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Network attack becomes more popular today. It is still difficult to detect the intrusion activity accurately for users websites through traditional approaches. A kind of integrated system is designed and established for the website security analysis based on Hadoop system. The core components of hardware subsystem is intrusion detection system (IDS) and Hadoop cluster. The data can be interacted between the intrusion detection system (IDS) controller, detection engine, website server, transferring server and Hadoop cluster. Based on the Map Reduce model, the Word Count algorithm is revised to obtain data statistical results such as IP address, intrusion level, intrusion type and intrusion time. These analyzed results are saved automatically into the MySQL database to form data tables, including statistical analysis, level distribution, week analysis and danger efficiency. Then, the resulted data are presented with visualization effect for the website managers. Introduction Network attack activities on the website become more popular and complicated. From the web browsing effects, some web pages may be changed with wrong information. Meanwhile, denial of service attack takes place to shut down the website server or network, making it inaccessible to its intended users. The manager need know the website operating situation online. However, most of the organizations lack of experience in preventing from such attacks. The traditional approaches with intrusion detection system (IDS) are always fail when facing to actual attacks. With the network attacking technology, the higher prevention caution is required to avoid the single system detection problem. One of the advanced technologies is big-data technology. It based on cloud computing platform and data mining technology. For example, With Log analysis, a kind of Hadoop-based Intrusion Detection System is designed as the simulation data as Map-Reduce algorithm [1] . For the computational Algorithms, the traditional Apriori association algorithm was applied in Hadoop cloud platform for the intrusion detection system [2]. The results show that the optimized Hadoop cloud IDS could be better in detection effects. Similarly, the Hadoop system was presented for fussy association analysis [3]. After comparison with traditional Apriori algorithm, the results demonstrated the advantage of cloud computing. Because all these simulation data are from data-set in KDD-CUP99 [2-6], it lacks truly validation in network operation process. Another test data-set is LLS DDOS 1.0. It was applied for the large-scale network security situation analysis to get the multidimensional association rule mining [7]. Besides, one kind of experimental system for intrusion detection was designed [8]. It was used to detect two types of intrusion of network sniffing and port scanning. It can 86 Advances in Computer Science Research (ACRS), volume 54 International Conference on Computer Networks and Communication Technology (CNCT2016)

Hadoop-based Intrusion Detection Technology and Data Visualization for Website Security