers the basic properties of hidden Markov processes (HMPs) or hidden Markov models (HMMs), a special type of stochastic process. It begins with a discussion of three distinct types of HMMs and shows that they are all equivalent from the standpoint of their expressive power or modeling ability: Type 1 hidden Markov model, or a HMM of the deterministic function of a Markov chain type; hidden Markov model of Type 2, or a HMM of the random function of a Markov chain type; and hidden Markov model of Type 3, or a HMM of the joint Markov process type. The chapter also examines various issues related to the computation of likelihoods in a HMM before concluding with an overview of the Viterbi algorithm and the Baum–Welch algorithm.

scholarly journals Activation Detection on fMRI Time Series Using Hidden Markov Model

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Rong Duan ◽  
Hong Man
Keyword(s):  
Time Series ◽  
Brain Tumor ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Viterbi Algorithm ◽  
Hidden Markov ◽  
Gaussian Model ◽  
Model Parameters ◽  
Ratio Test ◽  
Activation Detection

This paper introduces two unsupervised learning methods for analyzing functional magnetic resonance imaging (fMRI) data based on hidden Markov model (HMM). HMM approach is focused on capturing the first-order statistical evolution among the samples of a voxel time series, and it can provide a complimentary perspective of the BOLD signals. Two-state HMM is created for each voxel, and the model parameters are estimated from the voxel time series and the stimulus paradigm. Two different activation detection methods are presented in this paper. The first method is based on the likelihood and likelihood-ratio test, in which an additional Gaussian model is used to enhance the contrast of the HMM likelihood map. The second method is based on certain distance measures between the two state distributions, in which the most likely HMM state sequence is estimated through the Viterbi algorithm. The distance between the on-state and off-state distributions is measured either through a t-test, or using the Kullback-Leibler distance (KLD). Experimental results on both normal subject and brain tumor subject are presented. HMM approach appears to be more robust in detecting the supplemental active voxels comparing with SPM, especially for brain tumor subject.

scholarly journals A Hidden Markov Model for Forecasting Rainfall Data Availability at the Weather Station in West Sumatra

2020 ◽  
Vol 5 (2) ◽  
pp. 34 ◽  
Author(s):  
Rahmawati Ramadhan ◽  
Dodi Devianto
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Viterbi Algorithm ◽  
Hidden Markov ◽  
Secondary Data ◽  
Meteorological Station ◽  
Rainfall Data ◽  
Data Availability ◽  
Maritime Continent ◽  
West Sumatra

Indonesia is a maritime continent in Southeast Asian, laying between Indian Ocean and Pacific Ocean. This position intensely affects the level of rainfall in Indonesia, especially West Sumatra. The availability of rainfall data can form a Markov chain which its state is not able to be observed directly (hidden), is called the Hidden Markov Model (HMM). The purposes of this research are to predict the hidden state of the availability of rainfall data using decoding problems and to find the best state sequence (optimal) by using Viterbi Algorithm, and also to predict probability for the availability of rainfall data in the future by using the Baum Welch Algorithm in the Hidden Markov Model. This research uses secondary data with a period of one day from the availability of rainfall data at the Minangkabau Meteorological Station, Padang Pariaman Climatology Station, and Silaing Bawah Geophysics Station from January 2018 to July 2019. The results of the prediction show that the Hidden Markov Model can be used to predict the probability of rainfall data availability. The results for the availability of the highest rainfall data for one day ahead is at the Padang Pariaman Climatology Station, with a probability of 0.36, followed by Minangkabau Meteorological Station is 0.35, and Silaing Bawah Geophysics station is 0.29. The result has shown for the next one day period the probability of rainfall data available from the three stations will be available following the Viterbi algorithm.

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