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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 modem 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). 相似文献
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应用于分级网络的可扩展拓扑聚集算法 总被引：1，自引：0，他引：1
拓扑聚集在可扩展的路由机制中十分重要，如何在简化网络拓扑信息的同时获得好的性能，是拓扑聚集算法的关键问题．本提出了一个描述逻辑链路的新方法，能够简单有效地描述加性和乘性参数约束的网络，并扩展到多参数约束的情况．在此基础上，提出了一个改进的星型聚集算法．仿真结果表明该算法具有很好的性能． 相似文献
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Intrusion detection using rough set classification 总被引：2，自引：0，他引：2
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 compa 相似文献
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INTRODUCTIONAgoodsearchschemeisacriticalcompo nentinpeer to peernetwork .Aneffectivesearchschemeshouldhavegoodscalability ,highsearchefficiency ,goodpeerfailuretoler anceandsupporttokeywordpartial match .Existingsearchingschemescanbeclassifiedintothreecat… 相似文献
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This paper presents a "cluster" based search scheme in peer-to-peer network. The idea is based on the fact that data distribution in an information society has structured feature. We designed an algorithm to cluster peers that have similar interests. When receiving a query request, a peer will preferentially forward it to another peer which belongs to the same cluster and shares more similar interests. By this way search efficiency will be remarkably improved and at the same time good resilience against peer failure (the ability to withstand peer failure) is reserved. 相似文献
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