Incorporating evolutionary computation for securing wireless network against cyberthreats

Dwivedi, Shubhra; Vardhan, Manu; Tripathi, Sarsij

doi:10.1007/s11227-020-03161-w

Cited by 21 publications

(12 citation statements)

References 60 publications

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“…To beat this impediment, authors in [6,7] proposed another hybrid feature selection technique that diminishes the non-applicable features and selects the best ideal component subsets. The recurring pattern study in [8] indicated that individual hunt calculation locates the most suitable subsets that amplify information over-fitting, while a probing interest is less prone to information over-fitting in the part assurance, developing a modest number of tests [9]. Authors in [10] proposed the use of ELM and alpha profiling to diminish the required time while superfluous highlights were disposed of utilizing a group of separated, relationship and consistency-based feature selection procedures.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary Algorithm-based Feature Selection for an Intrusion Detection System

Singh

Shrivastava

2021

Eng. Technol. Appl. Sci. Res.

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Keeping computer reliability to confirm reliable, secure, and truthful correspondence of data between different enterprises is a major security issue. Ensuring information correspondence over the web or computer grids is always under threat of hackers or intruders. Many techniques have been utilized in intrusion detections, but all have flaws. In this paper, a new hybrid technique is proposed, which combines the Ensemble of Feature Selection (EFS) algorithm and Teaching Learning-Based Optimization (TLBO) techniques. In the proposed, EFS-TLBO method, the EFS strategy is applied to rank the features for choosing the ideal best subset of applicable information, and the TLBO is utilized to identify the most important features from the produced datasets. The TLBO algorithm uses the Extreme Learning Machine (ELM) to choose the most effective attributes and to enhance classification accuracy. The performance of the recommended technique is evaluated in a benchmark dataset. The experimental outcomes depict that the proposed model has high predictive accuracy, detection rate, false-positive rate, and requires less significant attributes than other techniques known from the literature.

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Section: Related Workmentioning

confidence: 99%

Evolutionary Algorithm-based Feature Selection for an Intrusion Detection System

Singh

Shrivastava

2021

Eng. Technol. Appl. Sci. Res.

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“…Genetic algorithms used in solving multicriteria optimization problems are variants of evolutionary search methods [7]. A large number of works have been devoted to researches in this area over the past few years.…”

Section: Review and Analysis Of Previous Studiesmentioning

confidence: 99%

“…IPT set cost. Relation (7) shows how it is possible to reduce (or increase) the risk of information loss for each invested cost unit. Next, we consider how the resulting expressions can be applied in GA.…”

Section: G Chmentioning

confidence: 99%

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Decision Support System on Optimization of Information Protection Tools Placement

A¹

2020

IJATCSE

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The article discusses the possibilities of modifying the genetic algorithm (GA) to solve the problem of selecting and optimizing the configuration of information protection tools (IPT) for the security circuits of information and communication systems (ICS). The scientific novelty of the work lies in the fact that the GA proposes the use of the total amount of risks from information loss, as well as the integral indicator of IPT and cost indicators for each class of IPT, as criteria for optimizing the composition of IPT. The genetic algorithm in the problem of optimizing the choice of the composition of IPT for ICS is considered as a variation of the problem associated with multi-selection. In this regard, the optimization of the IPT placement along the ICS protection circuits is considered as a modification of the combinatorial knapsack problem. The GA used in the computing core of the decision support system (DSS) differs from the standard GA. As a part of the GA modification, the chromosomes are presented in the form of matrices, the elements of which are numbers that correspond to the IPT numbers in the ICS nodes. In the process of modifying the GA, a k-point crossing-over was applied. A fitness function is presented as the sum of efficiency factors. At the same time, in addition to the traditional absolute indicators of the IPT efficiency, the total value of risks from information loss, as well as cost indicators for each class of IPT are taken into account. The practical value of the research lies in the implementation of DSS based on the proposed modification of GA. Computational experiments on the selection of a rational software algorithm for implementing the model are performed. It is shown that the implementation of the GA in DSS allows to accelerate the search for optimal variants of cybersecurity (CS) tools placement for ICS by more than 25 times. This advantage allows not only to quickly search through the variety of hardware-software IPT and their combinations for ICS, but also to combine the proposed algorithm with the available models and algorithms for optimizing the composition of the ICS cybersecurity circuits. Potentially, such a combination of models and algorithms will make it possible to quickly rebuild the ICS protection, adjusting its profiles in accordance with new threats and classes of cyber attacks.

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“…In general, wrapper methods can obtain better results than filter methods, but bring more expensive computational cost. A lot of evolutionary algorithms such as genetic algorithm (GA), differential evolution (DE), ant colony optimization (ACO), and simulated annealing are commonly used as wrapper methods for gene selection [57,58].…”

Section: Introductionmentioning

confidence: 99%

Gene Correlation Guided Gene Selection for Microarray Data Classification

Yang

Zhu

2021

BioMed Research International

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The microarray cancer data obtained by DNA microarray technology play an important role for cancer prevention, diagnosis, and treatment. However, predicting the different types of tumors is a challenging task since the sample size in microarray data is often small but the dimensionality is very high. Gene selection, which is an effective means, is aimed at mitigating the curse of dimensionality problem and can boost the classification accuracy of microarray data. However, many of previous gene selection methods focus on model design, but neglect the correlation between different genes. In this paper, we introduce a novel unsupervised gene selection method by taking the gene correlation into consideration, named gene correlation guided gene selection (G3CS). Specifically, we calculate the covariance of different gene dimension pairs and embed it into our unsupervised gene selection model to regularize the gene selection coefficient matrix. In such a manner, redundant genes can be effectively excluded. In addition, we utilize a matrix factorization term to exploit the cluster structure of original microarray data to assist the learning process. We design an iterative updating algorithm with convergence guarantee to solve the resultant optimization problem. Experimental results on six publicly available microarray datasets are conducted to validate the efficacy of our proposed method.

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Incorporating evolutionary computation for securing wireless network against cyberthreats

Cited by 21 publications

References 60 publications

Evolutionary Algorithm-based Feature Selection for an Intrusion Detection System

Evolutionary Algorithm-based Feature Selection for an Intrusion Detection System

Decision Support System on Optimization of Information Protection Tools Placement

Gene Correlation Guided Gene Selection for Microarray Data Classification

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