The 2nd International Workshop on Machine Learning for Trust, Security and Privacy in Computing and Communications (MLTrustCom 2021)
In conjunction with
The 20th IEEE International Conference on Trust, Security and Privacy in Computing and Communications
(IEEE TrustCom 2021)
18-20 August, 2021, Shenyang, China
Scope
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 20th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom 2021) will solicit papers on the following topics across various disciplines of trust, security and privacy in computing and communications.
Topics
New supervised machine learning methods for trust, security and privacy in computing and communications
New unsupervised machine learning methods for trust, security and privacy in computing and communications
New reinforcement learning methods for trust, security and privacy in computing and communications
New federated learning methods for trust, security and privacy in computing and communications
New optimization methods for trust, security and privacy in computing and communications
Important Dates
Paper Submission Deadline |
10 June 10 2021
|
| Author Notification |
25 June 25 2021
|
| Camera-Ready Paper Due |
10 July 10 2021 |
| Conference Dates |
18-20 August 2021 |
Organizing Committee
Steering Committee
Prof. Wenzhong Guo (Fuzhou University, China)
Prof. Chin-Chen Chang (IEEE Fellow; Feng-Chia University, Taiwan)
Prof. Chi-Hua Chen (Fuzhou University, China)
General Chairs
Prof. Genggeng Liu (Fuzhou University, China)
Prof. Chin-Ling Chen (Chaoyang University of Technology, Taiwan)
Session Chairs
Prof. Feng-Jang Hwang (University of Technology Sydney, Australia)
Prof. Yu-Chih Wei (National Taipei University of Technology, Taiwan)
Prof. Chia-Yu Lin (Yuan Ze University, Taiwan)
Technical Program Committee
Prof. Xiao-Guang Yue (European University Cyprus, Cyprus)
Prof. Hsu-Yang Kung (National Pingtung University of Science and Technology, Taiwan)
Prof. Chunjia Han (University of Greenwich, United Kingdom)
Prof. Chih-Min Yu (Yango University, China)
Dr. Ling Wu (Fuzhou University, China)
Dr. Xiaoyan Li (Fuzhou University, China)
Prof. Szu-Yin Lin (National Ilan University, Taiwan)
Prof. Hao-Hsiang Ku (National Taiwan Ocean University, Taiwan)
Prof. Bo-Wei Zhu (Macau University of Science and Technology, Macau)
Prof. Lei Xiong (Sichuan Fine Arts Institute, China)
Prof. Hsiao-Ting Tseng (National United University, Taiwan)
Proceedings
The conference proceedings will be published by IEEE Conference Publication Services (CPS) and will be submitted to the IEEE Xplore Digital Library and EI Compendex.
Submission and Publication Information
All papers need to be submitted electronically through the conference website (https://easychair.org/conferences/?conf=mltrustcom2021) with PDF format. Submitted papers must not substantially overlap with papers that have been published or that are simultaneously submitted to a journal or a conference with proceedings. A submission is limited to 6 pages for workshop papers in the IEEE Computer Society Proceedings Format with Portable Document Format (.pdf). A submission can have at most 4 additional pages with the pages overlength charge if accepted. Papers must be clearly presented in English, including tables, figures, references and appendices. Papers will be selected based on their originality, significance, relevance, and clarity of presentation assessed by at least two reviewers. Submission of a paper should be regarded as a commitment that, should the paper be accepted, at least one of the authors will register and attend the conference to present the work. IEEE TrustCom 2021 reserves the right to exclude a paper from distribution after the conference (e.g., removal from the digital library and indexing services), if the paper is not presented at the conference. Accepted and presented papers will be included in the IEEE CPS Proceedings. Distinguished papers presented at the conference, after further revision, will be published in special issues of high-quality international journals.
Contact
Prof. Chi-Hua Chen, Email: chihua0826@gmail.com