A Mutated Intrusion Detection System Using Principal Component Analysis and Time Delay Neural Network

2006 ◽  
pp. 246-254 ◽  
Author(s):  
Byoung-Doo Kang ◽  
Jae-Won Lee ◽  
Jong-Ho Kim ◽  
O-Hwa Kwon ◽  
Chi-Young Seong ◽  
...  
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Time Delay ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Principal Component ◽  
Component Analysis

A hybrid combination of LS-SVM and KPCA with bat algorithm for intrusion detection

2019 ◽  
Vol 12 ◽  
Author(s):  
Thiruppathy Kesavan. V ◽  
Loheswaran K
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Principal Component ◽  
Bat Algorithm ◽  
Least Square ◽  
Kernel Principal Component Analysis ◽  
Support Vector

: Intrusion Detection System is one of the prominent ways to identify the attacks by effectively monitoring the network. Designing an intrusion detection system that utilizes the resources efficiently by improving the precision is a challenging factor. This paper proposes a Least Square Support Vector Machine (LS-SVM) based on bat algorithm (BA) for efficient intrusion detection. The proposed technique is divided into two phases. In the first phase, the Kernel principal component analysis (KPCA) is utilized as a pre-processing of LS-SVM to decrease the dimension of feature vectors and abbreviates the preparing time with a specific end goal to decrease the noise caused by feature contrasts and enhance the implementation of LS-SVM. In the second phase, the LS-SVM with bat algorithm is applied for the classification of detection. BA utilizes programmed zooming to adjust investigation and abuse among the hunting procedure. Finally, as per the ideal feature subset, the feature weights and the parameters of LS-SVM are optimized at the same time. The proposed algorithm is named as Kernel principal component analysis based least square support vector machine with bat algorithm (KPCA-BA-LS-SVM). To show the adequacy of proposed method, the tests are completed on KDD 99 dataset which is viewed as an accepted benchmark for assessing the execution of intrusions detection. Furthermore, our proposed hybridization method gets a sensible execution regarding precision and efficiency.

scholarly journals Graphene Foam Chemical Sensor System Based on Principal Component Analysis and Backpropagation Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Hongling Hua ◽  
Xiaohui Xie ◽  
Jinjin Sun ◽  
Ge Qin ◽  
Caiyan Tang ◽  
...  
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Chemical Sensors ◽  
Chemical Sensor ◽  
Detection System ◽  
Principal Component ◽  
Component Analysis ◽  
Backpropagation Neural Network ◽  
Graphene Foam ◽  
New Strategy

A kind of graphene foam chemical sensor (GFCS) system based on the principal component analysis (PCA) and backpropagation neural network (BPNN) was presented in this paper. Compared with conventional chemical sensors, the GFCS could discriminate various chemical molecules with selectivity without surface modification. The GFCS system consisted of an unmodified graphene foam chemical sensor, an electrical resistance time domain detection system (ERTDS), and a pattern recognition module. The GFCS has been validated via several chemical molecules discrimination including chloroform, acetone, and ether. The experimental results showed that the discrimination accuracy for each molecule exceeded 97% and a single measurement can be achieved in ten minutes. This work may have presented a new strategy for research and application for graphene chemical sensors.

Sign in / Sign up

Export Citation Format

Share Document