A Feature Selection Approach Based on Simulated Annealing for Detecting Various Denial of Service Attacks

Jeong, In-Seon; Kim, Hongki; Kim, Tae-Hee; Lee, Dong Hwi; Kim, Kuinam J.; Kang, Seung‐Ho

doi:10.13052/jcs2445-9992.2016.001

Cited by 6 publications

(5 citation statements)

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“…In [16], four undersampling techniques based on neighbourhood searching (NB-based) by utilizing the k-NN rule to select and remove majority class examples from the potential region of overlapping. RF (Random Forest) and SVM (Support Vector Machine) were used in [16] for learning and their results compared with several pre-processing state-of-the-art techniques to rebalance datasets before applying the learning algorithm, like the SMOTE (Synthetic Minority Over-Sampling Technique) [40], kmUnder (k -means undersampling) [29], OBU [41], BLSMOTE [42] and ENN [43]. Columns "NB-SVM" and "NB-RF" in Tables 2 and 3 present the best G-mean and F-score values, respectively, selected for each classifier (SVM and RF) among the four (NB-based) methods, while posterior columns present the best value selected from the classifiers (SVM and RF) after applying the state-of-theart pre-processing techniques.Bold values in Tables 2 and 3 represents the best value in that row while Italic represnts the second and third best values in the same row.…”

Section: Resultsmentioning

confidence: 99%

An Improved Hybrid Approach for Handling Class Imbalance Problem

Desuky

Hussain

2021

Arab J Sci Eng

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Class imbalance issue that presents in many real-world datasets exhibit favouritism toward the majority class and showcases poor performance for the minority class. Such misclassifications may incur dubious outcome in case of disease diagnosis and other critical applications. Hence, it is a hot topic for the researchers to tackle the class imbalance issue. We present a novel hybrid approach for handling such datasets. We utilize simulated annealing algorithm for undersampling and apply support vector machine, decision tree, k-nearest neighbor and discriminant analysis for the classification task. We validate our technique in 51 real-world datasets and compare it with other recent works. Our technique yields better efficacy than the existing techniques and hence it can be applied in imbalance datasets to mitigate the misclassification.

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Section: Resultsmentioning

confidence: 99%

An Improved Hybrid Approach for Handling Class Imbalance Problem

Desuky

Hussain

2021

Arab J Sci Eng

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“…Boltzmann probability, P = e − Φ T is applied as an acceptance condition of the neighbour solution. Φ refers to the difference between the fitness of the optimal and neighbour solutions, and T is named temperature which is gradually reduced based on cooling schedule throughout the search procedure [20,23,25]. In this paper, as adopted in [25], the initial temperature equals 2 * |N| , where N is the number of features in each data set, and T = 0.93 * T , is applied to calculate the cooling schedule.…”

Section: Simulated Annealingmentioning

confidence: 99%

Hybrid Binary Dragonfly Algorithm with Simulated Annealing for Feature Selection

et al. 2021

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There are various fields are affected by the growth of data dimensionality. The major problems which are resulted from high dimensionality of data including high memory requirements, high computational cost, and low machine learning classifier performance. Therefore, proper selection of relevant features from the set of available features and the removal of irrelevant features will solve these problems. Therefore, to solve the feature selection problem, an improved version of Dragonfly Algorithm (DA) is proposed by combining it with Simulated Annealing (SA), where the improved algorithm named BDA-SA. To solve the local optima problem of DA and enhance its ability in selecting the best subset of features for classification problems, Simulated Annealing (SA) was applied to the best solution found by Binary Dragonfly algorithm in attempt to improve its accuracy. A set of frequently used data sets from UCI repository was utilized to evaluate the performance of the proposed FS approach. Results show that the proposed hybrid approach, named BDA-SA, has superior performance when compared to wrapper-based FS methods including a feature selection method based on the basic version of Binary Dragonfly Algorithm.

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“…Considering data reduction with respect to the set of features to be used for model training, Jeong et al (2016) propose a feature selection approach based on simulated annealing and apply it to a case study for detecting denial of service attacks [90]. This approach is similar to [91], which uses the same data set but a different local search algorithm.…”

Section: Framework For Data Reductionmentioning

confidence: 99%

A Survey of Feature Set Reduction Approaches for Predictive Analytics Models in the Connected Manufacturing Enterprise

2019

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The broad context of this literature review is the connected manufacturing enterprise, characterized by a data environment such that the size, structure and variety of information strain the capability of traditional software and database tools to effectively capture, store, manage and analyze it. This paper surveys and discusses representative examples of existing research into approaches for feature set reduction in the big data environment, focusing on three contexts: general industrial applications; specific industrial applications such as fault detection or fault prediction; and data reduction. The conclusion from this review is that there is room for research into frameworks or approaches to feature filtration and prioritization, specifically with respect to providing quantitative or qualitative information about the individual features in the dataset that can be used to rank features against each other. A byproduct of this gap is a tendency for analysts not to holistically generalize results beyond the specific problem of interest, and, related, for manufacturers to possess only limited knowledge of the relative value of smart manufacturing data collected.

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A Feature Selection Approach Based on Simulated Annealing for Detecting Various Denial of Service Attacks

Cited by 6 publications

References 0 publications

An Improved Hybrid Approach for Handling Class Imbalance Problem

An Improved Hybrid Approach for Handling Class Imbalance Problem

Hybrid Binary Dragonfly Algorithm with Simulated Annealing for Feature Selection

A Survey of Feature Set Reduction Approaches for Predictive Analytics Models in the Connected Manufacturing Enterprise

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