Multivariate Mutual Information-based Feature Selection for Cyber Intrusion Detection

Mohammadi, Sara; Desai, Vraj; Karimipour, Hadis

doi:10.1109/epec.2018.8598326

Cited by 32 publications

(12 citation statements)

References 20 publications

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“…It has previously been shown that information theoretical methods based on mutual information (MI) are powerful approaches for the detection of epistatic interactions [ 39 , 41 , 43 , 44 , 45 , 46 ]. Not only here, but also in many other fields, mutual information has been used as an effective measure for the association between variables including linear as well as non-linear relationships [ 53 , 61 , 63 , 69 , 122 , 123 , 124 , 125 ]. However, the general applicability of a method, particularly in the field of animal and plant breeding, requires it to be usable for qualitative as well as quantitative phenotypes.…”

Section: Discussionmentioning

confidence: 99%

MIDESP: Mutual Information-Based Detection of Epistatic SNP Pairs for Qualitative and Quantitative Phenotypes

Heinrich

Ramzan

Rajavel

et al. 2021

Biology

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The interactions between SNPs result in a complex interplay with the phenotype, known as epistasis. The knowledge of epistasis is a crucial part of understanding genetic causes of complex traits. However, due to the enormous number of SNP pairs and their complex relationship to the phenotype, identification still remains a challenging problem. Many approaches for the detection of epistasis have been developed using mutual information (MI) as an association measure. However, these methods have mainly been restricted to case–control phenotypes and are therefore of limited applicability for quantitative traits. To overcome this limitation of MI-based methods, here, we present an MI-based novel algorithm, MIDESP, to detect epistasis between SNPs for qualitative as well as quantitative phenotypes. Moreover, by incorporating a dataset-dependent correction technique, we deal with the effect of background associations in a genotypic dataset to separate correct epistatic interaction signals from those of false positive interactions resulting from the effect of single SNP×phenotype associations. To demonstrate the effectiveness of MIDESP, we apply it on two real datasets with qualitative and quantitative phenotypes, respectively. Our results suggest that by eliminating the background associations, MIDESP can identify important genes, which play essential roles for bovine tuberculosis or the egg weight of chickens.

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Section: Discussionmentioning

confidence: 99%

MIDESP: Mutual Information-Based Detection of Epistatic SNP Pairs for Qualitative and Quantitative Phenotypes

Heinrich

Ramzan

Rajavel

et al. 2021

Biology

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“…In 1948, Shannon introduced the concept of representing the measuring value of non-linear relationships between two variables, which is known as the concept of mutual information (MI), in the future [14]. The concept was developed to measure the non-linear relationships of three or more variables, further known as MMI.…”

Section: Multivariate Mutual Information (Mmi)mentioning

confidence: 99%

Performance of multivariate mutual information and autocorrelation encoding methods for the prediction of protein-protein interactions

Bustamam

Sunggawa

Siswantining

2022

IJ-AI

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<span lang="EN-US">Protein interactions play an essential role in the study of how an organism can be infected with a disease and also its effects. One of the challenges in computational methods in the prediction of protein-protein interactions is how to represent a sequence of amino acids in a vector so that it can be used in machine learning to create a model that can predict whether or not an interaction occurs in a protein pair. This paper examined the qualitative feature encoding methods of amino acid sequence, namely, multivariate mutual information (MMI), and the quantitative feature encoding methods, namely, autocorrelation. We develop the new design for MMI and autocorrelation feature encoding methods which give better results than the previous research. There are four ways to build the MMI method and six ways to build the autocorrelation method that we tested. We also built four types of MMI-autocorrelation (mixed) method and look for the best form of each type of MMI, autocorrelation, and mixed-method. We combine these feature encoding methods with support vector machine (SVM) as machine learning methods. We also test the encoding methods we propose to several machine learning classifier methods, such as random forest (RF), k-nearest neighbor (KNN), and gradient boosting.</span>

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“…Furthermore, MI-based measures are slightly affected by the monotone transformation and classifier selection [ 6 ]. These advantages allow MI-based measures for broad application in the analysis of various types of problems, including computer-aided diagnosis [ 7 ], cyber intrusion detection [ 8 ], heart failure recognition [ 9 ], and software cost estimation [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Nonparametric Feature Selection Approach Based on Mutual Information Transfer Network

Fard

2022

Entropy

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The filter feature selection algorithm is habitually used as an effective way to reduce the computational cost of data analysis by selecting and implementing only a subset of original features into the study. Mutual information (MI) is a popular measurement adopted to quantify the dependence among features. MI-based greedy forward methods (MIGFMs) have been widely applied to escape from computational complexity and exhaustion of high-dimensional data. However, most MIGFMs are parametric methods that necessitate proper preset parameters and stopping criteria. Improper parameters may lead to ignorance of better results. This paper proposes a novel nonparametric feature selection method based on mutual information and mixed-integer linear programming (MILP). By forming a mutual information network, we transform the feature selection problem into a maximum flow problem, which can be solved with the Gurobi solver in a reasonable time. The proposed method attempts to prevent negligence on obtaining a superior feature subset while keeping the computational cost in an affordable range. Analytical comparison of the proposed method with six feature selection methods reveals significantly better results compared to MIGFMs, considering classification accuracy.

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Multivariate Mutual Information-based Feature Selection for Cyber Intrusion Detection

Cited by 32 publications

References 20 publications

MIDESP: Mutual Information-Based Detection of Epistatic SNP Pairs for Qualitative and Quantitative Phenotypes

MIDESP: Mutual Information-Based Detection of Epistatic SNP Pairs for Qualitative and Quantitative Phenotypes

Performance of multivariate mutual information and autocorrelation encoding methods for the prediction of protein-protein interactions

A Novel Nonparametric Feature Selection Approach Based on Mutual Information Transfer Network

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