With the rapid expansion of computer networks during the past few years, security has become a crucial issue for modern computer systems. A good way to identify malicious use is through monitoring unusual user activity. To identify these malicious activities various data-mining and machine learning techniques have been deployed for intrusion detection. This book proposes Self Configuring Intrusion Detection System (SCIDS) to make tuning automatically. The key idea is to use the binary SLIPPER as a basic module, which is a rule learner based on confidence-rated boosting. This system is evaluated using the NSL KDD intrusion detection dataset. An experimental result shows the SCIDS system with SLIPPER algorithm gives better performance in terms of detection rate, false alarm rate, total misclassification cost and cost per example on NSL-KDD dataset than that of on KDD.
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Sandip Sonawane, Assitant Professor,Department of Information Technology,R. C. Patel Institute of Technology,Shirpur,Dist-Dhule,Maharashtra
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -With the rapid expansion of computer networks during the past few years, security has become a crucial issue for modern computer systems. A good way to identify malicious use is through monitoring unusual user activity. To identify these malicious activities various data-mining and machine learning techniques have been deployed for intrusion detection. This book proposes Self Configuring Intrusion Detection System (SCIDS) to make tuning automatically. The key idea is to use the binary SLIPPER as a basic module, which is a rule learner based on confidence-rated boosting. This system is evaluated using the NSL KDD intrusion detection dataset. An experimental result shows the SCIDS system with SLIPPER algorithm gives better performance in terms of detection rate, false alarm rate, total misclassification cost and cost per example on NSL-KDD dataset than that of on KDD. 80 pp. Englisch. Bestandsnummer des Verkäufers 9783659922893
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Sonawane SandipSandip Sonawane, Assitant Professor,Department of Information Technology,R. C. Patel Institute of Technology,Shirpur,Dist-Dhule,MaharashtraWith the rapid expansion of computer networks during the past few years, se. Bestandsnummer des Verkäufers 159147706
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -With the rapid expansion of computer networks during the past few years, security has become a crucial issue for modern computer systems. A good way to identify malicious use is through monitoring unusual user activity. To identify these malicious activities various data-mining and machine learning techniques have been deployed for intrusion detection. This book proposes Self Configuring Intrusion Detection System (SCIDS) to make tuning automatically. The key idea is to use the binary SLIPPER as a basic module, which is a rule learner based on confidence-rated boosting. This system is evaluated using the NSL KDD intrusion detection dataset. An experimental result shows the SCIDS system with SLIPPER algorithm gives better performance in terms of detection rate, false alarm rate, total misclassification cost and cost per example on NSL-KDD dataset than that of on KDD.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 80 pp. Englisch. Bestandsnummer des Verkäufers 9783659922893
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - With the rapid expansion of computer networks during the past few years, security has become a crucial issue for modern computer systems. A good way to identify malicious use is through monitoring unusual user activity. To identify these malicious activities various data-mining and machine learning techniques have been deployed for intrusion detection. This book proposes Self Configuring Intrusion Detection System (SCIDS) to make tuning automatically. The key idea is to use the binary SLIPPER as a basic module, which is a rule learner based on confidence-rated boosting. This system is evaluated using the NSL KDD intrusion detection dataset. An experimental result shows the SCIDS system with SLIPPER algorithm gives better performance in terms of detection rate, false alarm rate, total misclassification cost and cost per example on NSL-KDD dataset than that of on KDD. Bestandsnummer des Verkäufers 9783659922893
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