Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learn...Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learning algorithm, is used to rank the features extracted for detecting intrusions and generate intrusion detection models. Feature ranking is a very critical step when building the model. RSC performs feature ranking before generating rules, and converts the feature ranking to minimal hitting set problem addressed by using genetic algorithm (GA). This is done in classical approaches using Support Vector Machine (SVM) by executing many iterations, each of which removes one useless feature. Compared with those methods, our method can avoid many iterations. In addition, a hybrid genetic algorithm is proposed to increase the convergence speed and decrease the training time of RSC. The models generated by RSC take the form of'IF-THEN' rules, which have the advantage of explication. Tests and comparison of RSC with SVM on DARPA benchmark data showed that for Probe and DoS attacks both RSC and SVM yielded highly accurate results (greater than 99% accuracy on testing set).展开更多
Group distance coding is suitable for secret communication covered by printed documents. However there is no effective method against it. The study found that the hiding method will make group distances of text lines ...Group distance coding is suitable for secret communication covered by printed documents. However there is no effective method against it. The study found that the hiding method will make group distances of text lines coverage on specified values, and make variances of group distances among N-Window text lines become small. Inspired by the discovery, the research brings out a Support Vector Machine （SVM） based steganalysis algorithm. To avoid the disturbance of large difference among words length from same line, the research only reserves samples whose occurrence-frequencies are ± 10dB of the maximum frequency. The results show that the correct rate of the SVM classifier is higher than 90%.展开更多
In order to evaluate the level of the coal mine essential safety management, the comprehensive index system was designed base on the connotation principle of the mine essential safety management. Due to the disadvanta...In order to evaluate the level of the coal mine essential safety management, the comprehensive index system was designed base on the connotation principle of the mine essential safety management. Due to the disadvantage of index weight setting by subjective idea in the former method, support vector classification algorithm was used to assess the level of coal mine essential safety management. According to the advantages of the global search capability of the genetic algorithm, support vector classification parameters optimization method was proposed based on genetic algorithm, and genetic algorithm-support vector classification model of coal mine essential safety management assessment was established. Learning samples were constructed on the basis of former data of mine essential safety management evaluation. The test results show that the genetic algorithm-support vector classification model has higher evaluation accuracy and good generalization ability, and the advantage of no need for artificial setting of index weight and absence of the subjective factors influence to evaluation results.展开更多
This paper proposes robust version to unsupervised classification algorithm based on modified robust version of primal problem of standard SVMs, which directly relaxes it with label variables to a semi-definite progra...This paper proposes robust version to unsupervised classification algorithm based on modified robust version of primal problem of standard SVMs, which directly relaxes it with label variables to a semi-definite programming. Numerical results confirm the robustness of the proposed method.展开更多
文摘Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learning algorithm, is used to rank the features extracted for detecting intrusions and generate intrusion detection models. Feature ranking is a very critical step when building the model. RSC performs feature ranking before generating rules, and converts the feature ranking to minimal hitting set problem addressed by using genetic algorithm (GA). This is done in classical approaches using Support Vector Machine (SVM) by executing many iterations, each of which removes one useless feature. Compared with those methods, our method can avoid many iterations. In addition, a hybrid genetic algorithm is proposed to increase the convergence speed and decrease the training time of RSC. The models generated by RSC take the form of'IF-THEN' rules, which have the advantage of explication. Tests and comparison of RSC with SVM on DARPA benchmark data showed that for Probe and DoS attacks both RSC and SVM yielded highly accurate results (greater than 99% accuracy on testing set).
基金the National Natural Science Foundation of China under Grant No.61170269,No.61170272,No.61202082,No.61003285,and the Fundamental Research Funds for the Central Universities under Grant No.BUPT2013RC0308,No.BUPT2013RC0311
文摘Group distance coding is suitable for secret communication covered by printed documents. However there is no effective method against it. The study found that the hiding method will make group distances of text lines coverage on specified values, and make variances of group distances among N-Window text lines become small. Inspired by the discovery, the research brings out a Support Vector Machine （SVM） based steganalysis algorithm. To avoid the disturbance of large difference among words length from same line, the research only reserves samples whose occurrence-frequencies are ± 10dB of the maximum frequency. The results show that the correct rate of the SVM classifier is higher than 90%.
基金Supported by the National Nature Science Foundation of China （51174082） the Doctoral Research Fund of Henan Polytechnic University （B2010-69 B2011-056） the Guidance Program for Science and Technology Research of China National Coal Association （MTKJ2010-383）
文摘In order to evaluate the level of the coal mine essential safety management, the comprehensive index system was designed base on the connotation principle of the mine essential safety management. Due to the disadvantage of index weight setting by subjective idea in the former method, support vector classification algorithm was used to assess the level of coal mine essential safety management. According to the advantages of the global search capability of the genetic algorithm, support vector classification parameters optimization method was proposed based on genetic algorithm, and genetic algorithm-support vector classification model of coal mine essential safety management assessment was established. Learning samples were constructed on the basis of former data of mine essential safety management evaluation. The test results show that the genetic algorithm-support vector classification model has higher evaluation accuracy and good generalization ability, and the advantage of no need for artificial setting of index weight and absence of the subjective factors influence to evaluation results.
基金supported by the Key Project of the National Natural Science Foundation of China under Grant No.10631070
文摘This paper proposes robust version to unsupervised classification algorithm based on modified robust version of primal problem of standard SVMs, which directly relaxes it with label variables to a semi-definite programming. Numerical results confirm the robustness of the proposed method.
