The Development of Formal Model for Research of Information System’s Data Mining Programs

According to statistics, the number of samples of new attacks against information systems is increasing due to inability to detect new patterns and the lack of modern attack detection systems. To resolve this issue, we implement intelligent data analysis to detect attacks. There are many data mining programs, so it is important to choose the best program. The authors investigate the problem of information security from the viewpoint of new attacks, the programs for mining of information system’s events. The criteria for their evaluation have been formulated. Besides, the formal model for the study of programs for mining of information system’s events has been developed. The proposed formal model will help to choose the best program for event mining. If the requirements for the analyzed programs change, then changing the values in the best vector can also become a right solution. Thus, the developed formal evaluation model is universal and effective.

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Online Data Mining

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch007 ◽

2008 ◽

pp. 75-83

Author(s):

He´ctor Oscar Nigro ◽

Sandra Elizabeth González Císaro

Keyword(s):

Data Mining ◽

Data Analysis ◽

Online Analytical Processing ◽

Intelligent Data Analysis ◽

Online Data ◽

Analytical Processing

Several approaches for intelligent data analysis are not only available but also tried and tested. Online analytical processing (OLAP) and data mining represent two of the most important approaches. They mainly emphasize different aspects of the data and allow deriving of different kinds of information. So far, these approaches have mainly been used in isolation (Schwarz, 2002).

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Online Data Mining

Encyclopedia of Database Technologies and Applications ◽

10.4018/978-1-59140-560-3.ch072 ◽

2005 ◽

pp. 427-432

Author(s):

Héctor Oscar Nigro ◽

Sandra Elizabeth González Císaro

Keyword(s):

Data Mining ◽

Data Analysis ◽

Online Analytical Processing ◽

Intelligent Data Analysis ◽

Online Data ◽

Analytical Processing

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CONSTRUCTION OF ATTACK DETECTION SYSTEMS IN INFORMATION NETWORKS ON NEURAL NETWORK STRUCTURES

Cybersecurity Education Science Technique ◽

10.28925/2663-4023.2020.10.169183 ◽

2020 ◽

Vol 2 (10) ◽

pp. 169-183

Author(s):

Serhii Tolіupa ◽

Oleksandr Pliushch ◽

Ivan Parkhomenko

Keyword(s):

Neural Network ◽

Information Systems ◽

Detection System ◽

Attack Detection ◽

Cyber Attacks ◽

Network Structures ◽

Network Attack ◽

Detection Systems ◽

Protection Mechanisms ◽

Software Prototype

Systems for detecting network intrusions and detecting signs of attacks on information systems have long been used as one of the necessary lines of defense of information systems. Today, intrusion and attack detection systems are usually software or hardware-software solutions that automate the process of monitoring events occurring in an information system or network, as well as independently analyze these events in search of signs of security problems. As the number of different types and ways of organizing unauthorized intrusions into foreign networks has increased significantly in recent years, attack detection systems (ATS) have become a necessary component of the security infrastructure of most organizations. The article proposes a software prototype of a network attack detection system based on selected methods of data mining and neural network structures. The conducted experimental researches confirm efficiency of the created model of detection for protection of an information network. Experiments with a software prototype showed high quality detection of network attacks based on neural network structures and methods of intelligent data distribution. The state of protection of information systems to counter cyber attacks is analyzed, which made it possible to draw conclusions that to ensure the security of cyberspace it is necessary to implement a set of systems and protection mechanisms, namely systems: delimitation of user access; firewall; cryptographic protection of information; virtual private networks; anti-virus protection of ITS elements; detection and prevention of intrusions; authentication, authorization and audit; data loss prevention; security and event management; security management.

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CONSTRUCTION OF SYSTEMS OF DETECTION OF INVASIONS INTO THE INFORMATI TON AND TELECOMMUNICATIONS NETWORK ON THE BASIS OF METHODS OF INTELLECTUAL DISTRIBUTION OF DATA

Collection of scientific works of the Military Institute of Kyiv National Taras Shevchenko University ◽

10.17721/2519-481x/2020/68-09 ◽

2020 ◽

pp. 80-89

Author(s):

S. Toliupa ◽

O. Pliushch ◽

I. Parhomenko

Keyword(s):

Data Mining ◽

Experimental Research ◽

Intelligent Systems ◽

Detection System ◽

Attack Detection ◽

Information Network ◽

Distributed Information ◽

Network Attack ◽

Detection Model ◽

Detection Systems

The article proposes a combinatorial construction of a network attack detection system based on selected methods of data mining and conducts experimental research that confirms the effectiveness of the created detection model to protect the distributed information network. Experiments with a software prototype showed the high quality of detection of network attacks and proved the correctness of the choice of methods of data mining and the applicability of the developed techniques. The state of security of information and telecommunication systems against cyberattacks is analyzed, which allowed to draw conclusions that to ensure the security of cyberspace it is necessary to implement a set of systems and protection mechanisms, namely systems: delimitation of user access; firewall; cryptographic protection of information; virtual private networks; anti-virus protection of ITS elements; detection and prevention of intrusions; authentication, authorization and audit; data loss prevention; security and event management; security management. An analysis of publications of domestic and foreign experts, which summarizes: experience in building attack detection systems, their disadvantages and advantages; of attack and intrusion detection systems based on the use of intelligent systems. Based on the results of the review, proposals were formed on: construction of network attack detection systems on the basis of selected methods of data mining and experimental research, which confirms the effectiveness of the created detection model for the protection of the distributed information network.

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Biomedical Data Mining

Methods of Information in Medicine ◽

10.1055/s-0038-1625129 ◽

2009 ◽

Vol 48 (03) ◽

pp. 225-228 ◽

Cited By ~ 5

Author(s):

C. Combi ◽

A. Tucker ◽

N. Peek

Keyword(s):

Data Mining ◽

Data Analysis ◽

Image Data ◽

Special Topic ◽

Biomedical Data ◽

Intelligent Data Analysis ◽

Temporal Abstraction ◽

Computationally Efficient ◽

Search Spaces ◽

Clinical Databases

Summary Objective: To introduce the special topic of Methods of Information in Medicine on data mining in biomedicine, with selected papers from two workshops on Intelligent Data Analysis in bioMedicine (IDAMAP) held in Verona (2006) and Amsterdam (2007). Methods: Defining the field of biomedical data mining. Characterizing current developments and challenges for researchers in the field. Reporting on current and future activities of IMIA’s working group on Intelligent Data Analysis and Data Mining. Describing the content of the selected papers in this special topic. Results and Conclusions: In the biomedical field, data mining methods are used to develop clinical diagnostic and prognostic systems, to interpret biomedical signal and image data, to discover knowledge from biological and clinical databases, and in biosurveillance and anomaly detection applications. The main challenges for the field are i) dealing with very large search spaces in a both computationally efficient and statistically valid manner, ii) incorporating and utilizing medical and biological background knowledge in the data analysis process, iii) reasoning with time-oriented data and temporal abstraction, and iv) developing end-user tools for interactive presentation, interpretation, and analysis of large datasets.

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Research on the Content-Based Video Indexing for Smart Grid

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.945-949.3391 ◽

2014 ◽

Vol 945-949 ◽

pp. 3391-3395

Author(s):

Ming Liang Yan

Keyword(s):

Data Mining ◽

Cloud Computing ◽

Information Retrieval ◽

Social Network ◽

Data Analysis ◽

Internet Of Things ◽

Video Retrieval ◽

Video Data ◽

Intelligent Data Analysis ◽

Engine System

Data has become the fundamental resource by the emerging new services such as cloud computing, internet of things and social network. In the electric power applications, the video data mining plays an important role in the intelligent data analysis. With growth of video data in such an amazing speed, the information retrieval is becoming more and more important. This paper focuses on the analysis of the content-based video retrieval and proposes the design of a uniformed search engine system. The system is oriented to the retrieval of both the unstructured video contents and structured tags, which helps to achieve the integration of the heterogeneity data resources. In this paper, a retrieval framework is discussed and several problems are addressed.

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Intelligent Data Analysis with Data Mining

Intelligent Data Analysis ◽

10.1002/9781119544487.ch4 ◽

2020 ◽

pp. 63-83

Author(s):

Shivam Bachhety ◽

Ramneek Singhal ◽

Rachna Jain

Keyword(s):

Data Mining ◽

Data Analysis ◽

Intelligent Data Analysis

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Chemometric Expertise Of Clinical Monitoring Data Of Prolactinoma Patients

Open Chemistry ◽

10.1515/chem-2019-0050 ◽

2019 ◽

Vol 17 (1) ◽

pp. 408-412

Author(s):

Miroslava Nedyalkova ◽

Dimitar Dimitrov ◽

Borjana Donkova ◽

Vasil Simeonov

Keyword(s):

Data Mining ◽

Cluster Analysis ◽

Data Analysis ◽

Health Status ◽

Hierarchical Cluster ◽

Health Status Indicators ◽

Clinical Parameters ◽

Intelligent Data Analysis ◽

Major Goal ◽

Monitoring Procedure

AbstractThe present investigation indicates hidden relationships between the several clinical parameters usually monitored on prolactinoma patients using non-hierarchical cluster analysis. The major goal of the chemometric data mining is to offer a possible mode of optimization of the monitoring procedure by selecting a reduced number of health status indicators. The intelligent data analysis reveals the formation of three patterns of prolactinoma patients each one of them described by a set of clinical parameters. Thus, better strategies for considering patients with this diagnosis could be developed and clinically applied.

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