1st International Workshop on Machine Learning for Trust, Security and Privacy in Computing and Communications (MLTrustCom)

December 29th, 2020
Kupperingercole IGA Solutions

 December 29th, 2020 - January 1st, 2021 

1st International Workshop on Machine Learning for Trust, Security and Privacy in Computing and Communications (MLTrustCom)

Event submitted on Wednesday, August 12th 2020, approved by Charles Villanueva

1st International Workshop on Machine Learning for Trust, Security and Privacy in Computing and Communications (MLTrustCom), December 29th, 2020Click to Tweet

93 Days Until The Event
December 29th, 2020 - January 1st, 2021
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This event is part of a series managed by IEEE

 Our Review

If Machine Learning is your industry then this event would be incredible to attend. Organized by the highly-reputable IEEE, this ML Conference, taking place in Guangzhou, China looks like a leading global event on the subject matter. We also have other recommended Machine Learning conferences.

如果对机器学习感兴趣,那么这将是一个非常棒的会议。

- Review written by Henry Dalziel on Wednesday, August 12th 2020.
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Event Summary

The following description was either submitted by the Conference Organizer on Wednesday, August 12th 2020, or created by us.

In recent years, supervised machine learning methods (e.g. k nearest neighbors, Bayes’ theorem, decision tree, support vector machine, random forest, neural network, convolutional neural network, recurrent neural network, long short-term memory network, gated recurrent unit network), unsupervised machine learning methods (e.g. association rules, k-means, density-based spatial clustering of applications with noise, hierarchical clustering, deep belief networks, deep Boltzmann machine, auto-encoder, de-noising auto-encoder, etc.), reinforcement learning methods (e.g. generative adversarial network, deep Q network, trust region policy optimization, etc.) and federated learning methods have been applied to trust, security and privacy in computing and communications.

For instance, machine learning methods have been used to analyze the behaviors of the data stream in networks and extract the patterns of malicious activities (packet dropping, worm propagation, jammer attacks, etc.) for generating rules in intrusion detection systems.

Furthermore, time-series methods (e.g. local outlier factor, cumulative sum, adaptive online thresholding, etc.) have been proposed to retrieve the time-series features of anomalous behaviors for preventing cyber-attacks and malfunctions.

While the area of machine learning methods for trust, security, and privacy in computing and communications is a rapidly expanding field of scientific research, several open research questions are still needed to be discussed and studied. For instance, using and improving machine learning methods for malicious activity detection, attack detection, mobile endpoint analyses, repetitive security task automation, zero-day vulnerability prevention, and other security applications are the important issues in computing and communications.

This workshop named “Machine Learning for Trust, Security and Privacy in Computing and Communications” in conjunction with the 19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom 2020) will solicit papers on the following topics across various disciplines of trust, security and privacy in computing and communications.

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