GAN-based Intrusion Detection Data Enhancement

Fu, Wei; Qian, Liping; Xiao-hui, Zhu

doi:10.1109/ccdc52312.2021.9602568

Cited by 4 publications

(1 citation statement)

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“…In recent years, more and more research has applied GAN for data enhancement to improve the performance and robustness of machine learning models. In 2021, the authors of [23] proposed using the ACGAN model to solve the problem of the imbalanced distribution of 1D intrusion detection sample data, which improved the average detection accuracy of some classification models. In 2021, the authors of [24] proposed an improved DCGAN model with higher stability and sample balance to achieve higher classification accuracy for a few samples.…”

Section: Gan-based Data Enhancement Methodsmentioning

confidence: 99%

An Enhancement Method in Few-Shot Scenarios for Intrusion Detection in Smart Home Environments

Chen,

Wang,

Yang

et al. 2023

Electronics

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Different devices in the smart home environment are subject to different levels of attack. Devices with lower attack frequencies confront difficulties in collecting attack data, which restricts the ability to train intrusion detection models. Therefore, this paper presents a novel method called EM-FEDE (enhancement method based on feature enhancement and data enhancement) to generate adequate training data for expanding few-shot datasets. Training intrusion detection models with an expanded dataset can enhance detection performance. Firstly, the EM-FEDE method adaptively extends the features by analyzing the historical intrusion detection records of smart homes, achieving format alignment of device data. Secondly, the EM-FEDE method performs data cleaning operations to reduce noise and redundancy and uses a random sampling mechanism to ensure the diversity of the few-shot data obtained by sampling. Finally, the processed sampling data is used as the input to the CWGAN, and the loss between the generated and real data is calculated using the Wasserstein distance. Based on this loss, the CWGAN is adjusted. Finally, the generator outputs effectively generated data. According to the experimental findings, the accuracy of J48, Random Forest, Bagging, PART, KStar, KNN, MLP, and CNN has been enhanced by 21.9%, 6.2%, 19.4%, 9.2%, 6.3%, 7%, 3.4%, and 5.9%, respectively, when compared to the original dataset, along with the optimal generation sample ratio of each algorithm. The experimental findings demonstrate the effectiveness of the EM-FEDE approach in completing sparse data.

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Section: Gan-based Data Enhancement Methodsmentioning

confidence: 99%