Intrusion detection is regarded as classification in data mining field. However instead of directly mining the classification rules, class association rules, which are then used to construct a classifier, are mined fr...Intrusion detection is regarded as classification in data mining field. However instead of directly mining the classification rules, class association rules, which are then used to construct a classifier, are mined from audit logs. Some attributes in audit logs are important for detecting intrusion but their values are distributed skewedly. A relative support concept is proposed to deal with such situation. To mine class association rules effectively, an algorithms based on FP-tree is exploited. Experiment result proves that this method has better performance.展开更多
Typical association rules consider only items enumerated in transactions. Such rules are referred to as positive association rules. Negative association rules also consider the same items, but in addition consider neg...Typical association rules consider only items enumerated in transactions. Such rules are referred to as positive association rules. Negative association rules also consider the same items, but in addition consider negated items （i. e. absent from transactions）. Negative association rules are useful in market-basket analysis to identify products that conflict with each other or products that complement each other. They are also very convenient for associative classifiers, classifiers that build their classification model based on association rules. Indeed, mining for such rules necessitates the examination of an exponentially large search space. Despite their usefulness, very few algorithms to mine them have been proposed to date. In this paper, an algorithm based on FP tree is presented to discover negative association rules.展开更多
After the digital revolution,large quantities of data have been generated with time through various networks.The networks have made the process of data analysis very difficult by detecting attacks using suitable techn...After the digital revolution,large quantities of data have been generated with time through various networks.The networks have made the process of data analysis very difficult by detecting attacks using suitable techniques.While Intrusion Detection Systems(IDSs)secure resources against threats,they still face challenges in improving detection accuracy,reducing false alarm rates,and detecting the unknown ones.This paper presents a framework to integrate data mining classification algorithms and association rules to implement network intrusion detection.Several experiments have been performed and evaluated to assess various machine learning classifiers based on the KDD99 intrusion dataset.Our study focuses on several data mining algorithms such as;naïve Bayes,decision trees,support vector machines,decision tables,k-nearest neighbor algorithms,and artificial neural networks.Moreover,this paper is concerned with the association process in creating attack rules to identify those in the network audit data,by utilizing a KDD99 dataset anomaly detection.The focus is on false negative and false positive performance metrics to enhance the detection rate of the intrusion detection system.The implemented experiments compare the results of each algorithm and demonstrate that the decision tree is the most powerful algorithm as it has the highest accuracy(0.992)and the lowest false positive rate(0.009).展开更多
Air pollution is a major issue related to national economy and people's livelihood.At present,the researches on air pollution mostly focus on the pollutant emissions in a specific industry or region as a whole,and...Air pollution is a major issue related to national economy and people's livelihood.At present,the researches on air pollution mostly focus on the pollutant emissions in a specific industry or region as a whole,and is a lack of attention to enterprise pollutant emissions from the micro level.Limited by the amount and time granularity of data from enterprises,enterprise pollutant emissions are stll understudied.Driven by big data of air pollution emissions of industrial enterprises monitored in Beijing-Tianjin-Hebei,the data mining of enterprises pollution emissions is carried out in the paper,including the association analysis between different features based on grey association,the association mining between different data based on association rule and the outlier detection based on clustering.The results show that:(1)The industries affecting NOx and SO2 mainly are electric power,heat production and supply industry,metal smelting and processing industries in Beijing-Tianjin-Hebei;(2)These districts nearby Hengshui and Shijiazhuang city in Hebei province form strong association rules;(3)The industrial enterprises in Beijing-Tianjin-Hebei are divided into six clusters,of which three categories belong to outliers with excessive emissions of total vOCs,PM and NH3 respectively.展开更多
基金The work is supported by Chinese NSF(Project No.60073034)
文摘Intrusion detection is regarded as classification in data mining field. However instead of directly mining the classification rules, class association rules, which are then used to construct a classifier, are mined from audit logs. Some attributes in audit logs are important for detecting intrusion but their values are distributed skewedly. A relative support concept is proposed to deal with such situation. To mine class association rules effectively, an algorithms based on FP-tree is exploited. Experiment result proves that this method has better performance.
基金Supported by the National Natural Science Foun-dation of China(70371015) and the Science Foundation of JiangsuUniversity ( 04KJD001)
文摘Typical association rules consider only items enumerated in transactions. Such rules are referred to as positive association rules. Negative association rules also consider the same items, but in addition consider negated items （i. e. absent from transactions）. Negative association rules are useful in market-basket analysis to identify products that conflict with each other or products that complement each other. They are also very convenient for associative classifiers, classifiers that build their classification model based on association rules. Indeed, mining for such rules necessitates the examination of an exponentially large search space. Despite their usefulness, very few algorithms to mine them have been proposed to date. In this paper, an algorithm based on FP tree is presented to discover negative association rules.
文摘After the digital revolution,large quantities of data have been generated with time through various networks.The networks have made the process of data analysis very difficult by detecting attacks using suitable techniques.While Intrusion Detection Systems(IDSs)secure resources against threats,they still face challenges in improving detection accuracy,reducing false alarm rates,and detecting the unknown ones.This paper presents a framework to integrate data mining classification algorithms and association rules to implement network intrusion detection.Several experiments have been performed and evaluated to assess various machine learning classifiers based on the KDD99 intrusion dataset.Our study focuses on several data mining algorithms such as;naïve Bayes,decision trees,support vector machines,decision tables,k-nearest neighbor algorithms,and artificial neural networks.Moreover,this paper is concerned with the association process in creating attack rules to identify those in the network audit data,by utilizing a KDD99 dataset anomaly detection.The focus is on false negative and false positive performance metrics to enhance the detection rate of the intrusion detection system.The implemented experiments compare the results of each algorithm and demonstrate that the decision tree is the most powerful algorithm as it has the highest accuracy(0.992)and the lowest false positive rate(0.009).
基金supported by the National Natural Science Foundation of China[grant number 72271033]the Beijing Municipal Education Commission and Beijing Natural Science Foundation[grant number KZ202110017025]the National Undergraduate Innovation and Entrepreneurship Plan Project(2022J00244).
文摘Air pollution is a major issue related to national economy and people's livelihood.At present,the researches on air pollution mostly focus on the pollutant emissions in a specific industry or region as a whole,and is a lack of attention to enterprise pollutant emissions from the micro level.Limited by the amount and time granularity of data from enterprises,enterprise pollutant emissions are stll understudied.Driven by big data of air pollution emissions of industrial enterprises monitored in Beijing-Tianjin-Hebei,the data mining of enterprises pollution emissions is carried out in the paper,including the association analysis between different features based on grey association,the association mining between different data based on association rule and the outlier detection based on clustering.The results show that:(1)The industries affecting NOx and SO2 mainly are electric power,heat production and supply industry,metal smelting and processing industries in Beijing-Tianjin-Hebei;(2)These districts nearby Hengshui and Shijiazhuang city in Hebei province form strong association rules;(3)The industrial enterprises in Beijing-Tianjin-Hebei are divided into six clusters,of which three categories belong to outliers with excessive emissions of total vOCs,PM and NH3 respectively.
