Improving the Accuracy of Network Intrusion Detection System in Medical IoT Systems through Butterfly Optimization Algorithm

Li, Ya; Ghoreishi, Seyed-mohsen; Issakhov, Alibek

doi:10.1007/s11277-021-08756-x

Cited by 25 publications

(10 citation statements)

References 43 publications

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“…In a medical Internet of Things (IoT) system, an artificial neural network was employed to detect abnormal behavior, and the task of selecting the important and discriminative features of network traffic represented a crucial and challenging issue. Subsequently, an improvement in the accuracy of the network intrusion detection system in medical IoT systems through the BOA [188] was proposed. As a significant subset of the family of discrete optimization problems, the 0-1 knapsack problem has received considerable attention from researchers.…”

Section: G Research Progress Regarding Theory and Applications Of But...mentioning

confidence: 99%

A survey on several new popular swarm intelligence optimization algorithms

Wei

Huang

2023

Preprint

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Traditional optimization methods rely on parameter selection and a require high mathematical performance of the objective function. Thus, swarm intelligence optimization algorithms have attracted extensive attention as modern optimization methods in recent years, achieving remarkable results in the fields of machine learning, engineering optimization, process control, and elsewhere. Swarm intelligence optimization algorithms are a form of computing technology built upon the laws of biological group behavior, they are simple, fast, and place low requirements upon the objective functions. The traditional swarm intelligence algorithm offers new ideas for solving certain practical problems, however, it suffers from shortcomings in several experiments. In recent years, numerous scholars have proposed new swarm intelligence optimization algorithms, this paper selects several of the more typical swarm intelligence algorithms proposed in recent years at home and abroad, including the Whale Optimization Algorithm, Moth-Flame Optimization Algorithm, Fireworks Algorithm, Dragonfly Algorithm, Crow Search Algorithm, Butterfly Optimization Algorithm, and Pigeons Algorithm. Furthermore, the experimental performances of these algorithms are compared with respect to their convergence speed, accuracy, and stability, using 18 standard test functions, and the relative improvement methods are compared and analyzed. Finally, the characteristics of the swarm intelligence optimization algorithm are summarized, and its future development potential is discussed.

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Section: G Research Progress Regarding Theory and Applications Of But...mentioning

confidence: 99%

A survey on several new popular swarm intelligence optimization algorithms

Wei

Huang

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Ref. [11], the authors optimized fragments of the assembly of DNA using a metaheuristic consensus approach over an overlap layout. The outcomes of the methodology show the average performance as compared with other methodologies over 25 datasets.…”

Section: Related Workmentioning

confidence: 99%

“…To achieve the best fit, the regression model objects to predict data labels (q) in such a way that the difference between the true value and predicted value is minimum. The cost function is used for updating the best fit values of ∝ 1 and ∝ 2 to minimize the error rate using Equations ( 15)-( 17) [11,12].…”

Section: Traditional Regression Analysismentioning

confidence: 99%

“…Here r and ŷ are same. Coefficient of determination is used when there is no linear relationship between the variables, the residual quantities are as in Equation ( 22) [11].…”

Section: Proposed Hyperparameter Optimization Algorithmmentioning

confidence: 99%

“…The fitted linear model has a small magnitude of the residuals, the variability of Y using X shows how the change in the values of Y affects the prediction of the values. The variance is computed as in Equations ( 23)-( 30) [11].…”

Section: Proposed Hyperparameter Optimization Algorithmmentioning

confidence: 99%

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Optimization of Intrusion Detection Systems Determined by Ameliorated HNADAM-SGD Algorithm

et al. 2022

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Information security is of pivotal concern for consistently streaming information over the widespread internetwork. The bottleneck flow of incoming and outgoing data traffic introduces the issues of malicious activities taken place by intruders, hackers and attackers in the form of authenticity obstruction, gridlocking data traffic, vandalizing data and crashing the established network. The issue of emerging suspicious activities is managed by the domain of Intrusion Detection Systems (IDS). The IDS consistently monitors the network for the identification of suspicious activities, and generates alarm and indication in the presence of malicious threats and worms. The performance of IDS is improved by using different machine learning algorithms. In this paper, the Nesterov-Accelerated Adaptive Moment Estimation–Stochastic Gradient Descent (HNADAM-SDG) algorithm is proposed to determine the performance of Intrusion Detection Systems IDS. The algorithm is used to optimize IDS systems by hybridization and tuning of hyperparameters. The performance of algorithm is compared with other classification algorithms such as logistic regression, ridge classifier and ensemble algorithms where the experimental analysis and computations show the improved accuracy with 99.8%, sensitivity with 99.7%, and specificity with 99.5%.

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