Hybrid Classification Based Intrusion Detection and Prevention System - V., Balamurugan;S., Selvaperumal;S., Faizal Mukthar Hussain
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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.…mehr

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Produktbeschreibung
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.
Autorenporträt
Dr. V. Balamurugan travaille en tant que professeur associé/ CSE.Prof. S. Faizal Mukthar Hussain travaille comme professeur adjoint/ CSE et Dr. S. Selvaperumal travaille comme doyen - recherche et professeur/ EEE à Mohamed Sathak Engineering College, Sathak Nagar, SH 49, Kilakarai, Ramanathapuram, Tamilnadu, Inde.