On the versatility of radial basis function neural networks: A case study in the field of intrusion detection

Fisch, Dominik; Hofmann, Alexander; Sick, Bernhard

doi:10.1016/j.ins.2010.02.023

Cited by 52 publications

(32 citation statements)

References 64 publications

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“…Some of these techniques that are widely used for an intrusion detection are statistic (Lazarevic et al, 2003), hidden Markov model (Ye and Borror, 2004), artificial neural network (Debar et al, 1992;Novikov et al, 2006;Ramadas and Tjaden, 2003;Fisch et al, 2010), fuzzy logic (Saniee Abadeh et al, 2007;Chimphlee et al, 2006;Toosi and Kahani, 2007), rule learning (Cohen, 1995;Xuren et al, 2006) and outlier detection schema (Lazarevic et al, 2003).…”

Section: Related Workmentioning

confidence: 99%

Mutual information-based feature selection for intrusion detection systems

Amiri

Yousefi

Lucas

et al. 2011

Journal of Network and Computer Applications

392

170

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Section: Related Workmentioning

confidence: 99%

Mutual information-based feature selection for intrusion detection systems

Amiri

Yousefi

Lucas

et al. 2011

Journal of Network and Computer Applications

392

170

View full text Add to dashboard Cite

“…To date, most researches on ID have approached it from a classification standpoint, such as [26,50,59,63]. They perform a 2-class classification of network traffic: normal/anomalous in anomaly-based ID, and intrusive/non-intrusive in misusebased ID.…”

Section: Sql Injection Attacksmentioning

confidence: 99%

idMAS-SQL: Intrusion Detection Based on MAS to Detect and Block SQL injection through data mining

Pinzón¹,

Paz²,

Herrero³

et al. 2013

Information Sciences

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a b s t r a c tThis study presents a multiagent architecture aimed at detecting SQL injection attacks, which are one of the most prevalent threats for modern databases. The proposed architecture is based on a hierarchical and distributed strategy where the functionalities are structured on layers. SQL-injection attacks, one of the most dangerous attacks to online databases, are the focus of this research. The agents in each one of the layers are specialized in specific tasks, such as data gathering, data classification, and visualization. This study presents two key agents under a hybrid architecture: a classifier agent that incorporates a Case-Based Reasoning engine employing advanced algorithms in the reasoning cycle stages, and a visualizer agent that integrates several techniques to facilitate the visual analysis of suspicious queries. The former incorporates a new classification model based on a mixture of a neural network and a Support Vector Machine in order to classify SQL queries in a reliable way. The latter combines clustering and neural projection techniques to support the visual analysis and identification of target attacks. The proposed approach was tested in a real-traffic case study and its experimental results, which validate the performance of the proposed approach, are presented in this paper.

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“…Here, ''performance" means that the discriminative properties are evaluated. Examples are [25] (SVM, RBF, FCL, and others), [8] (SVM and Gaussian basis function classifiers), and [66] (SVM, RBF, and a hybrid classifier). Much more interesting here is the work dealing with interrelationships from a theoretical viewpoint.…”

Section: Some Comments On Related Workmentioning

confidence: 99%