Comparison of Two Feature Selection Methods in Intrusion Detection Systems

Fadaeieslam, Mohammad Javad; Minaei-Bidgoli, Behrouz; Soryani, Mohsen

doi:10.1109/cit.2007.99

Cited by 10 publications

(2 citation statements)

References 7 publications

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“…Some are continuous variables, whereas others are discrete. To make computation easier, it is important to discretize the continuous variables [16].…”

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confidence: 99%

Machine-Learning-Based Feature Selection Techniques for Large-Scale Network Intrusion Detection

Al-Jarrah

Siddiqui

ElSalamouny

et al. 2014

2014 IEEE 34th International Conference on Distributed Computing Systems Workshops

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Nowadays, we see more and more cyber-attacks on major Internet sites and enterprise networks. Intrusion Detection System (IDS) is a critical component of such infrastructure defense mechanism. IDS monitors and analyzes networks' activities for potential intrusions and security attacks. Machinelearning (ML) models have been well accepted for signaturebased IDSs due to their learnability and flexibility. However, the performance of existing IDSs does not seem to be satisfactory due to the rapid evolution of sophisticated cyber threats in recent decades. Moreover, the volumes of data to be analyzed are beyond the ability of commonly used computer software and hardware tools. They are not only large in scale but fast in/out in terms of velocity. In big data IDS, the one must find an efficient way to reduce the size of data dimensions and volumes. In this paper, we propose novel feature selection methods, namely, RF-FSR (RandomForest-Forward Selection Ranking) and RF-BER (RandomForest-Backward Elimination Ranking). The features selected by the proposed methods were tested and compared with three of the most well-known feature sets in the IDS literature. The experimental results showed that the selected features by the proposed methods effectively improved their detection rate and false-positive rate, achieving 99.8% and 0.001% on well-known KDD-99 dataset, respectively.

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“…Some are continuous variables, whereas others are discrete. To make computation easier, it is important to discretize the continuous variables [16].…”

mentioning

confidence: 99%

Machine-Learning-Based Feature Selection Techniques for Large-Scale Network Intrusion Detection

Al-Jarrah

Siddiqui

ElSalamouny

et al. 2014

2014 IEEE 34th International Conference on Distributed Computing Systems Workshops

View full text Add to dashboard Cite

show abstract

“…SVDD is described as a semiinfinite linear program that can be solved with standard techniques. Fadaeieslam et al [9] proposed FS based on decision dependent correlation using the SVM classifier. Suebsing and Hiransakolwong [10] applied Euclidean distance for selecting a subset of features to build a model for the detection of known and unknown patterns.…”

Section: Introductionmentioning

confidence: 99%

A new feature selection model based on ID3 and bees algorithm for intrusion detection system

Eesa¹,

Orman²,

Brifcani³

2015

Turk J Elec Eng & Comp Sci

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Abstract:Intrusion detection systems (IDSs) have become a necessary component of computers and information security framework. IDSs commonly deal with a large amount of data traffic and these data may contain redundant and unimportant features. Choosing the best quality of features that represent all of the data and exclude the redundant features is a crucial topic in IDSs. In this paper, a new combination approach based on the ID3 algorithm and the bees algorithm (BA) is proposed to select the optimal subset of features for an IDS. The BA is used to generate a subset of features, and the ID3 algorithm is used as a classifier. The proposed model is applied on KDD Cup 99 dataset. The obtained results show that the feature subset generated by the proposed ID3-BA gives a higher accuracy and detection rate with a lower false alarm rate when compared to the results obtained by using all features.

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Improved Genetic Algorithm for Intrusion Detection System

Pal

Parashar

2014

2014 International Conference on Computational Intelligence and Communication Networks

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Comparison of Two Feature Selection Methods in Intrusion Detection Systems

Cited by 10 publications

References 7 publications

Machine-Learning-Based Feature Selection Techniques for Large-Scale Network Intrusion Detection

Machine-Learning-Based Feature Selection Techniques for Large-Scale Network Intrusion Detection

A new feature selection model based on ID3 and bees algorithm for intrusion detection system

Improved Genetic Algorithm for Intrusion Detection System

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