An Abnormal Behavior Detection Based on Deep Learning

Zhang, Junwei; Ou, Jiaxiang; Ding, Chao; Shi, Wenbin

doi:10.1109/smartworld.2018.00045

Cited by 3 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among them, abnormal users are one of the standard methods criminals use to attack healthcare social networks. For example, merchants distort product value orientation for commercial interests [ 1 – 3 ]; criminals use multiple vests to deceive Internet users, steal information, or even online fraud [ 4 – 6 ]. According to statistics, the types of abnormal users also show a variety of forms due to different types of healthcare social networks.…”

Section: Introductionmentioning

confidence: 99%

Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning

Veeramakali

Shobanadevi

Nayak³

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

A key challenge in clinical recommendation systems is the problem of aberrant patient profiles in social networks. As a result of a person’s abnormal profile, numerous vests might be used to make fake remarks about them, cyber bullying, or cyber-attacks. Many clinical researchers have done extensive study on this topic. The most recent studies on this topic are summarized, and an overarching framework is provided. When it comes to the methods and datasets that make up the data collection, the feature presentation and algorithm selection layers provide an overview of the various types of algorithm selections available. The categorization and evaluation of diseases and disorders has been one of the major advantages of machine learning in medical. Because it was harder to predict, it rendered it more controllable. It might range from difficult-to-find cancers in the early stages to certain other illnesses spread through the bloodstream. In healthcare, we may pick methods in machine learning depending on reliable outcomes. To do so, we must run the findings through each method. The major issue arises during information training and validation. Because the dataset is so large, eliminating mistakes might be difficult. The providers, other characteristics, various algorithms, data labelling techniques, and assessment criteria are all presented and contrasted in depth. Detecting anomalous users in medical social networks, on the other hand, is a work in progress. The result evaluation layer provides an explanation of how to evaluate and mark up the results of the various algorithm selection layers. Finally, it looks forward to more study in this area.

show abstract

Section: Introductionmentioning

confidence: 99%

Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning

Veeramakali

Shobanadevi

Nayak³

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…This method uses genetic algorithms to find the optimal parameters of CNN network, which can extract power grid data of different groups at the same time, which greatly reduces the complexity of detection tasks. However, this method has not been tested in large-scale grid data, and the operational efficiency needs to be further improved [7]. Zhang et al [8] integrated autoencoders into Generative Adversarial Networks (GANs) to detect abnormal data by capturing measurements that are inconsistent with normal data.…”

Section: Related Workmentioning

confidence: 99%

Multi-node abnormal data identification in distribution networks based on deep learning

Li¹,

Yang²,

Xu³

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

With the continuous advancement of the construction of the power grid industry, the lean management of the distribution network is also being fully implemented. Due to unstable communications, system defects, equipment failures, and human factors, data center problems such as missing data and abnormal data collection in the process of power grid deployment and deployment are becoming more and more prominent. To improve the quality and availability of abnormal data in transmission networks, this paper proposes a deep learning-based method for identifying abnormal data of multiple nodes in distribution networks. The dual-channel deep CNN network is used to extract the time series power data features, and the abnormal data is corrected by redistributing the power loss of the transmission line by using the balance calculation relationship. Experiments show that the model proposed in this paper improves the detection rate of abnormal data identification and reduces the false alarm rate compared with traditional machine learning and statistical analysis detection methods. In addition, the abnormal data of the grid is checked and corrected to improve the availability of grid data.

show abstract

Abnormal electricity consumption detection based on ensemble learning

Zhou

Cheng

et al. 2019

2019 9th International Conference on Information Science and Technology (ICIST)

View full text Add to dashboard Cite

An Abnormal Behavior Detection Based on Deep Learning

Cited by 3 publications

References 15 publications

Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning

Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning

Multi-node abnormal data identification in distribution networks based on deep learning

Abnormal electricity consumption detection based on ensemble learning

Contact Info

Product

Resources

About