数据挖掘技术在入侵检测系统中的应用研究
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摘要
数据挖掘(Data Mining)技术是从已知数据集中挖掘有用知识的技术。近十年来的有关研究结果表明,将数据挖掘技术应用于入侵检测系统(Intrusion Detection System,IDS),对有效地进行特征选择,建立合适的检测模型,最终提高入侵检测系统的入侵检测能力,降低其误报率和漏报率有着十分重要的意义。
虽然将数据挖掘技术应用于IDS时可借鉴的算法较多,但由于能适合所有情形的数据挖掘算法是不存在的,所以算法研究方面至今尚无权威性的成果;同时,很多研究过于注重理论性与技术性,忽略了所引入的数据挖掘算法的复杂度对入侵检测系统效率的影响;此外,目前成熟的IDS产品基本都采用基于规则的检测方法,这类IDS将数据包与规则库的规则进行精确匹配,如果攻击模式很常见或过于特殊,就容易产生误报或漏报,从而降低入侵检测的准确率。
为此,本文以江苏省教育厅的研究项目“基于数据挖掘的入侵检测技术的研究”(02SJD520002)为背景,以适应IDS数据源特点、降低复杂度、提高效率为目标,对数据挖掘算法进行研究,包括特征选择算法、数值归约算法、聚类算法;也以增强灵活性、降低误报率和漏报率为目标,对基于数据挖掘的入侵检测方法进行研究。
论文针对入侵检测系统中被检测数据的特点,提出了一种适用于IDS的多次模糊迭代特征选择算法和一种适用于IDS的基于相关性度量的特征选择算法。多次模糊迭代特征选择算法由在属性空间中搜索特征子集、评估每个候选特征子集和分类这三个步骤组成,有与之相应的搜索算法和评估函数;该算法通过多次迭代去除特征值集的冗余特征得到精确度较高的特征值集,使用模糊逻辑得到与精确度要求相应的取值范围;由于单纯对数据进行操作,该算法能更客观地分析数据;论文还基于KDD Cup 99数据集对该算法进行了仿真分析;并将实验结果与特征可视化结果进行了比较;实验结果表明该算法在IDS数据集上可取得良好的特征选择效果。基于相关性度量的特征选择算法对特征值进行模糊处理,计算特征相关性度量值,按度量值降序排列特征,再基于该特征序列进行特征选择;以分类器作为评估系统,以KDD Cup 99为数据源的仿真结果验证了该算法能在不影响效率的同时降低时间复杂度。
论文还以提高IDS中分类挖掘的效率为目标,提出了一种适用于IDS中数据分类的数值归约算法,该算法一方面用值域来减少特征值数目,一方面将孤立的点放大为一个区域以预测类似行为;以KDD Cup 99数据集为数据源、以决策树分类算法为例的仿真实验结果表明,该算法能在降低已有分类算法的时间复杂度的同时使分类准确率有所提升。
聚类分析常被用于IDS的入侵检测阶段。本文针对经典模糊C-均值算法FCM的缺陷,提出了一种基于层次聚类的模糊聚类算法HFC,该算法采用凝聚的层次聚类方法,快速地发现高度聚集的数据区域,并对这些高密度区域进一步分析与合并,通过评估函数的评估,找到最优的聚类方案;仿真实验结果表明,该算法具有较高的聚类精确度和较强的排除噪声的能力;论文还通过基于KDD Cup 99数据集的仿真实验,分析了该算法对IDS中入侵检测的适用性。
为了提高基于规则的IDS的检测能力,论文提出了基于CBR (Case-Based Reasoning,基于案例的推理)的入侵检测方法;描述了实现CBR的步骤;给出了由规则设计和构造案例库的启发式方法;设计了适用于IDS的CBR引擎及案例匹配算法;分别通过基于Snort的规则集、自行开发的攻击平台及离线检测系统的实验和基于在线数据包的实验,验证了CBR对基于规则的IDS检测能力的增强作用。
最后,总结了所做的工作,分析了存在的不足,提出了进一步研究的目标。
论文对数据挖掘技术在入侵检测系统中的应用做了有益的研究。
Data mining is a technique to mine the useful knowledge from the existing data set. In recent ten years, related research results have testified that it is very important to apply data mining technique to intrusion detection system (IDS) for effectively selecting features, properly building detection model as well as improving detection efficiency and decreasing both the false positive rate and the negative rate.
When applying data mining technique to IDS, there are many algorithms to choose, but no algorithm can adapt to all circumstances, so there are still no authoritative results in algorithm research. Meanwhile, many researches lay particular stress on theory and technical aspects, and neglect the influence of algorithm complexity on the detection efficiency. In addition, most of the mature IDS products adopt the detection method based on rules to make the exact match between the packet and rule. If rules are too common or special, there will be many false or missing reports. That will reduce the accuracy of intrusion detection.
Therefore, based on the research project“Research on the Intrusion Detection Technology Based on Data Mining”(02SJD520002) sponsored by the Education Bureau of Jiangsu Province, this dissertation makes researches on the algorithms adapting to IDS such as feature selection algorithm, numerosity reduction algorithm, and clustering algorithm with the targets of meeting the characteristics of the data source in IDS, reducing the complexity of algorithm and improving the efficiency. It also makes researches on the intrusion detection method based on data mining techniques with the target of enhancing the flexibility and reducing both the false positive rate and the negative rate.
Based on the characteristics of the detected data in IDS, a Multi-time Fuzzy Iterating Feature Selection Algorithm adapting to IDS and a Correlation Measure-Based Feature Selection Algorithm for IDS are proposed in this dissertation. Multi-time Fuzzy Iterating Feature Selection Algorithm includes three steps, one is searching feature subsets from feature space, the other is valuating every candidate feature subset, and the last is classification. Corresponding search algorithm and valuation function are designed in this algorithm. The algorithm eliminates redundant features through multi-time iterating to get high precision feature value set, and uses fuzzy logic to get the value range meeting the need of precision. This algorithm can analyze data more objectively than the algorithms with field knowledge because it only operates on datasets. Simulation experiment and analysis are performed on the algorithm based on the KDD Cup 99 data set, and the experiment results are compared with feature visualization results. The results indicate: this algorithm can get good feature selection effect on IDS datasets. The Correlation Measure-Based Feature Selection Algorithm carries fuzzy process to feature value, calculates the degree of feature correlation, arranges features with descending order of the degree, then carry on feature selection based on the obtained feature sequence. The validity of this designed algorithm has been verified by doing experiments on the assessment system based on classifier and the dataset from the KDD Cup 99.
In order to improve the mining efficiency of data classification in IDS, this dissertation also proposes a Numerosity Reduction Algorithm Adapting to the Data Classification in IDS, which uses range of values to reduce the amount of feature values and expands an isolated point to a region in order to forecast similar behavior. The results of experiments with decision tree algorithms and the KDD Cup 99 dataset have shown that this algorithm can reduce the time complexity and increase the classifying accuracy of the existing classification algorithms.
Clustering is widely used in intrusion detection phase. In this dissertation, a hierarchical fuzzy clustering (HFC) algorithm is put forward to overcome the limitation of classical fuzzy C-means (FCM) algorithm. HFC can fast discover the high concentrated data areas by the agglomerative hierarchical clustering method, analyze and merge the data areas, and then use the evaluation function to find the optimum clustering scheme. The experimental results indicate that HFC has higher clustering precision and higher ability of excluding noises. The applicability of HFC algorithm to IDS is analyzed by doing experiments on KDD99 dataset.
In order to improve the detection ability of rule-based IDS, this dissertation puts forward an Intrusion Detection Method based on CBR (Case-Based Reasoning). The steps of implementing CBR are described, several illuminative methods for designing and constructing case base from rules are proposed, and a CBR engine as well as the case matching algorithms is designed. Finally, the experiment based on Snort rule sets, the attack platform, the offline detection system, and the experiment based on online packets are performed which verify the effect of CBR for enhancing the detection ability of rule-based IDS.
Finally, the works are summarized, the shortages are analyzed, and the targets of further research are given.
This dissertation has done useful researches on the applications of Data Mining Technique in Intrusion Detection System.
虽然将数据挖掘技术应用于IDS时可借鉴的算法较多,但由于能适合所有情形的数据挖掘算法是不存在的,所以算法研究方面至今尚无权威性的成果;同时,很多研究过于注重理论性与技术性,忽略了所引入的数据挖掘算法的复杂度对入侵检测系统效率的影响;此外,目前成熟的IDS产品基本都采用基于规则的检测方法,这类IDS将数据包与规则库的规则进行精确匹配,如果攻击模式很常见或过于特殊,就容易产生误报或漏报,从而降低入侵检测的准确率。
为此,本文以江苏省教育厅的研究项目“基于数据挖掘的入侵检测技术的研究”(02SJD520002)为背景,以适应IDS数据源特点、降低复杂度、提高效率为目标,对数据挖掘算法进行研究,包括特征选择算法、数值归约算法、聚类算法;也以增强灵活性、降低误报率和漏报率为目标,对基于数据挖掘的入侵检测方法进行研究。
论文针对入侵检测系统中被检测数据的特点,提出了一种适用于IDS的多次模糊迭代特征选择算法和一种适用于IDS的基于相关性度量的特征选择算法。多次模糊迭代特征选择算法由在属性空间中搜索特征子集、评估每个候选特征子集和分类这三个步骤组成,有与之相应的搜索算法和评估函数;该算法通过多次迭代去除特征值集的冗余特征得到精确度较高的特征值集,使用模糊逻辑得到与精确度要求相应的取值范围;由于单纯对数据进行操作,该算法能更客观地分析数据;论文还基于KDD Cup 99数据集对该算法进行了仿真分析;并将实验结果与特征可视化结果进行了比较;实验结果表明该算法在IDS数据集上可取得良好的特征选择效果。基于相关性度量的特征选择算法对特征值进行模糊处理,计算特征相关性度量值,按度量值降序排列特征,再基于该特征序列进行特征选择;以分类器作为评估系统,以KDD Cup 99为数据源的仿真结果验证了该算法能在不影响效率的同时降低时间复杂度。
论文还以提高IDS中分类挖掘的效率为目标,提出了一种适用于IDS中数据分类的数值归约算法,该算法一方面用值域来减少特征值数目,一方面将孤立的点放大为一个区域以预测类似行为;以KDD Cup 99数据集为数据源、以决策树分类算法为例的仿真实验结果表明,该算法能在降低已有分类算法的时间复杂度的同时使分类准确率有所提升。
聚类分析常被用于IDS的入侵检测阶段。本文针对经典模糊C-均值算法FCM的缺陷,提出了一种基于层次聚类的模糊聚类算法HFC,该算法采用凝聚的层次聚类方法,快速地发现高度聚集的数据区域,并对这些高密度区域进一步分析与合并,通过评估函数的评估,找到最优的聚类方案;仿真实验结果表明,该算法具有较高的聚类精确度和较强的排除噪声的能力;论文还通过基于KDD Cup 99数据集的仿真实验,分析了该算法对IDS中入侵检测的适用性。
为了提高基于规则的IDS的检测能力,论文提出了基于CBR (Case-Based Reasoning,基于案例的推理)的入侵检测方法;描述了实现CBR的步骤;给出了由规则设计和构造案例库的启发式方法;设计了适用于IDS的CBR引擎及案例匹配算法;分别通过基于Snort的规则集、自行开发的攻击平台及离线检测系统的实验和基于在线数据包的实验,验证了CBR对基于规则的IDS检测能力的增强作用。
最后,总结了所做的工作,分析了存在的不足,提出了进一步研究的目标。
论文对数据挖掘技术在入侵检测系统中的应用做了有益的研究。
Data mining is a technique to mine the useful knowledge from the existing data set. In recent ten years, related research results have testified that it is very important to apply data mining technique to intrusion detection system (IDS) for effectively selecting features, properly building detection model as well as improving detection efficiency and decreasing both the false positive rate and the negative rate.
When applying data mining technique to IDS, there are many algorithms to choose, but no algorithm can adapt to all circumstances, so there are still no authoritative results in algorithm research. Meanwhile, many researches lay particular stress on theory and technical aspects, and neglect the influence of algorithm complexity on the detection efficiency. In addition, most of the mature IDS products adopt the detection method based on rules to make the exact match between the packet and rule. If rules are too common or special, there will be many false or missing reports. That will reduce the accuracy of intrusion detection.
Therefore, based on the research project“Research on the Intrusion Detection Technology Based on Data Mining”(02SJD520002) sponsored by the Education Bureau of Jiangsu Province, this dissertation makes researches on the algorithms adapting to IDS such as feature selection algorithm, numerosity reduction algorithm, and clustering algorithm with the targets of meeting the characteristics of the data source in IDS, reducing the complexity of algorithm and improving the efficiency. It also makes researches on the intrusion detection method based on data mining techniques with the target of enhancing the flexibility and reducing both the false positive rate and the negative rate.
Based on the characteristics of the detected data in IDS, a Multi-time Fuzzy Iterating Feature Selection Algorithm adapting to IDS and a Correlation Measure-Based Feature Selection Algorithm for IDS are proposed in this dissertation. Multi-time Fuzzy Iterating Feature Selection Algorithm includes three steps, one is searching feature subsets from feature space, the other is valuating every candidate feature subset, and the last is classification. Corresponding search algorithm and valuation function are designed in this algorithm. The algorithm eliminates redundant features through multi-time iterating to get high precision feature value set, and uses fuzzy logic to get the value range meeting the need of precision. This algorithm can analyze data more objectively than the algorithms with field knowledge because it only operates on datasets. Simulation experiment and analysis are performed on the algorithm based on the KDD Cup 99 data set, and the experiment results are compared with feature visualization results. The results indicate: this algorithm can get good feature selection effect on IDS datasets. The Correlation Measure-Based Feature Selection Algorithm carries fuzzy process to feature value, calculates the degree of feature correlation, arranges features with descending order of the degree, then carry on feature selection based on the obtained feature sequence. The validity of this designed algorithm has been verified by doing experiments on the assessment system based on classifier and the dataset from the KDD Cup 99.
In order to improve the mining efficiency of data classification in IDS, this dissertation also proposes a Numerosity Reduction Algorithm Adapting to the Data Classification in IDS, which uses range of values to reduce the amount of feature values and expands an isolated point to a region in order to forecast similar behavior. The results of experiments with decision tree algorithms and the KDD Cup 99 dataset have shown that this algorithm can reduce the time complexity and increase the classifying accuracy of the existing classification algorithms.
Clustering is widely used in intrusion detection phase. In this dissertation, a hierarchical fuzzy clustering (HFC) algorithm is put forward to overcome the limitation of classical fuzzy C-means (FCM) algorithm. HFC can fast discover the high concentrated data areas by the agglomerative hierarchical clustering method, analyze and merge the data areas, and then use the evaluation function to find the optimum clustering scheme. The experimental results indicate that HFC has higher clustering precision and higher ability of excluding noises. The applicability of HFC algorithm to IDS is analyzed by doing experiments on KDD99 dataset.
In order to improve the detection ability of rule-based IDS, this dissertation puts forward an Intrusion Detection Method based on CBR (Case-Based Reasoning). The steps of implementing CBR are described, several illuminative methods for designing and constructing case base from rules are proposed, and a CBR engine as well as the case matching algorithms is designed. Finally, the experiment based on Snort rule sets, the attack platform, the offline detection system, and the experiment based on online packets are performed which verify the effect of CBR for enhancing the detection ability of rule-based IDS.
Finally, the works are summarized, the shortages are analyzed, and the targets of further research are given.
This dissertation has done useful researches on the applications of Data Mining Technique in Intrusion Detection System.
引文
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