System is divided into control rules and intelligent detection module.
Through practical testing, the system can display real-time network connection status on the application procedures can be effective control of network data can also be intelligent detection.
That is, data mining attempts to extract knowledge from data.
Data mining differs from traditional statistics in several ways: formal statistical inference is assumption driven in the sense that a hypothesis is formed and validated against the data.
Abstract: In this paper, data mining algorithms have been refined and optimized to achieve the intelligent detection of network data.
Winsock2 SPI used in the design of interception of network data, and use "session filtering" approach to network packet filtering.Abstract: Data mining techniques give us a feasible method to deal with great amount of data, which is generated during the software developing.Many methods have been used in data mining, Bayesian networks become a focus currently.Then, do performance requirement analysis; describe the main content of the three aspects of accuracy, time characteristics and flexibility, especially analysis the C/S structure model to build software systems.In this paper, describes the overall concept of software function and performance as the specific software requirement specification, lays a foundation for the development of storage management system., is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses.Data mining tools predict future trends and behaviours, allowing businesses to make proactive, knowledge-driven decisions.The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems.Data mining tools can answer business questions that traditionally were too time-consuming to resolve.Data mining in contrast is discovery driven in the sense that patterns and hypothesis are automatically extracted from data.Said another way, data mining is data driven, while statistics is human driven.