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• W3201215900 abstract "The digitalization of almost every sector raises the security and privacy concerns due to the vulnerabilities faced by the system and software. A huge number of new attacks are performed each day. The existing mechanisms to deal with these threats are intrusion detection system, firewall, antiviruses, etc. Nowadays, most works are based on machine learning techniques which needs huge amount of data. There are several benchmark datasets available, but most datasets are highly imbalanced in nature. Machine learning attack defense techniques which uses this type of dataset ends with ignoring the class having less instances. Also, the generation of data by performing attacks needs a deep knowledge and is time consuming. In order to get a well representative data for any attack, we have proposed a conditional Wasserstein generative adversarial network. The performance evaluation shows a promising result for the generated dataset which opens up further scope for future works." @default.
• W3201215900 created "2021-09-27" @default.
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• W3201215900 date "2021-01-01" @default.
• W3201215900 modified "2023-10-16" @default.
• W3201215900 title "GAN-Based Data Generation Approach for IDS: Evaluation on Decision Tree" @default.
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• W3201215900 doi "https://doi.org/10.1007/978-981-16-4294-4_4" @default.
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