A data balancing approach based on generative adversarial network

Yuan, Lixiang; Yu, Shiwen; Yang, Zhibang; Duan, Mingxing; Li, Kenli

doi:10.1016/j.future.2022.12.024

Cited by 17 publications

(7 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The technique balances the distribution of classes in the dataset and produces high-quality attack data by using long-short-term memory (LSTM) on both the generator and discriminator. The results showed the effectiveness of the B-GAN approach and its potential to improve intrusion detection systems' performance [40].…”

Section: Related Workmentioning

confidence: 92%

A High-Performance Multimodal Deep Learning Model for Detecting Minority Class Sample Attacks

Yu,

Xu,

Jiang

2023

Symmetry

View full text Add to dashboard Cite

A large amount of sensitive information is generated in today’s evolving network environment. Some hackers utilize low-frequency attacks to steal sensitive information from users. This generates minority attack samples in real network traffic. As a result, the data distribution in real network traffic is asymmetric, with a large number of normal traffic and a rare number of attack traffic. To address the data imbalance problem, intrusion detection systems mainly rely on machine-learning-based methods to detect minority attacks. Although this approach can detect minority attacks, the performance is not satisfactory. To solve the above-mentioned problems, this paper proposes a novel high-performance multimodal deep learning method. The method is based on deep learning. It captures the features of minority class attacks based on generative adversarial networks, which in turn generate high-quality minority class sample attacks. Meanwhile, it uses the designed multimodal deep learning model to learn the features of minority attacks. The integrated classifier then utilizes the learned features for multi-class classification. This approach achieves 99.55% and 99.95% F-measure, 99.56% and 99.96% accuracy on the CICIDS2017 dataset and the NSL-KDD dataset, respectively, with the highest false positives at only 3.4%. This exceeds the performance of current state-of-the-art methods.

show abstract

Section: Related Workmentioning

confidence: 92%

A High-Performance Multimodal Deep Learning Model for Detecting Minority Class Sample Attacks

Yu,

Xu,

Jiang

2023

Symmetry

View full text Add to dashboard Cite

show abstract

“…However, the reliability of the model trained by simulated IMU data as compared to real IMU data is unclear because noise can be easily introduced by the skin when using moving artifacts during IMU data acquisition. Recently, generative models such as GANs are being widely used to generate time series fabricated data [128][129][130]. These studies provide inspiration for using GANs to address the problem of lack of data for improving the performance of musculoskeletal force estimation models.…”

Section: Dataset For Deep Learning Methodsmentioning

confidence: 99%

Extended Application of Inertial Measurement Units in Biomechanics: From Activity Recognition to Force Estimation

Liang

Wang

Fan

et al. 2023

Sensors

View full text Add to dashboard Cite

Abnormal posture or movement is generally the indicator of musculoskeletal injuries or diseases. Mechanical forces dominate the injury and recovery processes of musculoskeletal tissue. Using kinematic data collected from wearable sensors (notably IMUs) as input, activity recognition and musculoskeletal force (typically represented by ground reaction force, joint force/torque, and muscle activity/force) estimation approaches based on machine learning models have demonstrated their superior accuracy. The purpose of the present study is to summarize recent achievements in the application of IMUs in biomechanics, with an emphasis on activity recognition and mechanical force estimation. The methodology adopted in such applications, including data pre-processing, noise suppression, classification models, force/torque estimation models, and the corresponding application effects, are reviewed. The extent of the applications of IMUs in daily activity assessment, posture assessment, disease diagnosis, rehabilitation, and exoskeleton control strategy development are illustrated and discussed. More importantly, the technical feasibility and application opportunities of musculoskeletal force prediction using IMU-based wearable devices are indicated and highlighted. With the development and application of novel adaptive networks and deep learning models, the accurate estimation of musculoskeletal forces can become a research field worthy of further attention.

show abstract

“…Following up on this scholarship, scientist Nelson, in 2020, used LSTM to compose lo-fi music, a music quality that treated elements as imperfections in the context of a recording. Second, for CNN Model, Researcher Yang (2017) created MidiNet, which generates multi-instrument music sequences [4]. In the same year, Scientist Dong (2017) used MuseGAN, which utilizes multiple generators to achieve synthetic multi-instrument music that respects dependencies between instruments [5].…”

Section: Literature Reviewmentioning

confidence: 99%

Deep learning in automatic music generation

Zhang¹

2023

ACE

View full text Add to dashboard Cite

Music is a grouping of musical tones from various frequencies. While artists composed through a deliberate arrangement of different notes, nowadays, A.I. programs learn to automatically generate short music through a machinal sequence of distinct notes. This essay compared the utility and efficiency of traditional machine learning (Regression Model) and deep learning methods (LSTM). This research only focused on instrumental classical music and used the MusicNet collection as the primary dataset. The comprehensive experiments are conducted from these two models, which suggests two results. Firstly, the LSTM model generates melodies that better fit the training styles. Secondly, models are better fitted on single music data than on the entire dataset.

show abstract

A data balancing approach based on generative adversarial network

Cited by 17 publications

References 22 publications

A High-Performance Multimodal Deep Learning Model for Detecting Minority Class Sample Attacks

A High-Performance Multimodal Deep Learning Model for Detecting Minority Class Sample Attacks

Extended Application of Inertial Measurement Units in Biomechanics: From Activity Recognition to Force Estimation

Deep learning in automatic music generation

Contact Info

Product

Resources

About