Evolution of a Hybrid Model for an Effective Perimeter Security Device

Defence Science Journal

A.R. Vasudevan¹  S. Selvakumar¹ 

[1] Department of Computer Science and Engineering, National Institute of Technology Tiruchirappalli

关键词: Singular value decomposition;    image fusion;    super resolution;    image registration;    interpolation;   

DOI  :  

学科分类：社会科学、人文和艺术（综合）

来源: Defence Scientific Information & Documentation Centre

【 摘 要 】

Clustering and classification models, or hybrid models are the most widely used models that can handle the diverse nature of NIDS dataset. Dirichlet process clustering technique is a non-parametric Bayesian mixture model that considers the data distribution of the dataset for the formation of distinct clusters. The number of clusters is not known a priori and it differs across different datasets. Determining the number of clusters based on the distribution of data instances can increase the performance of the model. Naive Bayes model, a supervised learning classification technique, maintains a better computational efficiency, by reducing the training time. In this paper, we propose a hybrid model to exploit the positive aspect of proper clustering of data instances and the computational efficiency in building a NIDS. RIPPER algorithm is used to extract rules from the traffic description for updation of the rule database. Experiments were conducted in the KDD CUP’99 and SSENet-2011 datasets to study the performance of the proposed model. Also, a comparison of three hybrid methods with the proposed hybrid model was carried out. The results showed that the proposed hybrid model is superior in building a robust perimeter security device.

【 授权许可】

Unknown