scholarly journals Attack Impact Discovery and Recovery with Dynamic Bayesian Networks

2019 ◽  
Vol 8 (S1) ◽  
pp. 74-79
Author(s):  
T. Rajeshwari ◽  
C. Thangamani
Keyword(s):  
Bayesian Network ◽  
Impact Analysis ◽  
Probabilistic Reasoning ◽  
Belief Propagation ◽  
Dynamic Bayesian Network ◽  
Data Communication ◽  
Dynamic Bayesian Networks ◽  
Attack Detection ◽  
Object State ◽  
Dependency Network

The network attacks are discovered using the Intrusion Detection Systems (IDS). Anomaly, signature and compound attack detection schemes are employed to fetch malicious data traffic activities. The attack impact analysis operations are carried out to discover the malicious objects in the network. The system objects are contaminated with process injection or hijacking. The attack ramification model discovers the contaminated objects. The dependency networks are built to model the information flow over the objects in the network. The dependency network is a directed graph built to indicate the data communication over the objects. The attack ramification models are designed with intrusion root information. The attack ramifications are applied to identify the malicious objects and contaminated objects. The attack ramifications are discovered with the information flows from the attack sources. The Attack Ramification with Bayesian Network (ARBN) scheme discovers the attack impact without the knowledge of the intrusion root. The probabilistic reasoning approach is employed to analyze the object state for ramification process. The objects lifetime is divided into temporal slices to verify the object state changes. The system call traces and object slices are correlated to construct the Temporal Dependency Network (TDN). The Bayesian Network (BN) is constructed with the uncertain data communication activities extracted from the TDN. The attack impact is fetched with loopy belief propagation on the BN model. The network security system is built with attack impact analysis and recovery operations. Live traffic data analysis process is carried out with improved temporal slicing concepts. Attack Ramification and Recovery with Dynamic Bayesian Network (ARRDBN) is built to support attack impact analysis and recovery tasks. The unsupervised attack handling mechanism automatically discovers the feasible solution for the associated attacks.

scholarly journals A dynamic Bayesian network approach for prognosis computations on discrete state systems

2017 ◽  
Vol 231 (5) ◽  
pp. 516-533 ◽  
Author(s):  
Josquin Foulliaron ◽  
Laurent Bouillaut ◽  
Patrice Aknin ◽  
Anne Barros
Keyword(s):  
Complex Systems ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Multicomponent System ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Discrete State ◽  
Virtual Maintenance

The maintenance optimization of complex systems is a key question. One important objective is to be able to anticipate future maintenance actions required to optimize the logistic and future investments. That is why, over the past few years, the predictive maintenance approaches have been an expanding area of research. They rely on the concept of prognosis. Many papers have shown how dynamic Bayesian networks can be relevant to represent multicomponent complex systems and carry out reliability studies. The diagnosis and maintenance group from French institute of science and technology for transport, development and networks (IFSTTAR) developed a model (VirMaLab: Virtual Maintenance Laboratory) based on dynamic Bayesian networks in order to model a multicomponent system with its degradation dynamic and its diagnosis and maintenance processes. Its main purpose is to model a maintenance policy to be able to optimize the maintenance parameters due to the use of dynamic Bayesian networks. A discrete state-space system is considered, periodically observable through a diagnosis process. Such systems are common in railway or road infrastructure fields. This article presents a prognosis algorithm whose purpose is to compute the remaining useful life of the system and update this estimation each time a new diagnosis is available. Then, a representation of this algorithm is given as a dynamic Bayesian network in order to be next integrated into the Virtual Maintenance Laboratory model to include the set of predictive maintenance policies. Inference computation questions on the considered dynamic Bayesian networks will be discussed. Finally, an application on simulated data will be presented.

Operation-oriented reliability and availability evaluation for onboard high-speed train control system with dynamic Bayesian network

2018 ◽  
Vol 233 (3) ◽  
pp. 455-469 ◽  
Author(s):  
Lei Jiang ◽  
Yiliu Liu ◽  
Xiaomin Wang ◽  
Mary Ann Lundteigen
Keyword(s):  
Control System ◽  
Bayesian Network ◽  
High Speed ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
High Speed Train ◽  
Train Control ◽  
Train Control System ◽  
Onboard System ◽  
Reliability And Availability

The reliability and availability of the onboard high-speed train control system are important to guarantee operational efficiency and railway safety. Failures occurring in the onboard system may result in serious accidents. In the analysis of the effects of failure, it is significant to consider the operation of an onboard system. This article presents a systemic approach to evaluate the reliability and availability for the onboard system based on dynamic Bayesian network, with taking into account dynamic failure behaviors, imperfect coverage factors, and temporal effects in the operational phase. The case studies are presented and compared for onboard systems with different redundant strategies, that is, the triple modular redundancy, hot spare double dual, and cold spare double dual. Dynamic fault trees of the three kinds of onboard system are constructed and mapped into dynamic Bayesian networks. The forward and backward inferences are conducted not only to evaluate the reliability and availability but also to recognize the vulnerabilities of the onboard systems. A sensitivity analysis is carried out for evaluating the effects of failure rates subject to uncertainties. To improve the reliability and availability, the recovery mechanism should be paid more attention. Finally, the proposed approach is validated with the field data from one railway bureau in China and some industrial impacts are provided.

scholarly journals The engineering skills training process modeling using dynamic bayesian nets

2021 ◽  
pp. 87-96
Author(s):  
Andrey Chukhray ◽  
Olena Havrylenko
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Learning Process ◽  
Probabilistic Inference ◽  
Dynamic Bayesian Network ◽  
Computer Training ◽  
Skills Training ◽  
Dynamic Bayesian Networks ◽  
Competence Level ◽  
Intelligent Computer

The subject of research in the article is the process of intelligent computer training in engineering skills. The aim is to model the process of teaching engineering skills in intelligent computer training programs through dynamic Bayesian networks. Objectives: To propose an approach to modeling the process of teaching engineering skills. To assess the student competence level by considering the algorithms development skills in engineering tasks and the algorithms implementation ability. To create a dynamic Bayesian network structure for the learning process. To select values for conditional probability tables. To solve the problems of filtering, forecasting, and retrospective analysis. To simulate the developed dynamic Bayesian network using a special Genie 2.0-environment. The methods used are probability theory and inference methods in Bayesian networks. The following results are obtained: the development of a dynamic Bayesian network for the educational process based on the solution of engineering problems is presented. Mathematical calculations for probabilistic inference problems such as filtering, forecasting, and smoothing are considered. The solution of the filtering problem makes it possible to assess the current level of the student's competence after obtaining the latest probabilities of the development of the algorithm and its numerical calculations of the task. The probability distribution of the learning process model is predicted. The number of additional iterations required to achieve the required competence level was estimated. The retrospective analysis allows getting a smoothed assessment of the competence level, which was obtained after the task's previous instance completion and after the computation of new additional probabilities characterizing the two checkpoints implementation. The solution of the described probabilistic inference problems makes it possible to provide correct information about the learning process for intelligent computer training systems. It helps to get proper feedback and to track the student's competence level. The developed technique of the kernel of probabilistic inference can be used as the decision-making model basis for an automated training process. The scientific novelty lies in the fact that dynamic Bayesian networks are applied to a new class of problems related to the simulation of engineering skills training in the process of performing algorithmic tasks.

Scene Retrieval Approach Based Dynamic Bayesian Networks

2011 ◽  
Vol 135-136 ◽  
pp. 873-878
Author(s):  
Qin Kun Xiao ◽  
Xiao Xia Hu ◽  
Song Gao
Keyword(s):  
Bayesian Networks ◽  
Image Retrieval ◽  
Bayesian Network ◽  
Prior Knowledge ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
Training Phase ◽  
New Materials ◽  
Feedback Information ◽  
Retrieval Accuracy

In this paper, a new materials image retrieval methodology based on dynamic Bayesian network (DBN) is proposed to overcome certain drawbacks inherited in previously proposed CBIR methods. Firstly, the DBN structure and initial parameters is constructed according to the prior knowledge. Secondly, a training phase is conducted for updating DBN parameters through using the feedback information. The training cycles can be stopped until the retrieval accuracy is adequately high. Experimental results on 10,000 images demonstrate the effectiveness of the proposed methodology.

A Hybrid Personalized Recommendation Method Based on Dynamic Bayesian Network

2011 ◽  
Vol 268-270 ◽  
pp. 1082-1085 ◽  
Author(s):  
Yu Long Ying ◽  
Quan Ping Hua
Keyword(s):  
Bayesian Network ◽  
Electronic Commerce ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Rapid Growth ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
Personalized Recommendation ◽  
Data Sparsity

With the rapid growth and wide application of electronic commerce, lots of information comes forth to people. However, our experiences and knowledge often do not enough to process the vast amount of information. The problem of obtaining useful information becomes more and more serious. To deal with the problem, the personalized service and recommender system play a more important role in many fields and collaborative filtering is one of the most successful technologies in recommender systems. However, with the tremendous growth in the amount of items and users, most collaborative filtering algorithms suffer from data sparsity which leads to inaccuracy of recommendation. Aiming at the problem of data sparsity, a hybrid personalized recommendation method based on dynamic Bayesian networks is presented. This method uses the dynamic Bayesian network technique to fill the vacant ratings at first, and then employs the user-based collaborative filtering to produce recommendations.

Multi-state reliability assessment for hydraulic lifting system based on the theory of dynamic Bayesian networks

2016 ◽  
Vol 230 (6) ◽  
pp. 533-544 ◽  
Author(s):  
Chun Su ◽  
Ning Lin ◽  
Yequn Fu
Keyword(s):  
Bayesian Network ◽  
Failure Modes ◽  
Reliability Assessment ◽  
Mechanical Systems ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
Effect Analysis ◽  
Analysis And Design ◽  
Reliability Modelling ◽  
Lifting System

Mechanical systems and their components usually have multiple failure modes and different performance states. Most existing system reliability modelling theories are developed on the basis of binary logic, which lack sufficient ability to describe the above phenomena. In this article, dynamic Bayesian network theory is employed to evaluate the multi-state reliability of a hydraulic lifting system. First, failure mode and effect analysis and structural analysis and design technique are comprehensively applied to analyse the functionalities and failure modes of the components. Afterwards, the time factor is integrated into the model by considering the state transition of the components. In this way, the multi-state reliability model of the system is established by dynamic Bayesian network. The reliability assessment and diagnostic analysis are performed by taking advantage of the dynamic Bayesian network’s bi-directional reasoning ability, and the results are in good agreement with actual situation. It shows that the proposed approach is effective and convenient for multi-state reliability modelling and analysis for mechanical systems.

scholarly journals Gait Type Analysis Using Dynamic Bayesian Networks

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3329 ◽  
Author(s):  
Patrick Kozlow ◽  
Noor Abid ◽  
Svetlana Yanushkevich
Keyword(s):  
Bayesian Network ◽  
Recognition Rate ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
The Body ◽  
Microsoft Kinect ◽  
Gait Abnormality ◽  
Joint Coordinates ◽  
Type Classification ◽  
Body Joint

This paper focuses on gait abnormality type identification—specifically, recognizing antalgic gait. Through experimentation, we demonstrate that detecting an individual’s gait type is a viable biometric that can be used along with other common biometrics for applications such as forensics. To classify gait, the gait data is represented by coordinates that reflect the body joint coordinates obtained using a Microsoft Kinect v2 system. Features such as cadence, stride length, and other various joint angles are extracted from the input data. Using approaches such as the dynamic Bayesian network, the obtained features are used to model as well as perform gait type classification. The proposed approach is compared with other classification techniques and experimental results reveal that it is capable of obtaining a 88.68% recognition rate. The results illustrate the potential of using a dynamic Bayesian network for gait abnormality classification.

Time and Spatial Modeling of Wear Mechanisms in Nuclear Steam Turbine Blades Using Dynamic Bayesian Networks

2018 ◽  
Author(s):  
David A. Quintanar-Gago ◽  
Pamela F. Nelson ◽  
Ángeles Díaz-Sánchez
Keyword(s):  
Bayesian Network ◽  
Failure Mode ◽  
Wear Mechanisms ◽  
Failure Modes ◽  
Turbine Blades ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
Time Dependent ◽  
Prototype Model ◽  
Quantitative Methodology

This paper describes a quantitative methodology to estimate the probability of blade failure modes resulting from typical wear mechanisms in nuclear turbines, which can be used to optimize maintenance. The approach used to model time and spatial dependence of wear mechanisms that affect blades involves the coupling of a Static Bayesian Network to a Dynamic Bayesian Network. This prototype model has been designed to use conditional and time dependent Weibull-like failure rates that can be computed from reliability data bases (failure times and modes, associated causes, row and blade part that failed) to quantify Markov matrixes contained within dynamic nodes. The model can be used to make inferences such as the most probable causes of failure in a row and blade part, and visualize the probability as a function of time. It can be also used to determine the riskier location given evidence such as failure mode or the wear mechanisms involved. Also, maintenance tasks acting over time dependent failure functions have been implemented to exemplify the effect of perfect and three kinds of imperfect actions and how they affect the mechanisms and failure mode evolution, given the conditional dependences among them.

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