A multilayer perceptron artificial neural network approach for improving the accuracy of intrusion detection systems

Mohammed, Abdulrahman Jassam; Arif, Muhanad Hameed; Ali, Ali Adil

doi:10.11591/ijai.v9.i4.pp609-615

Cited by 8 publications

(5 citation statements)

References 26 publications

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“…The regularization and construction of XGBoost are highly influenced by hyperparameters such as learning rate, ensemble size, and maximum depth of base learners. The goal of hyperparameter optimization in HO-XGB is to minimize the objective function in (1). Optimization of hyperparameters is an important task in automated machine learning since it improves model performance.…”

Section: Backgrounds and Related Workmentioning

confidence: 99%

“…A vast amount of information has been transmitted across intricate network connections worldwide. Consequently, the establishment of a secure information system has garnered significant attention from both private and governmental institutions, aiming to thwart potential attackers [1]. Network attacks have emerged as one of contemporary society [2].…”

Section: Introductionmentioning

confidence: 99%

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Hyperparameters optimization XGBoost for network intrusion detection using CSE-CIC-IDS 2018 dataset

Chimphlee,

Chimphlee

2024

IJ-AI

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<p>With the introduction of high-speed internet access, the demand for security and dependable networks has grown. In recent years, network attacks have gotten more complex and intense, making security a vital component of organizational information systems. Network intrusion detection systems (NIDS) have become an essential detection technology to protect data integrity and system availability against such attacks. NIDS is one of the most well-known areas of machine learning software in the security field, with machine learni ng algorithms constantly being developed to improve performance. This research focuses on detecting abnormalities in societal infiltration using the hyperparameters optimization XGBoost (HO-XGB) algorithm with the Communications Security Establishment-The Canadian Institute for Cybersecurity-Intrusion Detection System2018 (CSE-CIC-IDS2018) dataset to get the best potential results. When compared to typical machine learning methods published in the literature, HO-XGB outperforms them. The study shows that XGBoost outperforms other detection algorithms. We refined the HO-XGB model's hyperparameters, which included learning_rate, subsample, max_leaves, max_depth, gamma, colsample_bytree, min_child_weight, n_estimators, max_depth, and reg_alpha. The experimental findings reveal that HO-XGB1 outperforms multiple parameter settings for intrusion detection, effectively optimizing XGBoost's hyperparameters.</p>

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Section: Backgrounds and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hyperparameters optimization XGBoost for network intrusion detection using CSE-CIC-IDS 2018 dataset

Chimphlee,

Chimphlee

2024

IJ-AI

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“…The nodes in the layers with MLP are fully connected to the nodes in the previous layer. MLP allows updates to be made by propagating the error across the network using backpropagation [31].…”

Section: Baseline Modelsmentioning

confidence: 99%

Hybrid ConvLSTM Model for Evaluating the Performance of SMEs in The Software Sector

Utku

2024

NATURENGS MTU Journal of Engineering and Natural Sciences Malatya Turgut Ozal University

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SME is a term used for businesses based on the number of employees, size, and turnover. SMEs form the basis of the economy and are indispensable organizations of business life worldwide. In this study, the ConvLSTM model was created to evaluate the financial performance of SMEs operating in the software sector in Turkey. The motivation of the study is to analyze the performance of SMEs operating in the software sector in Turkey. The study used data from the Small and Medium Enterprises Development of Türkiye for 2018-2022. ConvLSTM was compared with LR, LSTM, SVM, CNN, RF, and MLP. Experiments showed that ConvLSTM outperformed other models, performing above 0.8 R2 for all parameters.

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“…There are several dozen different neural network architectures, and the effectiveness of many of them has been proven mathematically [25]. [26].…”

Section: The Choice Of the Learning Algorithm For Artificial Neural N...mentioning

confidence: 99%

Learning algorithm of artificial neural network factor forecasting power consumption of users

et al. 2022

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Seasonal fluctuations in electricity consumption, an uneven load of supply lines reduce not only the indicator of energy efficiency of networks but also contribute to a decrease in the service life of elements of power supply systems. Revealing the patterns of such fluctuations makes it possible to build models of power consumption, predict its dynamics, which in general will contribute to ensuring the energy efficiency of urban electrical networks and increasing the reliability of power supply systems. A computational, computer and neural network model is proposed that allows to increase the accuracy of the forecast of electricity consumption by household consumers. Based on the developed mathematical model, taking into account the obtained factor coefficients - ti, h, c, s, k for 2020 for 9 cities of the Republic of Tajikistan, monthly coefficients characterizing the terrain conditions (αi) were calculated. The results obtained using the proposed method was compared with the results of a computer and neural network model.

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A multilayer perceptron artificial neural network approach for improving the accuracy of intrusion detection systems

Cited by 8 publications

References 26 publications

Hyperparameters optimization XGBoost for network intrusion detection using CSE-CIC-IDS 2018 dataset

Hyperparameters optimization XGBoost for network intrusion detection using CSE-CIC-IDS 2018 dataset

Hybrid ConvLSTM Model for Evaluating the Performance of SMEs in The Software Sector

Learning algorithm of artificial neural network factor forecasting power consumption of users

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