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- W2469369952 abstract "Anomaly exposure is a grave concern in network intrusion classification (IDS). Furthermost anomaly based IDSs employ supervised procedures, whose enactments extremely be contingent on attack-free training statistics. Nonetheless, this generous of training data is problematic to acquire in real-world network environment. Additionally, with fluctuating network setting or services, patterns of typical traffic will be reformed. This leads to high false-positive rate of supervised IDSs. Real-time detection is one of the most imperative issues in intrusion detection. When the network data is becoming enormous with high dimensionality, real-time detection with high detection accuracy and low false alarm rate is challenging for previous methods. The vertical progression in data volume and Hadoop cluster extent make it a momentous encounter to diagnose and localize problems in a production-level cluster setting competently and within a petite epoch of time. Frequently, the disseminated monitoring schemes are not accomplished of perceiving a problem thriving in earlier when a comprehensive Hadoop cluster twitches to depreciate in enactment or becomes unattainable. This research exertion proposed an unsupervised outlier detection based on fuzzy-based model also with typically K-means clustering algorithm and categorize different types of anomaly which can overwhelmed the downsides of supervised incongruity detection. The tryouts were accomplished on KDD’99 intrusion detection dataset to associate the performance and enactment of the proposed procedure with a typical K-means clustering algorithm and perceived reduced computational time and enriched classification accuracy." @default.
- W2469369952 created "2016-07-22" @default.
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- W2469369952 date "2016-01-01" @default.
- W2469369952 modified "2023-09-25" @default.
- W2469369952 title "Fuzzy Clustering-Based Efficient Classification Model for Large TCP Dump Dataset Using Hadoop Framework" @default.
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- W2469369952 doi "https://doi.org/10.1007/978-981-10-0129-1_45" @default.
- W2469369952 hasPublicationYear "2016" @default.
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