A data-driven network intrusion detection system using feature selection and deep learning

Zhang, Lianming; Liu, Kui; Xie, Xiaowei; Bai, Wenji; Wu, Baolin; Dong, Pingping

doi:10.1016/j.jisa.2023.103606

Cited by 9 publications

(2 citation statements)

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“…where, τ ij (t) represents the pheromone concentration between attribute nodes i and j; ρ represents the pheromone evaporation level, 0 ≤ ρ ≤ 1; ∆τ ij (t) represents the total pheromone left by ants on the path from attribute nodes i to j during each iteration, as shown in Eq. (8).…”

Section: Improved Aco For Attribute Reductionmentioning

confidence: 99%

“…Currently, the mainstream rule generation methods include association rule mining [7], neural networks [8], and rough sets [9]. Association rule mining algorithm is the most commonly used rule mining method, often used to discover associations in datasets, with the basic idea of analyzing the associations between item sets in the dataset to find frequent item sets and generate association rules.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Rule Generation in Product Design Through Rough Set Theory and Ant Colony Optimization

Wang,

Wang

2024

PMDF

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Limitations inherent in conventional rule generation methodologies, particularly concerning knowledge redundancy and efficiency in product design, are addressed through the adoption of a rough set-based approach in this study. An enhancement to the Ant Colony Optimization (ACO) algorithm's information gain ratio is introduced by integrating a redundancy detection mechanism, which notably accelerates the convergence process. Furthermore, the application of a classification consistency algorithm effectively minimizes the number of attributes, facilitating the extraction of potential associative rules. Comparative validation performed on a public dataset demonstrates that the proposed attribute reduction approach surpasses existing methods in terms of attribute count reduction, reduction rate, and execution time. When applied to an automotive design case study, the approach yields rules with 100% coverage and accuracy, characterized by a reduced average number of attributes per rule. These findings underscore the superiority of the rough set-based methodology in generating product design rules, providing a robust framework that enhances both the precision and applicability of the design process.

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Section: Improved Aco For Attribute Reductionmentioning

confidence: 99%