Combination of Feature Selection and Hybrid Classifier as to Network Intrusion Detection System Adopting FA, GWO, and BAT Optimizers

Alizadeh, Mousa; Mousavi, Sadegh E; Beheshti, Mohammad Taghi Hamidi; Ostadi, Ali

doi:10.1109/icspis54653.2021.9729365

Cited by 20 publications

(8 citation statements)

References 11 publications

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“…Even though the search process can be easily parallelized, the grid search suffers from the curse of dimensionality. The random search, on the contrary, can sample the critical dimension of search space, performing more efficiently than grid search 41,42 . Employing nature‐inspired meta‐heuristic algorithms is an alternative way to optimize hyper‐parameters of neural networks with promising results 43,44 .…”

Section: Related Workmentioning

confidence: 99%

“…The random search, on the contrary, can sample the critical dimension of search space, performing more efficiently than grid search. 41,42 Employing nature-inspired meta-heuristic algorithms is an alternative way to optimize hyper-parameters of neural networks with promising results. 43,44 As presented in Reference 45, an LSTM-CNN network forecasted energy consumption, where the PSO algorithm finds the optimal number of CNN kernels, the number of LSTM units, and the number of units in the fully connected layer.…”

Section: Hyper-parameter Optimizationmentioning

confidence: 99%

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An optimized hybrid methodology for short‐term traffic forecasting in telecommunication networks

Alizadeh,

Beheshti,

Ramezani

et al. 2023

Trans Emerging Tel Tech

Self Cite

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With the rapid development of telecommunication networks, the predictability of network traffic is of significant interest in network analysis and optimization, bandwidth allocation, and load balancing adjustment. Consequently, in recent years, significant research attention has been paid to forecasting telecommunication network traffic. Telecommunication traffic forecasting problems can be considered a time‐series problem, wherein periodic historical data is fed as the input to a model. Time‐series forecasting approaches are broadly categorized as statistical machine learning (ML) methods and their combinations. Statistical approaches forecast linear characteristics of time‐series data, unable to capture nonlinear and complex patterns. ML‐based approaches can model nonlinear characteristics of data. In recent years, hybrid approaches combining statistical and ML‐based approaches have been widely used to model linear and nonlinear data characteristics. However, the performance of these approaches highly depends on feature selection techniques and hyper‐parameter tuning of ML methods. A novel hybrid method is proposed for short‐term traffic forecasting based on feature selection and hyperparameter optimization to address this problem. It combines statistical and ML methods to model linear and nonlinear components of data. First, a novel feature selection technique, modified mutual information based on a linear combination of targets, is proposed to find the candidate input variables. Next, a combination of vector auto regressive moving average (VARMA), long short‐term memory (LSTM), and multilayer perceptron (MLP), called VARMA‐LSTM‐MLP forecaster, is suggested to forecast short‐term traffic. A hybrid metaheuristic algorithm, composed of firefly and BAT, is employed to find the optimal set of hyper‐parameter values. The proposed method is assessed by a real‐world dataset containing Tehran city's daily telecommunication data in IRAN. The evaluation results demonstrate that the proposed method outperforms the existing methods in terms of mean squared error and mean absolute error.

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Section: Related Workmentioning

confidence: 99%

Section: Hyper-parameter Optimizationmentioning

confidence: 99%

An optimized hybrid methodology for short‐term traffic forecasting in telecommunication networks

Alizadeh,

Beheshti,

Ramezani

et al. 2023

Trans Emerging Tel Tech

Self Cite

View full text Add to dashboard Cite

show abstract

“…The transfer of knowledge from a model that has already been trained on one task to another related task speeds up model training and improves performance [ 50 ]. In the structure of the CNN constructed above, TL is integrated into the format described in Table 3 .…”

Section: Research Modelmentioning

confidence: 99%

Application interface design of Chongqing intangible cultural heritage based on deep learning

Liu,

Cheng,

2023

Heliyon

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“…in this definition [8]. Thus, optimization has usage in many engineering disciplines such as geotechnics [9], transportation [10][11][12], wind turbine airfoil geometry and automotive magnetological brake designs [13], antenna design [14,15], and so forth. It is worthy to mentioned that while designing, engineers have to consider safety, economy, and aesthetics into account.…”

Section: Introductionmentioning

confidence: 99%

Design cost minimization of a reinforced concrete column section using overnew swarm-based optimization algorithms

Tunca,

Carbas

2024

Neural Comput & Applic

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It is very tiresome for a practiser to detect the best feasible sizing design of structural members including reinforced concrete columns that is a highly nonlinear and complicated structural engineering optimization problem. This is due to such a design is practically conducted via conventional trial-and-error computing methods in which resistance to external loads, cost efficiency, and aesthetic factors, etc. have to be considered. This study focuses on minimizing the design cost of primarily proposed reinforced concrete column design problem via three overnew swarm-based optimizers such as Coati Optimization Algorithm, Fox Optimizer and Pelican Optimization Algorithm (POA) that are firstly utilized for this purpose. In this regard, the type of steel rebar distribution, the characteristic strength of the concrete, the height and width of the column section, and the number and diameter of the rebars are treated as discrete design variables of the newly proposed complex reinforced concrete column design cost optimization problem. In solution, the design requirements specified in practice code provisions should also be met. Here, Turkish Building Earthquake Code 2018 specifications are considered as practice structural design constraints. Consequently, the algorithmic performances of three overnew swarm-based metaheuristic optimization algorithms are compared and evaluated in detail. Amongst them, the POA shows most fruitful algorithmic design solution performance.

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Combination of Feature Selection and Hybrid Classifier as to Network Intrusion Detection System Adopting FA, GWO, and BAT Optimizers

Cited by 20 publications

References 11 publications

An optimized hybrid methodology for short‐term traffic forecasting in telecommunication networks

An optimized hybrid methodology for short‐term traffic forecasting in telecommunication networks

Application interface design of Chongqing intangible cultural heritage based on deep learning

Design cost minimization of a reinforced concrete column section using overnew swarm-based optimization algorithms

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