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Application of an Improved SVM MultiClass Classification to Intrusion Detection  [PDF]
LI Tai-bai,TANG Wan-mei
Journal of Chongqing Normal University , 2012,
Abstract: Intrusion detection system as the key technology of network security becomes research hot spot of the current network security, while precision and generalization performance is the key point of intrusion detection algorithm. According to binary tree method and the characteristics of sphere structured support vector machine, an improved SVM multiclass classification algorithm is proposed to intrusion detection. This algorithm uses similarity functions as weight value and selects two kinds of sample similarity minimum to structure twoclass classifier; to bottomup structure kinds of twoclass classifier of sphere structured SVM. Finally it is applied to intrusion detection. The KDD CUP 1999 intrusion detection data used to simulate experiments. Experimental results show that the algorithm effectively improved the detection accuracy and generalization performance.
Comparisons of SVM and LS-SVM for Intrusion Detection
基于支持向量机和最小二乘支持向量机的入侵检测比较

REN Xun-yi WANG Ru-chuan,XIE Yong-juan,
任勋益
,王汝传,谢永娟

计算机科学 , 2008,
Abstract: This paper utilizes support vector machine and least square-support vector machine for intrusion detection.We normalizae data,reduce the data with principal component analysis,train and test reduced data with support vector and least square support vector machine.We do three experiments on KDDCUP'99 data set,and utilize Receiver Operating Characteristics curves to evaluate classifier's ability of SVM and LS-SVM,and statistic time cost.Experimental results show SVM has more classifying ability than LS-SVM,bu...
Incremental SVM Intrusion Detection Algorithm Based on Distance Weighted Template Reduction and Attribute Information Entropyc
基于距离加权模板约简和属性信息嫡的增量SVM入侵检测算法

徐永华,李广水
计算机科学 , 2012,
Abstract: In order to solve the problem of the SVM intrusion detection method which has low detection rate, high disforting rate and slow detection speed, a kind of incremental SVM intrusion detection algorithm based on distance weighfed template reduction and the attribute information entropy was proposed. In this algorithm, the training sample set reduction is made according to the sample for the samples and the neighbors to the total distance weighted weight, then,the clustering sample point and the noise of the fitting point are taken out through the adjacent to the border area segmentation and based on sample attribute information entropy, and then, using the sample dispersion extracts possible support vector machine, and incremental learning based on KK I} conditions is made to construct the optimal SVM classifier. The simulation results show that the algorithm has good detection rate and the detection efficiency, and distorting rate low.
An Integrated Intrusion Detection System by Combining SVM with AdaBoost  [PDF]
Yu Ren
Journal of Software Engineering and Applications (JSEA) , 2014, DOI: 10.4236/jsea.2014.712090
Abstract: In the Internet, computers and network equipments are threatened by malicious intrusion, which seriously affects the security of the network. Intrusion behavior has the characteristics of fast upgrade, strong concealment and randomness, so that traditional methods of intrusion detection system (IDS) are difficult to prevent the attacks effectively. In this paper, an integrated network intrusion detection algorithm by combining support vector machine (SVM) with AdaBoost was presented. The SVM is used to construct base classifiers, and the AdaBoost is used for training these learning modules and generating the final intrusion detection model by iterating to update the weight of samples and detection model, until the number of iterations or the accuracy of detection model achieves target setting. The effectiveness of the proposed IDS is evaluated using DARPA99 datasets. Accuracy, a criterion, is used to evaluate the detection performance of the proposed IDS. Experimental results show that it achieves better performance when compared with two state-of-the-art IDS.
Application of Data Mining to Network Intrusion Detection: Classifier Selection Model  [PDF]
Huy Nguyen,Deokjai Choi
Computer Science , 2010,
Abstract: As network attacks have increased in number and severity over the past few years, intrusion detection system (IDS) is increasingly becoming a critical component to secure the network. Due to large volumes of security audit data as well as complex and dynamic properties of intrusion behaviors, optimizing performance of IDS becomes an important open problem that is receiving more and more attention from the research community. The uncertainty to explore if certain algorithms perform better for certain attack classes constitutes the motivation for the reported herein. In this paper, we evaluate performance of a comprehensive set of classifier algorithms using KDD99 dataset. Based on evaluation results, best algorithms for each attack category is chosen and two classifier algorithm selection models are proposed. The simulation result comparison indicates that noticeable performance improvement and real-time intrusion detection can be achieved as we apply the proposed models to detect different kinds of network attacks.
Intrusion Awareness Based on Data Fusion and SVM Classification
Ramnaresh Sharma,Manish Shrivastava
International Journal of Advanced Computer Research , 2012,
Abstract: Network intrusion awareness is important factor forrisk analysis of network security. In the currentdecade various method and framework are availablefor intrusion detection and security awareness.Some method based on knowledge discovery processand some framework based on neural network.These entire model take rule based decision for thegeneration of security alerts. In this paper weproposed a novel method for intrusion awarenessusing data fusion and SVM classification. Datafusion work on the biases of features gathering ofevent. Support vector machine is super classifier ofdata. Here we used SVM for the detection of closeditem of ruled based technique. Our proposedmethod simulate on KDD1999 DARPA data set andget better empirical evaluation result in comparisonof rule based technique and neural network model.
A Decision Tree Classifier for Intrusion Detection Priority Tagging  [PDF]
Adel Ammar
Journal of Computer and Communications (JCC) , 2015, DOI: 10.4236/jcc.2015.34006
Abstract: Snort rule-checking is one of the most popular forms of Network Intrusion Detection Systems (NIDS). In this article, we show that Snort priorities of true positive traffic (real attacks) can be approximated in real-time, in the context of high speed networks, by a decision tree classifier, using the information of only three easily extracted features (protocol, source port, and destination port), with an accuracy of 99%. Snort issues alert priorities based on its own default set of attack classes (34 classes) that are used by the default set of rules it provides. But the decision tree model is able to predict the priorities without using this default classification. The obtained tagger can provide a useful complement to an anomaly detection intrusion detection system.
Face Detection Using Adaboosted SVM-Based Component Classifier  [PDF]
Seyyed Majid Valiollahzadeh,Abolghasem Sayadiyan,Mohammad Nazari
Computer Science , 2008,
Abstract: Recently, Adaboost has been widely used to improve the accuracy of any given learning algorithm. In this paper we focus on designing an algorithm to employ combination of Adaboost with Support Vector Machine as weak component classifiers to be used in Face Detection Task. To obtain a set of effective SVM-weaklearner Classifier, this algorithm adaptively adjusts the kernel parameter in SVM instead of using a fixed one. Proposed combination outperforms in generalization in comparison with SVM on imbalanced classification problem. The proposed here method is compared, in terms of classification accuracy, to other commonly used Adaboost methods, such as Decision Trees and Neural Networks, on CMU+MIT face database. Results indicate that the performance of the proposed method is overall superior to previous Adaboost approaches.
Early Detection of Breast Cancer using SVM Classifier Technique
Y.Ireaneus Anna Rejani,Dr.S.Thamarai Selvi
International Journal on Computer Science and Engineering , 2009,
Abstract: This paper presents a tumor detection algorithm from mammogram. The proposed system focuses on the solution of two problems. One is how to detect tumors as suspicious regions with a very weak contrast to their background and another is how to extract features which categorize tumors. The tumor detection method follows the scheme of (a) mammogram enhancement. (b) The segmentation of the tumor area. (c) The extraction of features from the segmented tumor area. (d) The use of SVM classifier. The enhancement can be defined as conversion of the image quality to a better and more understandable level. The mammogram enhancement procedure includes filtering, top hat operation, DWT. Then the contrast stretching is used to increase the contrast of the image. The segmentation of mammogram images has been playing an important role to improve the detection and diagnosis of breast cancer. The most common segmentation method used is thresholding. The features are extracted from the segmented breast area. Next stage include, which classifies the regions using the SVM classifier. The method was tested on 75 mammographic images, from the mini-MIAS database. The methodology achieved a sensitivity of 88.75%.
Early Detection of Breast Cancer using SVM Classifier Technique  [PDF]
Y. Ireaneus Anna Rejani,S. Thamarai Selvi
Computer Science , 2009,
Abstract: This paper presents a tumor detection algorithm from mammogram. The proposed system focuses on the solution of two problems. One is how to detect tumors as suspicious regions with a very weak contrast to their background and another is how to extract features which categorize tumors. The tumor detection method follows the scheme of (a) mammogram enhancement. (b) The segmentation of the tumor area. (c) The extraction of features from the segmented tumor area. (d) The use of SVM classifier. The enhancement can be defined as conversion of the image quality to a better and more understandable level. The mammogram enhancement procedure includes filtering, top hat operation, DWT. Then the contrast stretching is used to increase the contrast of the image. The segmentation of mammogram images has been playing an important role to improve the detection and diagnosis of breast cancer. The most common segmentation method used is thresholding. The features are extracted from the segmented breast area. Next stage include, which classifies the regions using the SVM classifier. The method was tested on 75 mammographic images, from the mini-MIAS database. The methodology achieved a sensitivity of 88.75%.
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