Proceedings ArticleDOI
Comparison of Two Feature Selection Methods in Intrusion Detection Systems
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TLDR
A new method for feature selection based on Decision Dependent Correlation (DDC) is proposed and the results on DARPA KDD99 benchmark dataset indicate that the proposed method outperforms Principal Component Analysis (PCA).Abstract:
The quality of features directly affects the performance of classification. Many feature selection methods introduced to remove redundant and irrelevant features, because raw features may reduce accuracy or robustness of classification. In this paper we proposed a new method for feature selection based on Decision Dependent Correlation (DDC). We have used SVM classifier and the results on DARPA KDD99 benchmark dataset indicate that the proposed method outperforms Principal Component Analysis (PCA).read more
Citations
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Proceedings ArticleDOI
Machine-Learning-Based Feature Selection Techniques for Large-Scale Network Intrusion Detection
TL;DR: Novel feature selection methods, namely, RF-FSR ( Random Forest-Forward Selection Ranking) and RF-BER (Random Forest-Backward Elimination Ranking) are proposed and shown that the selected features by the proposed methods effectively improved their detection rate and false-positive rate.
Proceedings ArticleDOI
Intrusion detection system based on Multi-Layer Perceptron Neural Networks and Decision Tree
TL;DR: A method based on the combination of Decision Tree (DT) algorithm and Multi-Layer Perceptron (MLP) ANN is proposed which is able to identify attacks with high accuracy and reliability.
Proceedings ArticleDOI
An Investigation on Intrusion Detection System Using Machine Learning
TL;DR: This paper has conducted a rigorous experiment on Intrusion Detection System (IDS) that uses machine learning algorithms, namely, Random Forest and Support Vector Machine (SVM), and demonstrated the comparison between the model’s performance before and after feature selection of bothRandom Forest and SVM.
Journal ArticleDOI
A new feature selection model based on ID3 and bees algorithm for intrusion detection system
TL;DR: A new combination approach based on the ID3 algorithm and the bees algorithm is proposed to select the optimal subset of features for an IDS, which gives a higher accuracy and detection rate with a lower false alarm rate when compared to the results obtained by using all features.
Proceedings ArticleDOI
Machine Learning Techniques for Feature Reduction in Intrusion Detection Systems: A Comparison
TL;DR: Three methods for feature selection based on Decision trees (DT), Flexible Neural Tree (FNT) and Particle Swarm Optimization (PSO) are compared and results based on comparison of three methods on DARPA KDD99 benchmark dataset indicate that DT has almost better accuracy.
References
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Journal ArticleDOI
Feature deduction and ensemble design of intrusion detection systems
TL;DR: This study investigated the performance of two feature selection algorithms involving Bayesian networks and Classification and Regression Trees and an ensemble of BN and CART and proposed an hybrid architecture for combining different feature selection algorithm for real world intrusion detection.
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