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- W4386870327 abstract "Intrusion detection systems (IDSs) for computer networks play a crucial role in an organization’s performance. IDSs have been created and put into use over the years, utilizing a variety of methodologies to make sure that business networks are safe, dependable, and accessible. In this study, we concentrate on IDSs created by machine learning methods. IDSs based on machine learning (ML) techniques are proficient and reliable at spotting network assaults. However, as the data spaces increase, the effectiveness of these systems declines. Implementing a suitable removing features strategy that can eliminate some characteristics that have little bearing on categorization is essential. To examine the best characteristics in the data, this research suggested an efficient hybrid model that improves computation time and malware detection. This method addresses the problem of high negative result performance and low negative predictive value. Pre-processed data must first be correlated using the Gain Ratio and Co-Relation. Combining these approaches enables learning based on such an essential set of attributes and demonstrates improvement in accuracy and amount of temporal complexity." @default.
- W4386870327 created "2023-09-20" @default.
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- W4386870327 date "2023-01-01" @default.
- W4386870327 modified "2023-09-30" @default.
- W4386870327 title "Hybrid Feature Extraction for Analysis of Network System Security—IDS" @default.
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- W4386870327 doi "https://doi.org/10.1007/978-981-99-5080-5_3" @default.
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