An Intrusion-Detection Model Based on Fuzzy Class-Association-Rule Mining Using Genetic Network Programming

Mabu, Shingo; Chen, Ci; Lu, Nannan; Shimada, Kaoru; Hirasawa, Kotaro

doi:10.1109/tsmcc.2010.2050685

Cited by 173 publications

(73 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This dataset is still the most trustful and credible public benchmark dataset [53,54,55,56,57,58,59] for evaluating network intrusion detection algorithms. In the dataset, 41 features including 9 categorical features and 32 continuous features are extracted for each network connection.…”

Section: Methodsmentioning

confidence: 99%

“…Zanero and Savaresi [25] first use unsupervised clustering to reduce the network packet payload to a tractable size, and then a traditional anomaly detection algorithm is applied to intrusion detection. Mabu et al [49] detect intrusions by mining fuzzy class association rules using genetic network programming. Panigrahi and Sural [51] detect intrusions using fuzzy logic, which combines evidence from a user's current and past behaviors.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection

Gao

Wang

et al. 2014

IEEE Trans. Cybern.

146

View full text Add to dashboard Cite

Current network intrusion detection systems (NIDS) lack the adaptability to the frequently changing network environments. Furthermore, intrusion detection in the new distributed architectures is now a major requirement. In this paper, we propose two online Adaboost-based intrusion detection algorithms. In the first algorithm, a traditional online Adaboost process is used where decision stumps are used as weak classifiers. In the second algorithm, an improved online Adaboost process is proposed, and online GMMs are used as weak classifiers. We further propose a distributed intrusion detection framework, in which a local parameterized detection model is constructed in each node using the online Adaboost algorithm. A global detection model is constructed in each node by combining the local parametric models using a small number of samples in the node. This combination is achieved using an algorithm based on particle swarm optimization (PSO) and support vector machines (SVM). The global model in each node is used to detect intrusions. Experimental results show that the improved online Adaboost process with GMMs obtains a higher detection rate and a lower false alarm rate than the traditional online Adaboost process which uses decision stumps. Both the algorithms outperform existing intrusion detection algorithms. It is also shown that our PSO and SVM-based algorithm effectively combines the local detection models into the global model in each node: the global model in a node can handle the intrusion types which are found in other nodes, without sharing the samples of these intrusion types.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection

Gao

Wang

et al. 2014

IEEE Trans. Cybern.

146

View full text Add to dashboard Cite

show abstract

“…The created rule-database is utilized for classification. This data mining method has been applied to network intrusion detection systems [8] and medical systems [9]. GNP can handle a huge database efficiently, and the extracted rules have a form of easy-to-understand.…”

Section: Genetic Network Programing Gnpmentioning

confidence: 99%

A random-forests-based classifier using class association rules and its application to an intrusion detection system

Mabu

Gotoh

Obayashi

et al. 2016

Artif Life Robotics

Self Cite

View full text Add to dashboard Cite

“…It has been analyzed that the use of Fuzzy Logic makes the detection process synonym to real world, defining the candidate set on the basis of uncertainty in support and confidence framework [3] while the Genetic Algorithm optimizes the detection process making the intrusion detection effective and optimizing the membership function. Further, these membership functions and patterns are stored for future use [10].…”

Section: Future Opportunitiesmentioning

confidence: 99%

A Survey on Terrorist Network Mining: Current Trends and Opportunities

Chaurasia¹

2012

IJCSES

View full text Add to dashboard Cite

show abstract

An Intrusion-Detection Model Based on Fuzzy Class-Association-Rule Mining Using Genetic Network Programming

Cited by 173 publications

References 21 publications

Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection

Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection

A random-forests-based classifier using class association rules and its application to an intrusion detection system

A Survey on Terrorist Network Mining: Current Trends and Opportunities

Contact Info

Product

Resources

About