With the popularization of Internet and the wide use of electronic mails, the number of spam mails grows continuously. The matter has made e-mail users feel inconvenient. If e-mail servers can be integrated with data mining and artificial intelligence techniques and learn spam rules and filter out spam mails automatically, they will help every person who is bothered by spam mails to enjoy a clear e-mail environment. In this research, we propose an architecture called union defense to oppose against the spread of spam mails. Under the architecture, we need a rule-based data mining and artificial intelligence algorithm. Rough sets theory will be a good choice. Rough sets theory was proposed by Palwak, a logician living in Poland. It is a rule-based data mining and artificial intelligence algorithm and suitable to find the potential knowledge of inexact and incomplete data out. This research developed a spam filter based on rough sets theory. It can search for the characteristic rules of spam mails and can use these rules to filter out spam mails. This system set up by this research can be appended to most of existing e-mail servers. Besides, the system support Chinese, Japanese and Korean character sets and overcome the problem that most spam filters only can deal with English mails. We can develop a rule exchange approach between e-mail servers in the future works to realize union defense.

Growing volume of spam mails have not only decreased the productivity of people but also become a security threat on the Internet. Mail servers should have abilities to filter out spam mails which change time by time precisely and manage increasing spam rules which generated by mail servers automatically and effectively. Most paper only focused on single aspect (especially for spam rule generation) to prevent spam mail. However, in real word, spam prevention is not just applying data mining algorithm for rule generation. To filter out spam mails correctly in a real world, there are still many issues should be considered in addition to spam rule generation. In this paper, we integrate three modules to form a complete anti-spam system, they are spam rule generation module, spam rule reinforcement module and spam rule exchange module. In this paper, rule-based data mining approach is used to generate exchangeable spam rules. The feedback of user¡¦s returns is reinforced spam rule. The distributing spam rules are exchanged through machine-readable XML format. The results of experiment draw the following conclusion: (1) The spam filter can filter out the Chinese mails by analyzing the header characteristics. (2) Rules exchanged among mail improve the spam recall and accuracy of mail servers. (3) Rules reinforced improve the effectiveness of spam rule.