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Streams can have many text fields or even be a mix of different media types. The result is data streams that have a variable length and contain incomplete information. Such variable length, incomplete and noisy data streams are common in various areas of industry. Such data streams pose a difficult challenge for stream classifiers. This paper describes a new classification approach that addresses the challenges of variable length and noisy data streams. This approach has been implemented and its efficacy demonstrated through comparing it to a rule-based classifier. The results of the study demonstrated that the new system performs well with both variable length and noisy data streams. Such classifiers are particularly useful in industrial environments. In this paper, a secure adaptive fuzzy-logic based IDS (IDSs) is proposed. Different from the conventional IDSs, the new IDSs does not rely on the existing signatures. 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