UnBBayes: Modeling Uncertainty for Plausible Reasoning in the Semantic Web

Carvalho, Rommel N.; Laskey, Kathryn Blackmond; Costa, Paulo C. G.; Ladeira, Marcelo; Santos, Laécio L.; Matsumoto, Shou

doi:10.5772/7315

Cited by 22 publications

(13 citation statements)

References 18 publications

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“…[17] This proposal uses UnBBayes [17] Figure 18 shows the probability distribution of states of node ProtocolType. These probability distribution are obtained by analyzing the experimental network traffic .ProtocolType node has three node states tcp, udp & icmp and in case of ICMP Ping Flood attack the probability of icmp protocol type is high.…”

Section: Network Traffic Analysis and User Interfacementioning

confidence: 99%

A Probabilistic Approach to Detect and Prevent Bandwidth Depletion Attacks

Boruah¹

2016

IJCA

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Capturing uncertain aspects in network security domain and their analysis by an intelligent agent is an important research domain in the current world of implementing AI in network security. When an intelligent agent is referred to, the picture that immediately comes to minds is a design that can sense the environment and take legitimate decisions by itself based upon the knowledge gathered from its environment. Hence the ability of reasoning among the agents is an important factor which governs this ability to act. There are a lots of knowledge representation schemes which are used in domain specific situations. One such situation is representing knowledge in uncertain domains. Traditional probabilistic languages lack the expressive power to handle relational domains where as classical first-order logic is sufficiently expressive, but again lacks a coherent uncertainty reasoning capability. So, an effort was made to combine both the expressiveness of first order logic as well as plausible reasoning capability of Bayesian networks in a reasoning scheme called Multi Entity Bayesian Networks (MEBN) logic. The proposal in this paper tries to detect and prevent a type of bandwidth depletion attacks (which falls in the category of DOS attacks) by filtering out the features of the network traffic relevant to these attacks and providing them as input to a MEBN model, which finally decides the fate of the traffic i.e. either it is to be allowed to enter the network or flagged as a probable threat in future and dropped.

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Section: Network Traffic Analysis and User Interfacementioning

confidence: 99%

A Probabilistic Approach to Detect and Prevent Bandwidth Depletion Attacks

Boruah¹

2016

IJCA

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“…For implementation, UnBBayes [9] have been used which provides graphical user interface to create complete probabilistic ontology on the basis of MultiEntity Bayesian Logic. It provides graphical interface for each component of MEBN like Entities, MFrag, Context node, Resident node, input node, MTheory etc.…”

Section: Methodsmentioning

confidence: 99%

“…Modeling of uncertain situation can be done using MEBN and PR-OWL, which is efficient for the expression of complex situations. UnBBayes is a graphical user interface developed in Java, which implements PR-OWL to create a probabilistic ontology [9]. It provides facilities to create and save a knowledge base as well as to generate the Situation-Specific Bayesian Network (SSBN).…”

Section: Multi-entity Bayesian Network (Mebn) [2]mentioning

confidence: 99%

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Risk Prediction for Production of an Enterprise

Ravi¹,

Singh²

2013

IJCATR

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Despite all preventive measures, there is so much possibility of risks in any project development as well as in enterprise ma nagement. There is no any standard mechanism or methodology available to assess the risks in any project or production management. Using some precautionary steps, the manager can only avoid the risks as much as he can. To address this issue, this paper presents a probabilistic risk assessment model for the production of an enterprise. For this, Multi-Entity Bayesian Network (MEBN) has been used to represent the requirements for production management as well as to assess the risks adherence in production management, where MEBN combines expressivity of first-order logic and probabilistic feature of Bayesian network. Bayesian network provides the feature to represent the probabilistic uncertainty and reasoning about probabilistic knowledge base, which is used here to represent the probable risks behind each causes of a risk. The proposed probabilistic model is discussed with the help of a case study, which is used to predict risks inherent in the production of an enterprise, which depends upon various measures like labour availability, power backup, transport availability etc.

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“…Probabilistic Web Ontology Language (PR-OWL) [11] provides uncertainty representation using OWL constructs based on Multi-Entity Bayesian Network (MEBN) logic [9] and it is successfully implemented in UnBBayes [21] graphical user interface to model a probabilistic ontology [2], [8], [13], [14], [15], [18], [24], [26], [27] and [37]. MEBN logic is an extended form of Bayesian network along with expressive power of first order logic formula, where Bayesian network will encode a set of dependent evidences in graphical form and degree of belief of evidence can be interpreted with the help of constraints specified using first-order formulas.…”

Section: Mebn and Pr-owl 20mentioning

confidence: 99%

Bayesian Network for Uncertainty Representation in Semantic Web: A Survey

Ravi¹,

Singh²

2013

IJCATR

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Bayesian network is a probabilistic model to represent uncertainty available in knowledge base and using it tremendous works have been done to prove its relevance in uncertainty representation and reasoning using Bayesian inference. Probability can be used to represent uncertainty like prediction information, situational awareness, data and knowledge fusion etc in knowledge base to implement various real life situations. Various approaches based on description logic, object oriented, entity relational, and first order logic have been tried to represent uncertainty successfully. One of them is Multi-Entity Bayesian Network (MEBN) logic to represent probabilistic information and performing knowledge fusion in ontology, which is realized using PR-OWL (Probabilistic Web Ontology Language). This paper aims at giving an overall view, the work carried out so far to represent uncertainty with the help of Bayesian Network in semantic web and a list of works done using MEBN/PR-OWL for knowledge fusion or the representation of uncertainty in semantic web.

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UnBBayes: Modeling Uncertainty for Plausible Reasoning in the Semantic Web

Cited by 22 publications

References 18 publications

A Probabilistic Approach to Detect and Prevent Bandwidth Depletion Attacks

A Probabilistic Approach to Detect and Prevent Bandwidth Depletion Attacks

Risk Prediction for Production of an Enterprise

Bayesian Network for Uncertainty Representation in Semantic Web: A Survey

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