Anomaly Intrusion Detection Using SVM and C4.5 Classification With an Improved Particle Swarm Optimization (I-PSO)

Sandeep, V.; Kondappan, Saravanan; Jone, A. Amir Anton; Barath, S. Raj

doi:10.4018/ijisp.2021040106

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…IQPSO-SVM was chosen over the standard SVM because the latter's classification accuracy and generalization ability are affected by the choice of the penalty parameter (C) and kernel function parameter (g), and it takes considerable time to adjust these parameters [52]. As an optimization algorithm, the PSO algorithm has good global optimization ability, and embedding it into SVM can optimize the parameters of the support vector machine to the maximum extent [53,54]. However, the reliability of the PSO algorithm results is closely related to the restriction degree of the connection between particles.…”

Section: Iqpso-svmmentioning

confidence: 99%

Risk Assessment of Deep Coal and Gas Outbursts Based on IQPSO-SVM

Zhu

Yang

Wang

et al. 2022

IJERPH

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Coal and gas outbursts seriously threaten the mining safety of deep coal mines. The evaluation of the risk grade of these events can effectively prevent the occurrence of safety accidents in deep coal mines. Characterized as a high-dimensional, nonlinear, and small-sample problem, a risk evaluation method for deep coal and gas outbursts based on an improved quantum particle swarm optimization support vector machine (IQPSO-SVM) was constructed by leveraging the unique advantages of a support vector machine (SVM) in solving small-sample, high-dimension, and nonlinear problems. Improved quantum particle swarm optimization (IQPSO) is used to optimize the penalty and kernel function parameters of SVM, which can solve the optimal local risk and premature convergence problems of particle swarm optimization (PSO) and quantum particle swarm optimization (QPSO) in the training process. The proposed algorithm can also balance the relationship between the global search and local search in the algorithm design to improve the parallelism, stability, robustness, global optimum, and model generalization ability of data fitting. The experimental results prove that, compared with the test results of the standard SVM, particle swarm optimization support vector machine (PSO-SVM), and quantum particle swarm optimization support vector machine (QPSO-SVM) models, IQPSO-SVM significantly improves the risk assessment accuracy of coal and gas outbursts in deep coal mines. Therefore, this study provides a new idea for the prevention of deep coal and gas outburst accidents based on risk prediction and also provides an essential reference for the scientific evaluation of other high-dimensional and nonlinear problems in other fields. This study can also provide a theoretical basis for preventing coal and gas outburst accidents in deep coal mines and help coal mining enterprises improve their safety management ability.

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Section: Iqpso-svmmentioning

confidence: 99%

Risk Assessment of Deep Coal and Gas Outbursts Based on IQPSO-SVM

Zhu

Yang

Wang

et al. 2022

IJERPH

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“…The goal is to improve the detection capability of operating malware detection systems. Although we can keep the accuracy of the system above 94%, which means that only 6 of the 100 detections displayed to the network administrator are error messages, we can increase the number of detections by about 7% [7]. Dzelila Mehanovic constructed a random forest classifier based on features extracted from the token smart contract code of the DeFi project.…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection Classification Based on Random Forest

2022

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In recent years, network security has become increasingly severe and network intrusions are frequent. On the one hand, it is difficult to capture intrusions because of the increasingly large volume of computer networks and complex network topology, and on the other hand, it is more difficult to capture intrusions because the characteristics of intrusions are becoming more diverse and complex. The purpose of this paper is to study intrusion detection classification based on random forest. After constructing a high-precision decision tree, the double failure metric is used as the distance between two decision trees, and the k-means++ algorithm is used to select high-precision decision trees with a certain degree of independence to form the final random forest. The improved random forest algorithm for setting up clustering and the random forest algorithm were experimented as algorithm comparisons. The KDD-NSL dataset was selected and the experimental results were compared to demonstrate the effectiveness of the random forest algorithm based on gradient boosting. The experimental results show that the improved random forest model has a significant reduction in decision tree size and a significant increase in diversity due to the k-means++ algorithm for clustering and extracting cluster centers.

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Urban Landscape Design Optimization System Based on Improved Particle Swarm Optimization Algorithm

Zheng,

Ma,

et al. 2024

Lecture Notes on Data Engineering and Communications Technologies

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Anomaly Intrusion Detection Using SVM and C4.5 Classification With an Improved Particle Swarm Optimization (I-PSO)

Cited by 4 publications

References 24 publications

Risk Assessment of Deep Coal and Gas Outbursts Based on IQPSO-SVM

Risk Assessment of Deep Coal and Gas Outbursts Based on IQPSO-SVM

Intrusion Detection Classification Based on Random Forest

Urban Landscape Design Optimization System Based on Improved Particle Swarm Optimization Algorithm

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