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- W2930542996 abstract "Ubiquitous cyber-intrusions endanger the security of our devices constantly. They may bring irreversible damages to the system and cause leakage of privacy. Thus, it is of vital importance to promptly detect these intrusions. Traditional methods such as Decision Trees and Support Vector Machine (SVM) are used to classify normal internet connections and cyber-intrusions. However, the intrusions are largely fewer than normal connections, which limits the capability of these methods. Anomaly detection methods such as Isolation Forest can handle the imbalanced data. Nevertheless, when the features of data increase, these methods lack enough ability to learn the distribution. Generative adversarial network (GAN) has been proposed to solve the above issues. With its strong generative ability, it only needs to learn the distribution of normal status, and identify the abnormal status when intrusion occurs. But existing models are not suitable to process discrete values, leading to immense degradation of detection performance. To cope with these challenges, in this paper, we propose a novel GAN-based model with specifically-designed loss function to detect cyber-intrusions. Experiment results show that our model outperforms state-of-the-art models and remarkably reduce the overhead." @default.
- W2930542996 created "2019-04-11" @default.
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- W2930542996 date "2019-04-04" @default.
- W2930542996 modified "2023-10-18" @default.
- W2930542996 title "GAN-based method for cyber-intrusion detection." @default.
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- W2930542996 hasPublicationYear "2019" @default.
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