The main contribution of this work is to improve the security of the system and efficiently detect the intruder attack in the cloud environment. It also concentrates on the resource allocation process, which reduces the execution time of the packets with improving the service rate of packets. The false alarm rate and detection rate of the proposed system are improved when compared to the existing traditional intrusion detection approaches.To sum up, the research process provides an efficient intrusion detection and prevention system for solving intruder attacks and security problems. Comparison of traditional machine learning methods and their hybrid methods are also reported for the process of intrusion detection mechanism.
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Dr. V. Balamurugan is working as an Associate Professor/ CSE.Prof. S. Faizal Mukthar Hussain is working as an Assistant Professor/ CSE and Dr. S. Selvaperumal is working as a Dean - Research and Professor/ EEE in Mohamed Sathak Engineering College, Sathak Nagar, SH 49, Kilakarai, Ramanathapuram, Tamilnadu, India.
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The main contribution of this work is to improve the security of the system and efficiently detect the intruder attack in the cloud environment. It also concentrates on the resource allocation process, which reduces the execution time of the packets with improving the service rate of packets. The false alarm rate and detection rate of the proposed system are improved when compared to the existing traditional intrusion detection approaches.To sum up, the research process provides an efficient intrusion detection and prevention system for solving intruder attacks and security problems. Comparison of traditional machine learning methods and their hybrid methods are also reported for the process of intrusion detection mechanism. 144 pp. Englisch. Bestandsnummer des Verkäufers 9786206754480
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Taschenbuch. Zustand: Neu. Hybrid Classification Based Intrusion Detection and Prevention System | on Cloud Environment | Balamurugan V. (u. a.) | Taschenbuch | Englisch | 2023 | LAP LAMBERT Academic Publishing | EAN 9786206754480 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 127456335
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The main contribution of this work is to improve the security of the system and efficiently detect the intruder attack in the cloud environment. It also concentrates on the resource allocation process, which reduces the execution time of the packets with improving the service rate of packets. The false alarm rate and detection rate of the proposed system are improved when compared to the existing traditional intrusion detection approaches.To sum up, the research process provides an efficient intrusion detection and prevention system for solving intruder attacks and security problems. Comparison of traditional machine learning methods and their hybrid methods are also reported for the process of intrusion detection mechanism.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 144 pp. Englisch. Bestandsnummer des Verkäufers 9786206754480
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The main contribution of this work is to improve the security of the system and efficiently detect the intruder attack in the cloud environment. It also concentrates on the resource allocation process, which reduces the execution time of the packets with improving the service rate of packets. The false alarm rate and detection rate of the proposed system are improved when compared to the existing traditional intrusion detection approaches.To sum up, the research process provides an efficient intrusion detection and prevention system for solving intruder attacks and security problems. Comparison of traditional machine learning methods and their hybrid methods are also reported for the process of intrusion detection mechanism. Bestandsnummer des Verkäufers 9786206754480
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