Wyniki wyszukiwania - BazTech

1

Analysis of neural networks usage for detection of a new attack in IDS

Kukiełka P., Kotulski Z.

Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica

|

2010

|

Vol. 10, no. 1

51--59

EN

Generally, Intrusion Detection Systems (IDS) work using two methods of identification of attacks: by signatures, that are specific defined elements of the network traffic possible to identify and by anomalies being some deviation form of the network behaviour assumed as normal. Recently, some attempts have been made to implement artificial intelligence method for detection of attacks. Many such implementations use for testing and learning process the data set provided by KDD (Knowledge Discovery and Data Mining Competition) project in 1999. Unfortunately, KDD99 data set was created more than eight years ago and during this time many new attacks have been discovered. In this paper we present our research on updating KDD99 data with traces of attacks of new types. After updating, the data set was used for training and testing MLP (Multi Layer Perceptron) neural network architecture IDS.

2

Eksploracja danych w kontekście procesu Knowledge Discovery In Databases (KDD) i metodologii Cross-Industry Standard Process For Data Mining (CRISP-DM)

Mirończuk M., Maciak T.

Metody Informatyki Stosowanej

|

2009

|

nr 2 (19)

65-79

EN

Article aims at introducing for the readers few problems connected with KDD process, Data Mining project modeling with the use of CRlSP-DM The systemized knowledge, aproaches to and generic terms was presented in the article. In the first part article describes approach to Data Exploration as one of the KDD cycle, which is specialized Knowledge Discovery process. Then article takes the subject of CRlSP-DM method. The context of method usage depending on scale and integration of project, which they concern - ivestigate of useing text mining in Inteligent Decission Support System (IDSS) develop by informatic faculty of Fire Service. At the end of the article the summary was made, which contains common features between the two looks on the exploration and extracting knowledge from data bases.

3

Metodyka realizacji procesu pozyskiwania wiedzy z danych

Rostek K.

Zarządzanie Przedsiębiorstwem

|

2005

|

Vol. 8, nr 2

70-81

EN

The process of discovering knowledge in databases may become a strong tool which would facilitate retaining competitive advantage on the insurance market. Its classification capabilities will enable insurance companies to acquaint themselves with their customers and their preferences, and to gain an in-depth understanding of the current policy portfolio. As a result of the predictive properties of the process an insurance company will be able to respond proactively to its customers' expectations, and thus ensure retaining the customers and counteracting customer loss to competitors. The predictive properties also enable some market behavior and risks on the part of the competition to be anticipated before they actually occur, and this increases the probability of being the first to counteract. However, in order to enable correct and effective completion of the knowledge discovery and data mining processes, the methodology of the process completion has to be adhered to and the terms and conditions for each of the stages of the process have to be met. Many computer information tools have been developed to support knowledge discovery in databases. However, even the best of programs will not solve all the problems connected with completion of the process and are not sufficient to ensure the success of the project as a whole. It is necessary to have an effective and efficient operating methodology. The methodology I am presenting has been developed based on the known methodology of computer tools for data mining purposes (specificity on the SAS and SPSS methodologies) and on the basis of my own professional experience. Methodology DAD (Data-Analysis-Decision) includes nine stages: formulating process assessments, extracting records and variables, extract processing, getting acquainted with the data, analysis of cross-correlations, analysis of multi-factor correlations, assessment of model results, transformation of results into knowledge, assessment of the usefulness of knowledge. The particular description of methodology DAD are presented in this article.