Building Efficient Intrusion Detection Model Based on Principal Component Analysis and C4.5

2006 ◽  
Author(s):  
You Chen ◽  
Yang Li ◽  
Xue-Qi Cheng ◽  
Li Guo
Keyword(s):  
Principal Component Analysis ◽  
Intrusion Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Detection Model ◽  
Model Based

Applying Weighted PCA on Multiclass Classification for Intrusion Detection

2011 ◽  
pp. 220-241
Author(s):  
Mohsen Moshki ◽  
Mehran Garmehi ◽  
Peyman Kabiri
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Intrusion Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Multiclass Classification ◽  
Extended Version ◽  
Number Of Classes

In this chapter, application of Principal Component Analysis (PCA) and one of its extensions on intrusion detection is investigated. This extended version of PCA is modified to cover an important shortcoming of traditional PCA. In order to evaluate these modifications, it is mathematically proved that these modifications are beneficial and later on a known dataset such as the DARPA99 dataset is used to verify results experimentally. To verify this approach, initially the traditional PCA is used to preprocess the dataset. Later on, using a simple classifier such as KNN, the effectiveness of the multiclass classification is studied. In the reported work, instead of traditional PCA, a revised version of PCA named Weighted PCA (WPCA) will be used for feature extraction. The results from applying the aforementioned method to the DARPA99 dataset show that this approach results in better accuracy than the traditional PCA when a number of features are limited, a number of classes are large, and a population of classes is unbalanced. In some situations WPCA outperforms traditional PCA by more than 1% in accuracy.

scholarly journals EVALUATING MBSE POTENTIAL USING PRODUCT AND DEVELOPMENT CHARACTERISTICS – A STATISTICAL INVESTIGATION

2020 ◽  
Vol 1 ◽  
pp. 2385-2394
Author(s):  
M. Schöberl ◽  
E. Rebentisch ◽  
J. Trauer ◽  
M. Mörtl ◽  
J. Fottner
Keyword(s):  
Principal Component Analysis ◽  
Systems Engineering ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Investigation ◽  
Product Complexity ◽  
Organizational Complexity ◽  
Model Based ◽  
Using Data ◽  
Model Based Systems Engineering

AbstractAs model-based systems engineering (MBSE) is evolving, the need for evaluating MBSE approaches grows. Literature shows that there is an untested assertion in the MBSE community that complexity drives the adoption of MBSE. To assess this assertion and support the evaluation of MBSE, a principal component analysis was carried out on eight product and development characteristics using data collected in an MBSE course, resulting in organizational complexity, product complexity and inertia. To conclude, the method developed in this paper enables organisations to evaluate their MBSE adoption potential.

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