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

Chandrasekhar, A. M.; Raghuveer, K.

doi:10.1007/978-1-4614-6154-8_49

Cited by 14 publications

(6 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With RBM in [17], the accuracy of intrusion detection is about 0.94, while the test dataset is not published. Adaboost [29], Auto‐encoder Network [30], Bayesian Network [31], Flexible Neural Tree [32], Radial SVM [33] have been used in other literatures, and the experiment results show that the accuracy is not as well as LSSVM‐IDS proposed in [34]. The proposed IDS model using NDNN is also compared with other ten methods.…”

Section: Methodsmentioning

confidence: 99%

Network intrusion detection algorithm based on deep neural network

Jia

Wang

2019

IET Information Security

View full text Add to dashboard Cite

Section: Methodsmentioning

confidence: 99%

Network intrusion detection algorithm based on deep neural network

Jia

Wang

2019

IET Information Security

View full text Add to dashboard Cite

“…where I(C; f i ) is the amount of information that feature f i carries about the class C. MR, in (4), is the relative minimum redundancy of feature f i against feature f s and is defined by (5).…”

Section: Flexible Mutual Information Based Feature Selectionmentioning

confidence: 99%

“…It is clear that LSSVM-IDS + FMIFS enjoys the best results at 99.94% accuracy, 98.93% detection rate and 0.28% false positive rate. [45] 99.45 n/a n/a n/a n/a SVM + SA and FS [46] 99.42 n/a n/a n/a n/a TUIDS [19] 94.76 n/a n/a n/a n/a Radial SVM [5] n/a 98.94 97.11 97.80 97.78 Table 6 shows a comparison with the results achieved by CSV-ISVM proposed in [20] that has been tested on Kyoto 2006+ dataset. Through the results, both systems show continuous improvement in detection rates and reduction in false positive rates.…”

Section: Comparative Studymentioning

confidence: 99%

“…Bagged boosting-based on C5 decision trees [2] and Kernel Miner [3] are two of the earliest attempts to build intrusion detection schemes. Methods proposed in [4] and [5] have successfully applied machine learning techniques, such as Support Vector Ma- • Z. Tan is with the Services, Cybersecurity and Safety Group, University of Twente, Enschede, Netherlands. E-mail: Z.Tan@utwente.nl.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm

Ambusaidi

Nanda

et al. 2016

IEEE Trans. Comput.

528

241

View full text Add to dashboard Cite

Redundant and irrelevant features in data have caused a long-term problem in network traffic classification. These features not only slow down the process of classification but also prevent a classifier from making accurate decisions, especially when coping with big data. In this paper, we propose a mutual information based algorithm that analytically selects the optimal feature for classification. This mutual information based feature selection algorithm can handle linearly and nonlinearly dependent data features. Its effectiveness is evaluated in the cases of network intrusion detection. An Intrusion Detection System (IDS), named Least Square Support Vector Machine based IDS (LSSVM-IDS), is built using the features selected by our proposed feature selection algorithm. The performance of LSSVM-IDS is evaluated using three intrusion detection evaluation datasets, namely KDD Cup 99, NSL-KDD and Kyoto 2006+ dataset. The evaluation results show that our feature selection algorithm contributes more critical features for LSSVM-IDS to achieve better accuracy and lower computational cost compared with the state-of-the-art methods.

show abstract

“…As shown in literature review, a significant number of stateof-the-art IDSs, such as the ones in [20]- [24] , were evaluated using "10% of KDD Cup 99" data. Therefore, training and testing our system on the "10% KDD Cup 99" data can assist to provide a fair comparisons with those systems.…”

Section: A Training and Testing Datasetmentioning

confidence: 99%

A Novel Feature Selection Approach for Intrusion Detection Data Classification

Ambusaidi

Tan

et al. 2014

2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications

View full text Add to dashboard Cite

Intrusion Detection Systems (IDSs) play a significant role in monitoring and analyzing daily activities occurring in computer systems to detect occurrences of security threats. However, the routinely produced analytical data from computer networks are usually of very huge in size. This creates a major challenge to IDSs, which need to examine all features in the data to identify intrusive patterns. The objective of this study is to analyze and select the more discriminate input features for building computationally efficient and effective schemes for an IDS. For this, a hybrid feature selection algorithm in combination with wrapper and filter selection processes is designed in this paper. Two main phases are involved in this algorithm. The upper phase conducts a preliminary search for an optimal subset of features, in which the mutual information between the input features and the output class serves as a determinant criterion. The selected set of features from the previous phase is further refined in the lower phase in a wrapper manner, in which the Least Square Support Vector Machine (LSSVM) is used to guide the selection process and retain optimized set of features. The efficiency and effectiveness of our approach is demonstrated through building an IDS and a fair comparison with other stateof-the-art detection approaches. The experimental results show that our hybrid model is promising in detection compared to the previously reported results.

show abstract

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

Cited by 14 publications

References 11 publications

Network intrusion detection algorithm based on deep neural network

Network intrusion detection algorithm based on deep neural network

Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm

A Novel Feature Selection Approach for Intrusion Detection Data Classification

Contact Info

Product

Resources

About