Research on SVDD Network Intrusion Detection of the Optimal Feature Selection for Particle Swarm

2014 ◽  
Vol 716-717 ◽  
pp. 860-863
Author(s):  
Xiao Yu Zhang ◽  
Zhen Wei Wei ◽  
Xiao Lin
Keyword(s):  
Intrusion Detection ◽  
Particle Swarm ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Set ◽  
Network Intrusion ◽  
Detection Ratio ◽  
Sample Set ◽  
Optimal Feature

Focusing on the problem about the higher dimensionality of sample set in the intrusion detection, propose an optimized method of support vector data description (SVDD) based on particle swarm optimization (PSO) and apply it to the intrusion detection of network exception. This method adopts PSO to eliminate the superfluous parameters in SVDD and carries out dimension reduction to data; then, establish the super sphere model to detect the network intrusion data and output the results of intrusion detection. Carry out the simulation experiment based on the standard detection data set of KDD CUP' 99, and the result shows that this method, comparing with the traditional SVDD, can effectively improve the detection ratio with a smaller amount of calculation.

scholarly journals A Support Vector Data Description Committee for Face Detection

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yi-Hung Liu ◽  
Yung Ting ◽  
Shian-Shing Shyu ◽  
Chang-Kuo Chen ◽  
Chung-Lin Lee ◽  
...  
Keyword(s):  
Face Detection ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Detection Accuracy ◽  
Vector Data ◽  
Data Set ◽  
Data Description ◽  
The Face ◽  
Face Detector

Face detection is a crucial prestage for face recognition and is often treated as a binary (face and nonface) classification problem. While this strategy is simple to implement, face detection accuracy would drop when nonface training patterns are undersampled. To avoid these problems, we propose in this paper a one-class learning-based face detector called support vector data description (SVDD) committee, which consists of several SVDD members, each of which is trained on a subset of face patterns. Nonfaces are not required in the training of the SVDD committee. Therefore, the face detection accuracy of SVDD committee is independent of the nonface training patterns. Moreover, the proposed SVDD committee is also able to improve generalization ability of the original SVDD when the face data set has a multicluster distribution. Experiments carried out on the extended MIT face data set show that the proposed SVDD committee can achieve better face detection accuracy than the widely used SVM face detector and performs better than other one-class classifiers, including the original SVDD and the kernel principal component analysis (Kernel PCA).

scholarly journals Intrusion detection in internet of things using supervised machine learning based on application and transport layer features using UNSW-NB15 data-set

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Muhammad Ahmad ◽  
Qaiser Riaz ◽  
Muhammad Zeeshan ◽  
Hasan Tahir ◽  
Syed Ali Haider ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Detection System ◽  
Transport Layer ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Data Set ◽  
Network Intrusion ◽  
Iot Devices

AbstractInternet of Things (IoT) devices are well-connected; they generate and consume data which involves transmission of data back and forth among various devices. Ensuring security of the data is a critical challenge as far as IoT is concerned. Since IoT devices are inherently low-power and do not require a lot of compute power, a Network Intrusion Detection System is typically employed to detect and remove malicious packets from entering the network. In the same context, we propose feature clusters in terms of Flow, Message Queuing Telemetry Transport (MQTT) and Transmission Control Protocol (TCP) by using features in UNSW-NB15 data-set. We eliminate problems like over-fitting, curse of dimensionality and imbalance in the data-set. We apply supervised Machine Learning (ML) algorithms, i.e., Random Forest (RF), Support Vector Machine and Artificial Neural Networks on the clusters. Using RF, we, respectively, achieve 98.67% and 97.37% of accuracy in binary and multi-class classification. In clusters based techniques, we achieved 96.96%, 91.4% and 97.54% of classification accuracy by using RF on Flow & MQTT features, TCP features and top features from both clusters. Moreover, we show that the proposed feature clusters provide higher accuracy and requires lesser training time as compared to other state-of-the-art supervised ML-based approaches.

Dimensionality Reduction Method of Training Sample Set for SVDD Based on Statistical Information

2012 ◽  
Vol 220-223 ◽  
pp. 2097-2101 ◽  
Author(s):  
Mian Wu ◽  
Bin Chen ◽  
Bao Cheng Gao ◽  
Xiao Bin Cheng ◽  
Zhao Li Yan
Keyword(s):  
Low Complexity ◽  
Training Sample ◽  
Statistical Information ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Position Information ◽  
Vector Distribution ◽  
Sample Set ◽  
Dimensionality Reduction Method

In order to solve problem of high time complexity of support vector data description (SVDD) in training process, a low complexity SVDD algorithm is proposed by introducing statistical information grid (STING).The algorithm applies STING division to sample set space using support vector distribution characteristics and kernel distances between samples and sphere’s centre. Based on position information of sample points, it rejects non-support vectors and obtained simplified sample set. The results show that proposed method can reduce training scale and time without decreasing classification accuracy.

A pruned support vector data description-based outlier detection method: Applied to robust process monitoring

2020 ◽  
Vol 42 (11) ◽  
pp. 2113-2126 ◽  
Author(s):  
Ping Yuan ◽  
Zhizhong Mao ◽  
Biao Wang
Keyword(s):  
Process Monitoring ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Data Sets ◽  
Vector Data ◽  
Training Set ◽  
Data Set ◽  
Data Description ◽  
One Class Classifier ◽  
Comparative Results

Support vector data description (SVDD) is a boundary-based one-class classifier that has been widely used for process monitoring during recent years. However, in some applications where databases are often contaminated by outliers, the performance of SVDD would become deteriorated, leading to low detection rate. To this end, this paper proposes a pruned SVDD model in order to improve its robustness. In contrast to other robust SVDD models that are developed from the algorithmic level, we prune the basic SVDD from a data level. The rationale is to exclude outlier examples from the final training set as many as possible. Specifically, three different SVDD models are constructed successively with different training sets. The first model is used to extract target points by means of rejecting more suspect outlier examples. The second model is constructed using those extracted target points, and is used to recover some false outlier examples labeled by the first model. We build the third (final) model with the final training set consisting of target examples by the first model and false outlier examples by the second model. We validate our proposed method on 20 benchmark data sets and TE data set. Comparative results show that our pruned model could improve the robustness of SVDD more efficiently.

Sign in / Sign up

Export Citation Format

Share Document