Effective Value of Decision Tree with KDD 99 Intrusion Detection Datasets for Intrusion Detection System

Lee, Joong‐Hee; Lee, Jong‐Hyouk; Sohn, Sung Won; Ryu, Jong-Ho; Chung, Tai-Myoung

doi:10.1109/icact.2008.4493974

Cited by 61 publications

(36 citation statements)

References 7 publications

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“…They showed that the frequent patterns mined from audit data can be used as reliable user anomaly detection models, and as guidelines for selecting temporal statistical features to build effective classification models. Ye Changguo, et al(2009), adopted fuzzy associate rules mining method and apriori algorithm to abnormal detecting experiment based on network, and improved the support and credit, and compared and analyzing the experiment result Joong-Hee Leet, et al(2008),they generate the decision trees for DoS attack,R2L attack, U2R attack, and Scan attack. The ID3 algorithm is used as the learning algorithm to generate the decision tree automatically, and the DARPA Set is adopted for the training data.…”

Section: Literature Reviewedmentioning

confidence: 99%

Computer Security and Intrusion detection System-A Data Mining Based Approach

Anand¹,

Kumar²

2017

int.jour.sci.res.mana

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Abstract-With an increased understanding of how systems work, intruders have become skilled at determining weaknesses in systems and exploiting them to obtain such increased privileges that they can do anything on the system. Intruders also use patterns of intrusion that are difficult to trace and identify. They frequently use several levels of indirection before breaking into target systems and rarely indulge in sudden bursts of suspicious or anomalous activity. They also cover their tracks so that their activity on the penetrated system is not easily discovered. We must have measures in place to detect security breaches, i.e., identify intruders and intrusions. Intrusion detection systems fill this role and usually form the last line of defense in the overall protection scheme of a computer system. They are useful not only in detecting successful breaches of security, but also in monitoring attempts to breach security, which provides important information for timely countermeasures. This paper focused on how data mining is used for Intrusion detection System

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Section: Literature Reviewedmentioning

confidence: 99%

Computer Security and Intrusion detection System-A Data Mining Based Approach

Anand¹,

Kumar²

2017

int.jour.sci.res.mana

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“…For the evaluation results, we made use of about 10% of the full huge data set which amounted to about 27000 data points. The performance evaluation was made for both testing KDD 99 Winner [36] Multi-Class SVM [38] Layered conditional random fields [39] Columbia Model [40] Decision tree [41] Our Technique…”

Section: Discussionmentioning

confidence: 99%

“…with the other state of art both the neural the table-11, it is apparent that the our technique has obtained a reliable peak scores for all types of intrusions attained 98.94% R2L and U2R attacks we when compared for all 4 types of attacks . We have received the best results as we have employed fuzzythe number of attributes of the data and by the utilization of radial Multi-Class SVM [38] Layered conditional random fields [39] Columbia Model [40] Decision tree [41] Our Technique…”

Section: Comparative Analysismentioning

confidence: 99%

“…KDD 99 Winner [36] Multi-Class SVM [38] Layered conditional random fields [39] Columbia Model [40] Decision tree [41] Our Technique This paper presents an efficient technique for intrusion detection by making use of fuzzy networks and radial support vector machines. The proposed technique consists of initial clustering, fuzzy neural network, formation of SVM vector and the final classification using the radial SVM.…”

Section: U2r Attacksmentioning

confidence: 99%

“…For the evaluation results, we made use of about 10% of the full huge data set which amounted to about 27000 data points. The performance evaluation was made for both testing 0 50 100 KDD 99 Winner [36] PNrule [37] Class SVM [38] Layered conditional random fields [39] Columbia Model [40] Decision tree [41] BPNN [42] Our Technique U2R Attacks 0 50 100 KDD 99 Winner [36] PNrule [37] Class SVM [38] Layered conditional random fields [39] Columbia Model [40] Decision tree [41] BPNN [42] Our Technique…”

Section: U2r Attacksmentioning

confidence: 99%

See 2 more Smart Citations

Fortification of Hybrid Intrusion Detection System Using Variants of Neural Networks and Support Vector Machines

Azad¹,

Raghuveer²

2013

IJNSA

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Intrusion Detection Systems (IDS) form a key part of system defence, where it identifies abnormal activities happening in a computer system. In recent years different soft computing based techniques have been proposed for the development of IDS. On the other hand, intrusion detection is not yet a perfect technology. This has provided an opportunity for data mining to make quite a lot of important contributions in the field of intrusion detection. In this paper we have proposed a new hybrid technique by utilizing data mining techniques such as fuzzy C means clustering, Fuzzy neural network / Neurofuzzy and radial basis function(RBF) SVM for fortification of the intrusion detection system. The proposed technique has five major steps in which, first step is to perform the relevance analysis, and then input data is clustered using Fuzzy C-means clustering. After that, neuro-fuzzy is trained, such that each of the data point is trained with the corresponding neuro-fuzzy classifier

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An Effective Technique for Intrusion Detection Using Neuro-Fuzzy and Radial SVM Classifier

Chandrasekhar¹,

Raghuveer

2013

Lecture Notes in Electrical Engineering

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Effective Value of Decision Tree with KDD 99 Intrusion Detection Datasets for Intrusion Detection System

Cited by 61 publications

References 7 publications

Computer Security and Intrusion detection System-A Data Mining Based Approach

Computer Security and Intrusion detection System-A Data Mining Based Approach

Fortification of Hybrid Intrusion Detection System Using Variants of Neural Networks and Support Vector Machines

An Effective Technique for Intrusion Detection Using Neuro-Fuzzy and Radial SVM Classifier

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