Dialogue Generation With GAN

Su, Hui; Shen, Xiaoyu; Hu, Pengwei; Li, Wenjie; Chen, Yun

doi:10.1609/aaai.v32i1.12158

Cited by 18 publications

(1 citation statement)

References 4 publications

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“…GAN allows the generator to effectively learn data features by engaging in a game-like interaction with the discriminator to simulate data distributions. GAN has demonstrated remarkable advancements in generating images, sounds, and texts [8][9][10]. As a result, researchers from various domains are increasingly incorporating this method into their research endeavors.…”

Section: Introductionmentioning

confidence: 99%

A New Data-Balancing Approach Based on Generative Adversarial Network for Network Intrusion Detection System

et al. 2023

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An intrusion detection system (IDS) plays a critical role in maintaining network security by continuously monitoring network traffic and host systems to detect any potential security breaches or suspicious activities. With the recent surge in cyberattacks, there is a growing need for automated and intelligent IDSs. Many of these systems are designed to learn the normal patterns of network traffic, enabling them to identify any deviations from the norm, which can be indicative of anomalous or malicious behavior. Machine learning methods have proven to be effective in detecting malicious payloads in network traffic. However, the increasing volume of data generated by IDSs poses significant security risks and emphasizes the need for stronger network security measures. The performance of traditional machine learning methods heavily relies on the dataset and its balanced distribution. Unfortunately, many IDS datasets suffer from imbalanced class distributions, which hampers the effectiveness of machine learning techniques and leads to missed detection and false alarms in conventional IDSs. To address this challenge, this paper proposes a novel model-based generative adversarial network (GAN) called TDCGAN, which aims to improve the detection rate of the minority class in imbalanced datasets while maintaining efficiency. The TDCGAN model comprises a generator and three discriminators, with an election layer incorporated at the end of the architecture. This allows for the selection of the optimal outcome from the discriminators’ outputs. The UGR’16 dataset is employed for evaluation and benchmarking purposes. Various machine learning algorithms are used for comparison to demonstrate the efficacy of the proposed TDCGAN model. Experimental results reveal that TDCGAN offers an effective solution for addressing imbalanced intrusion detection and outperforms other traditionally used oversampling techniques. By leveraging the power of GANs and incorporating an election layer, TDCGAN demonstrates superior performance in detecting security threats in imbalanced IDS datasets.

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Section: Introductionmentioning

confidence: 99%