scholarly journals DESIGN OF KNOWLEDGE BASE FOR CYBER SECURITY SYSTEMS ON THE BASIS OF SUBJECT IDENTIFICATION METHOD

2020 ◽  
Vol 4 (8) ◽  
pp. 135-148
Author(s):  
Valerii Lakhno ◽  
Dmytro Kasatkin ◽  
Maksym Misiura ◽  
Borys Husiev
Keyword(s):  
Expert System ◽  
Knowledge Base ◽  
Cyber Security ◽  
Subject Area ◽  
Security Systems ◽  
Cyber Threat ◽  
Fuzzy Topological Space ◽  
Subject Identification ◽  
Objects Of Knowledge ◽  
The Eu

The article presents the results of research performed in the process of designing an expert system (ES) designed to assess the threats to information security (IS) of critical information facilities (CIF). The approach to designing of expert system on the basis of syllogisms and logic of predicates, and also a method of meaningful identification of objects of knowledge base (KB) is offered. The essence of the method is that each object of the database of the projected EU, is matched by a tuple of keywords (ToK), the significance of which is determined by experts. Thus, each database object is placed in accordance with the element of the finite fuzzy topological space of the database objects. Meaningful identification takes place on the distance between the objects of the database. The approach proposed in the work, in comparison with the decisions of other authors, has a number of advantages. Namely, it allows: to model different variants of cyber threat scenarios for CIF and their consequences; determine the contribution of each of the factors or components of the architecture of the IS CIF to the overall picture of the probability of a cyber threat to the CIF; model the interaction of all IS factors and, if necessary, visualize this interaction; calculate and further rank the values of cyber threat probabilities for CIF for specific threat scenarios; automate the processes of threat modeling through the use of developed software and significantly reduce the time for audit of threats. It is shown that the use of the method of meaningful identification allows to increase the adequacy of the models of the selected subject area, as well as to prevent erroneous introduction of the same judgments of experts and goals in the EU database, in particular by combining hierarchies of goals formed by different expert groups. It is shown that the method can also be used to find the goals of the hierarchy, the exact wording of which, according to keywords, is unknown.

scholarly journals USING THE ONTOLOGY FOR CREATING KNOWLEDGE BASE OF THE EXPERT SYSTEM OF THE SUBJECT AREA OF THE STORING OF VEGETABLES AND FRUITS BY THE TECHNOLOGY «ULO»

2020 ◽  
Author(s):  
Ye. Proskurka ◽  
S. Kyrychuk
Keyword(s):  
Expert System ◽  
Knowledge Base ◽  
Subject Area ◽  
Automation System ◽  
The Subject ◽  
Vegetables And Fruits

Using the ontology for creating the knowledge base of the expert system of the subject area of the storing of vegetables and fruits by the technology «ULO» is reviewed in this article. The expert system, which will be created with CLIPS language, will be used for manage of the automation system of the warehouses for the storing of vegetables and fruits by the technology «ULO».

An expert system for real-time well field management

2012 ◽  
Vol 12 (5) ◽  
pp. 699-706 ◽  
Author(s):  
B. S. Marti ◽  
G. Bauser ◽  
F. Stauffer ◽  
U. Kuhlmann ◽  
H.-P. Kaiser ◽  
...  
Keyword(s):  
Drinking Water ◽  
Expert System ◽  
Knowledge Base ◽  
Real Time ◽  
Urban Areas ◽  
Industrial Sites ◽  
Water Wells ◽  
Well Field ◽  
Field Management ◽  
Historical Management

Well field management in urban areas faces challenges such as pollution from old waste deposits and former industrial sites, pollution from chemical accidents along transport lines or in industry, or diffuse pollution from leaking sewers. One possibility to protect the drinking water of a well field is the maintenance of a hydraulic barrier between the potentially polluted and the clean water. An example is the Hardhof well field in Zurich, Switzerland. This paper presents the methodology for a simple and fast expert system (ES), applies it to the Hardhof well field, and compares its performance to the historical management method of the Hardhof well field. Although the ES is quite simplistic it considerably improves the water quality in the drinking water wells. The ES knowledge base is crucial for successful management application. Therefore, a periodic update of the knowledge base is suggested for the real-time application of the ES.

Creating a Knowledge Base for a Mivar Expert System for the Diagnosis of Diabetes Mellitus

2021 ◽  
Vol 54 (6) ◽  
pp. 421-424
Author(s):  
H. Kim ◽  
D. A. Chuvikov ◽  
D. V. Aladin ◽  
O. O. Varlamov ◽  
L. E. Adamova ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Expert System ◽  
Knowledge Base ◽  
Diagnosis Of Diabetes Mellitus

scholarly journals Diagnostics and Prognostics of Energy Conversion Processes via Knowledge-Based Systems

Proceedings ◽  
2020 ◽  
Vol 58 (1) ◽  
pp. 1
Author(s):  
Roberto Melli ◽  
Enrico Sciubba
Keyword(s):  
Expert System ◽  
Knowledge Base ◽  
Real Time ◽  
System Analysis ◽  
Intelligent System ◽  
Time Derivative ◽  
Time History ◽  
Control Procedure ◽  
Time Data ◽  
Ongoing Research

This paper presents a critical and analytical description of an ongoing research program aimed at the implementation of an expert system capable of monitoring, through an Intelligent Health Control procedure, the instantaneous performance of a cogeneration plant. The expert system is implemented in the CLIPS environment and is denominated PROMISA as the acronym for Prognostic Module for Intelligent System Analysis. It generates, in real time and in a form directly useful to the plant manager, information on the existence and severity of faults, forecasts on the future time history of both detected and likely faults, and suggestions on how to control the problem. The expert procedure, working where and if necessary with the support of a process simulator, derives from the available real-time data a list of selected performance indicators for each plant component. For a set of faults, pre-defined with the help of the plant operator (Domain Expert), proper rules are defined in order to establish whether the component is working correctly; in several instances, since one single failure (symptom) can originate from more than one fault (cause), complex sets of rules expressing the combination of multiple indices have been introduced in the knowledge base as well. Creeping faults are detected by analyzing the trend of the variation of an indicator over a pre-assigned interval of time. Whenever the value of this ‘‘discrete time derivative’’ becomes ‘‘high’’ with respect to a specified limit value, a ‘‘latent creeping fault’’ condition is prognosticated. The expert system architecture is based on an object-oriented paradigm. The knowledge base (facts and rules) is clustered—the chunks of knowledge pertain to individual components. A graphic user interface (GUI) allows the user to interrogate PROMISA about its rules, procedures, classes and objects, and about its inference path. The paper also presents the results of some simulation tests.

An Integrated System for Design of Mechanisms by an Expert System—DOMES: Theory

1990 ◽  
Vol 112 (4) ◽  
pp. 488-493 ◽  
Author(s):  
B. Yang ◽  
P. Datseris ◽  
U. Datta ◽  
J. Kowalski
Keyword(s):  
Expert System ◽  
Knowledge Base ◽  
Design Problem ◽  
Integrated System ◽  
Structural Constraints ◽  
Design Engineers ◽  
Engine Design ◽  
Synthesis Of Mechanisms ◽  
System 2 ◽  
Human Designer

Methodologies have been developed and implemented in LISP and OPS-5 languages which address type synthesis of mechanisms. Graph theory and separation of structure from function concepts have been integrated into an expert system called DOMES (Design Of Mechanism by an Expert System) to effectively implement the following three activities: (1) enumeration of all nonisomorphic labelled graphs; (2) identification of those graphs which satisfy structural constraints; (3) sketching of a mechanism corresponding to a given graph. Developed theories and algorithms are applied to a Robot Gripper design [19] and a Variable Stroke Piston Engine design [16]. The results from these two applications indicate that the automated techniques effectively identify all previously obtained solutions via manual techniques. Additional solutions are also identified and several errors of the manual process are detected. The developed methodologies and software appear to perform a complete and unbiased search of all possible candidate designs and are not prone to the errors of the manual process. Other important features of DOMES are: (1) it can learn and reason, by analogy, about a new design problem based on its experience of the problems previously solved by the system; (2) it has the capability to incrementally expand its knowledge base of rejection criteria by converting into LISP code information obtained through a query-based interactive session with a human designer; (3) it can select the set of rejection criteria relevant to a design problem from its knowledge base of rejection criteria. These procedures could become a powerful tool for design engineers, especially at the conceptual stage of design.

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