A Decision Support System base line flexible architecture to intrusion detection

被引:3
作者
Castellano, Marcello [1 ]
Mastronardi, Giuseppe [1 ]
Aprile, Angela [1 ]
Minardi, Mirko [1 ]
Catalano, Pierpaolo [1 ]
Dicensi, Vito [1 ]
Tarricone, Gianfranco [1 ]
机构
[1] Dipartimento di Elettrotecnica ed Elettronica, Politecnico di Bari, 70125 Bari
关键词
Data mining; Decision support system; Flexible architecture; Intrusion detection; Knowledge discovery; Mining engine; Web mining;
D O I
10.4304/jsw.2.6.30-41
中图分类号
学科分类号
摘要
Becoming more competitive and more effective in the current scenes of Business and Public Administration, the organizations must be able to approach easily and quickly to the information on the customers and the external conditions of market. In other words it means to head at the improvement of the Decision Support Systems. Decision Support System is concerned with developing systems aimed at helping decision makers solve problems and make decisions. In this paper, we refer to a decision support system based on flexible architecture able to discover the knowledge in a distributed and heterogeneous multi-organization environment. The system architecture has been designed according to the Service Oriented Architecture and Model View Controller design pattern. Moreover it is based on a mining engine whose purpose is to generate and to use suitable e-services for knowledge by driving the user through various stages of the Knowledge Discovery process. An Intrusion Detection application is also described. © 2006 ACADEMY PUBLISHER.
引用
收藏
页码:30 / 41
页数:11
相关论文
共 40 条
[1]  
Castellano M., Pastore N., Arcieri F., Summo V., de Grecis G.B., A Knowledge Center for a Social and Economic Growth of the Territory, International Conference On System Sciences, (2005)
[2]  
Castellano M., Pastore N., Arcieri F., Summo V., de Grecis G.B., An e-Government Cooperative Framework for Government Agencies, International Conference On System Sciences, (2005)
[3]  
Castellano M., Pastore N., Arcieri F., Summo V., de Grecis G.B., A Flexible Mining Architecture for Providing New E-Knowledge Services, International Conference On System Sciences, (2005)
[4]  
Castellano M., Pastore N., Arcieri F., Summo V., de Grecis G.B., Orchestrating Knowledge Discovery Process, E-Service Intelligence: Methodologies, Technologies and Application, pp. 447-496
[5]  
Castellano M., Pastore N., Arcieri F., Summo V., de Grecis G.B., A Web Mining Process for e-Knowledge Service, E-Service Intelligence: Methodologies, Technologies and Application, pp. 447-496
[6]  
Julisch K., Data mining for Intrusion Detection: A Critical Review, IBM Research
[7]  
El-Sayed M., Ruiz C., Rundensteiner E.A., FSMiner: Efficient and Incremental Mining of Frequent Sequence Patterns in Web logs, ACM WIDM'04, pp. 12-13, (2004)
[8]  
Kemmerer R., Vigna G., Hi-DRA: Intrusion Detection for Internet Security, IEEE Proceeding, 93, pp. 1847-1857, (2005)
[9]  
Valeur F., Mutz D., Vigna G., A Learning Based Approach to the Detection of SQL Attacks, Conference on Detection of Intrusion and Malware and Vulnerability Assessment (DIMVA), (2005)
[10]  
Lee W., Stolfo S.J., Mok K.W., Data mining approaches for intrusion detection, Proceedings of the 7th USENIX Security Symposium, (1998)