scholarly journals Network Security Situation Assessment Model Based on Extended Hidden Markov

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yiwei Liao ◽  
Guosheng Zhao ◽  
Jian Wang ◽  
Shu Li
Keyword(s):  
Network Security ◽  
Probability Distribution ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Transition Matrix ◽  
State Transition ◽  
Hidden Markov ◽  
State Transition Matrix ◽  
State Probability ◽  
Risk Loss

A network security situation assessment system based on the extended hidden Markov model is designed in this paper. Firstly, the standard hidden Markov model is expanded from five-tuple to seven-tuple, and two parameters of network defense efficiency and risk loss vector are added so that the model can describe network security situation more completely. Then, an initial algorithm of state transition matrix was defined, observation vectors were extracted from the fusion of various system security detection data, the network state transition matrix was created and modified by the observation vectors, and a solution procedure of the hidden state probability distribution sequence based on extended hidden Markov model was derived. Finally, a method of calculating risk loss vector according to the international definition was designed and the current network risk value was calculated by the hidden state probability distribution; then the global security situation was assessed. The experiment showed that the model satisfied practical applications and the assessment result is accurate and effective.

A model based on hidden graphic evaluation and review technique network to evaluate reliability and lifetime of multi-state systems

2018 ◽  
Vol 233 (3) ◽  
pp. 369-378 ◽  
Author(s):  
Wenjie Dong ◽  
Sifeng Liu ◽  
Zhigeng Fang ◽  
Yingsai Cao ◽  
Ye Ding
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Network Model ◽  
State Transition ◽  
Likelihood Function ◽  
Hidden Markov ◽  
Reliability Assessment ◽  
Assessment Method ◽  
Performance Degradation ◽  
State System

The essence of multi-state system performance degradation is a process of deteriorating state transition. On the basis of hidden Markov model and graphic evaluation and review technique network, this article proposes a new reliability assessment method called hidden graphic evaluation and review technique network model for multi-state system. Specifically, nodes in graphic evaluation and review technique network represent hidden states of a system at different deteriorating times, and they can be expanded through a series of observable sequences. Baum–Welch algorithm in hidden Markov model is introduced to train parameters and when logarithmic likelihood function of the output reaches convergent, we can estimate the most probable output state and obtain the state transition probability eventually. Suppose performance degradation amount between different nodes follows gamma distribution, a method of improved maximum likelihood function is introduced to estimate parameters. According to signal flow graph theory and Mason formula, equivalent transfer function from the initial node to any other nodes can be obtained, then expectation and variance of performance degradation amount can be presented. In the real case study, we compare the reliability assessment method proposed in this article with other two traditional methods, which show the rationality of hidden graphic evaluation and review technique network model.

Equivalence of Linear Boltzmann Chains and Hidden Markov Models

1996 ◽  
Vol 8 (1) ◽  
pp. 178-181 ◽  
Author(s):  
David J. C. MacKay
Keyword(s):  
Probability Distribution ◽  
Markov Model ◽  
Hidden Markov Models ◽  
State Transition ◽  
Markov Models ◽  
Hidden Markov ◽  
Simple Condition ◽  
Transition Energies ◽  
State Sequence ◽  
The Relationship

Several authors have studied the relationship between hidden Markov models and “Boltzmann chains” with a linear or “time-sliced” architecture. Boltzmann chains model sequences of states by defining state-state transition energies instead of probabilities. In this note I demonstrate that under the simple condition that the state sequence has a mandatory end state, the probability distribution assigned by a strictly linear Boltzmann chain is identical to that assigned by a hidden Markov model.

scholarly journals Understanding the Complexity of Regional Innovation Capacity Dynamics in China: From the Perspective of Hidden Markov Model

2021 ◽  
Vol 13 (4) ◽  
pp. 1658
Author(s):  
Shuai Liu ◽  
Xiao-Yu Xu ◽  
Kai Zhao ◽  
Li-Ming Xiao ◽  
Qi Li
Keyword(s):  
Intellectual Property ◽  
Markov Model ◽  
Hidden Markov Model ◽  
State Transition ◽  
Hidden Markov ◽  
High State ◽  
Regional Innovation ◽  
Heterogeneous Effects ◽  
Innovation Capacity ◽  
Positive Impacts

This study aimed to explore the state transition of regional innovation capacity (RIC) and analyze the heterogeneous effects of determinants in an innovative subject and environment of RIC state transition based on the data collected from 30 provinces in China during 2000–2017. By applying a hidden Markov model (HMM), this study identified three RIC states: low, medium, and high. The results suggested that (1) the overall state of RIC rapidly improved but with a significant disparity across regions in China; (2) the lock-in effect of RIC is most significant in regions with a medium state, while the enterprise-dominated mode of regional innovation helps RIC transition from a medium state to a high state or to remain in a high state; and (3) the interaction and collaboration between universities and enterprises in a region can stimulate RIC to higher states for all regions. Intellectual property administrative protection exerts positive impacts on RIC transitions to higher states. Intellectual property judicial protection only exerts positive impacts on an RIC’s transition from a medium state to a high state or remaining in a high state, while these positive impacts are not significant when RIC is in a low state. Highlighting the dynamic nature of RIC evolution and the heterogeneity of determinants affecting RIC state transition, the findings provide policymakers a roadmap to identify RIC states and make precise policies based on the current RIC state.

scholarly journals Hidden Markov Model Application to Transfer The Trader Online Forex Brokers

CAUCHY ◽  
2012 ◽  
Vol 2 (2) ◽  
pp. 66
Author(s):  
Farida Suharleni ◽  
Agus Widodo ◽  
Endang Wahyu H
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Transition Matrix ◽  
Viterbi Algorithm ◽  
Hidden Markov ◽  
Initial State ◽  
Simulation Data ◽  
Observation Matrix ◽  
Probability Of Transition ◽  
Construction Model

<p>Hidden Markov Model is elaboration of Markov chain, which is applicable to cases that can’t directly observe. In this research, Hidden Markov Model is used to know trader’s transition to broker forex online. In Hidden Markov Model, observed state is observable part and hidden state is hidden part. Hidden Markov Model allows modeling system that contains interrelated observed state and hidden state. As observed state in trader’s transition to broker forex online is category 1, category 2, category 3, category 4, category 5 by condition of every broker forex online, whereas as hidden state is broker forex online Marketiva, Masterforex, Instaforex, FBS and Others. First step on application of Hidden Markov Model in this research is making construction model by making a probability of transition matrix (A) from every broker forex online. Next step is making a probability of observation matrix (B) by making conditional probability of five categories, that is category 1, category 2, category 3, category 4, category 5 by condition of every broker forex online and also need to determine an initial state probability (π) from every broker forex online. The last step is using Viterbi algorithm to find hidden state sequences that is broker forex online sequences which is the most possible based on model and observed state that is the five categories. Application of Hidden Markov Model is done by making program with Viterbi algorithm using Delphi 7.0 software with observed state based on simulation data. Example: By the number of observation T = 5 and observed state sequences O = (2,4,3,5,1) is found hidden state sequences which the most possible with observed state O as following : where X1 = FBS, X2 = Masterforex, X3 = Marketiva, X4 = Others, and X5 = Instaforex.</p>

scholarly journals Bearing Diagnostics of Hydro Power Plants Using Wavelet Packet Transform and a Hidden Markov Model with Orbit Curves

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Gabriel Pino ◽  
José Roberto Ribas ◽  
Luciana Fernandes Guimarães
Keyword(s):  
Probability Distribution ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Power Plants ◽  
Hidden Markov ◽  
Wavelet Packet ◽  
Transition Probability ◽  
Peak Load ◽  
Wavelet Packet Transform ◽  
Hydro Power

The contribution of a medium-sized hydro power plant to the power grid can be either at base load or at peak load. When the latter is the most common operation mode, it increases the start and stop frequency, intensifying the hydro turbine components’ degradation, such as the guide bearings. This happens due to more frequent operation in transient states, which means being outside the service point of the machines’ nominal condition, consisting of speed, flow, and gross head. Such transient state operation increases the runner bearings’ mechanical vibration. The readings are acquired during the runner start-ups and filtered by a DC component mean value and a wavelet packet transform. The filtered series are used to estimate the relationship between the maximum orbit curve displacement and the accumulated operating hours. The estimated equation associated with the ISO 7919-5 vibration standards establishes the sojourn times of the degradation states, sufficient to obtain the transition probability distribution. Thereafter, a triangular probability function is used to determine the observation probability distribution in each state. Both matrices are inputs required by a hidden Markov model aiming to simulate the equipment deterioration process, given a sequence of maximum orbit curve displacements.

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