Hybrid Detection Model Combining Grey Scale Weight Based Quoit Filter with Support Vector Machine

The objective of this article is to develop an intrusion detection model aimed at distinguishing attacks in the network. The aim of building IDS relies on upon preprocessing of intrusion data, choosing most relevant features and in the plan of an efficient learning algorithm that properly groups the normal and malicious examples. In this experiment, the detection model uses an ensemble approach of supervised (SVM) and unsupervised (K-Means) to detect the patterns. This technique first divides the data and forms two clusters as per K-Means and labels the clusters using the Support Vector Machine (SVM). The parameters of K-Means and SVM are tuned and optimized using an intrusion dataset. The SVM provides up to 88%, and K-Means provides up to 83% accuracy individually. However, the ensemble of K-Means and SVM provides more than 99% on three benchmarked datasets in less time. The SVM only classifies three instances of each cluster randomly and labels them as per a majority voting approach. The proposed approach outperforms compared to earlier ensemble approaches on intrusion datasets.

Download Full-text

Freeway Accident Detection Model Based on Support Vector Machine

ICTE 2011 ◽

10.1061/41184(419)512 ◽

2011 ◽

Author(s):

Bin Chen ◽

Cao He ◽

Jianbo Wang

Keyword(s):

Support Vector Machine ◽

Support Vector ◽

Detection Model ◽

Model Based ◽

Accident Detection

Download Full-text

Intrusion Detection Model Based On Particle Swarm Optimization and Support Vector Machine

2007 IEEE Symposium on Computational Intelligence in Security and Defense Applications ◽

10.1109/cisda.2007.368152 ◽

2007 ◽

Cited By ~ 33

Author(s):

Surat Srinoy

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Intrusion Detection ◽

Particle Swarm ◽

Support Vector ◽

Swarm Optimization ◽

Detection Model ◽

Model Based

Download Full-text

Study about Recognition of Digital Meter Dial Reading Based on SVM

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.615.194 ◽

2014 ◽

Vol 615 ◽

pp. 194-197

Author(s):

Zhen Yuan Tu ◽

Fang Hua Ning ◽

Wu Jia Yu

Keyword(s):

Support Vector Machine ◽

Cross Validation ◽

Recognition Rate ◽

Classification Model ◽

Support Vector ◽

Feature Points ◽

Svm Classification ◽

Performance Factors ◽

Model Combining ◽

Fast Recognition

In practice, it is difficult for Support Vector Machine (SVM) to have a relatively high recognition rate as well as a quite fast recognition speed. In order to resolve this defect, in this paper we build a SVM classification model combining numerical characteristics. We use readings of rotary natural meters as the test temple, do positioning, preprocessing, feature points extracting, classifying and other series of operations to the numeric region of the dial. Then with the idea of cross-validation, we keep doing parameter optimation to SVM. At last, after making a comprehensive contrast of the effects which numerous performance factors make on the experimental outputs, we try to give our explanation of the outputs from different perspectives.

Download Full-text