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- W2503979952 abstract "Intrusion detection systems (IDSs) have become an efficient defense tool against network attacks since they allow network administrator to detect policy violations. However, traditional IDS are vulnerable to novel malicious attacks. It is hard to make prevention mechanism to detect novel attack. It takes time to prevent from novel detected attack. This paper presents data mining and fuzzy logic-based mechanism for effectively identifying intrusion activities. The proposed fuzzy logic and data mining based mechanism can detect intrusion behavior of the networks intrusion and improve the detection rate of intrusion detection. The proposed mechanism identifies if the detected novel DoS attack is a variation of previously known DoS attack or not. If novel attack is identify as a variation of previous known attack then it is easy to make pretension mechanism in less time. The KDD Cup 99 intrusion detection dataset is used for experiments and evaluations of the proposed intrusion detection system. Experimental results are provided to support the proposed mechanism." @default.
- W2503979952 created "2016-08-23" @default.
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- W2503979952 date "2016-01-01" @default.
- W2503979952 modified "2023-09-25" @default.
- W2503979952 title "A Multi-classifiers Based Novel DoS/DDoS Attack Detection Using Fuzzy Logic" @default.
- W2503979952 cites W2485256768 @default.
- W2503979952 doi "https://doi.org/10.1007/978-981-10-0135-2_77" @default.
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