The 1st International Workshop on Machine Learning and Deep Learning for Data Security Applications (MLDLDSA 2021)

 

In conjunction with

The 26th International Conference on Database Systems for Advanced Applications

(DASFAA 2021)

 

April 11-14, 2021, Taipei International Convention Center, Taiwan

 

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 data security applications. For instance, machine learning methods have been used to analyze the behavior 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 behavior for preventing cyber-attacks and malfunctions.

 

While the area of machine learning and deep learning methods for data security applications is a rapidly expanding field of scientific research, several open research questions still need to be discussed and studied. For instance, using and improving machine learning and deep 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 database systems and data management. This workshop named "Machine Learning for Data Security Applications" in conjunction with the 26th International Conference on Database Systems for Advanced Applications (DASFAA 2021) will solicit papers on various disciplines of data security applications, including but not limited to:

 

Topics

Ø   New supervised machine learning methods for data security applications

Ø   New unsupervised machine learning methods for data security applications

Ø   New reinforcement learning methods for data security applications

Ø   New federated learning methods for data security applications

Ø   New optimization methods for data security applications

Ø   New homomorphic encryption methods for data security applications

 

Important Dates:

Paper Submission Deadline

November 30, 2020 (PDT) December 15, 2020 (Extension)

Author Notification

January 04, 2021

Conference Dates

April 11-14, 2021

 

Organizing Committee

Steering Committee

Ø   Prof. Wenzhong Guo (Fuzhou University, China)

Ø   Prof. Chin-Chen Chang (IEEE Fellow; Feng-Chia University, Taiwan)

 

General Chairs

Ø   Prof. Chi-Hua Chen (Fuzhou University, China)

Ø   Prof. Brij B. Gupta (National Institute of Technology Kurukshetra, India)

 

Session Chairs

Ø   Prof. Feng-Jang Hwang (University of Technology Sydney, Australia)

Ø   Prof. Fuquan Zhang (Minjiang University, China)

Ø   Prof. Yu-Chih Wei (National Taipei University of Technology, Taiwan)

Ø   Prof. K. Shankar (Alagappa University, India)

 

Technical Program Committee

Ø   Prof. Haishuai Wang (Fairfield University & Harvard University, United States of America)

Ø   Prof. Eyhab Al-Masri (University of Washington Tacoma, United States of America)

Ø   Prof. Xianbiao Hu (Missouri University of Science and Technology, United States of America)

Ø   Prof. Victor Hugo C. de Albuquerque (University of Fortaleza, Brazil)

Ø   Prof. Xiao-Guang Yue (European University Cyprus, Cyprus)

Ø   Prof. Hanhua Chen (Huazhong University of Science and Technology, China)

Ø   Dr. Ching-Chun Chang (Tsinghua University, China)

Ø   Prof. Chunjia Han (University of Greenwich, United Kingdom)

Ø   Dr. Doris Xin (Newcastle University, United Kingdom)

Ø   Dr. Lingjuan Lyu (National University of Singapore, Singapore)

Ø   Prof. Ting Bi (Maynooth University, Ireland)

Ø   Prof. Fang-Jing Wu (Technische Universität Dortmund, Germany)

Ø   Dr. Paula Fraga-Lamas (Universidade da Coruña, Spain)

Ø   Prof. Usman Tariq (Prince Sattam bin Abdulaziz University, Saudi Arabia)

Ø   Prof. Hsu-Yang Kung (National Pingtung University of Science and Technology, Taiwan)

Ø   Prof. Chin-Ling Chen (Chaoyang University of Technology, Taiwan)

Ø   Prof. Hao-Chun Lu (Chang Gung University, Taiwan)

Ø   Prof. Yao-Huei Huang (Fu-Jen Catholic University, Taiwan)

Ø   Prof. Hao-Hsiang Ku (National Taiwan Ocean University, Taiwan)

Ø   Prof. Hsiao-Ting Tseng (National Central University, Taiwan)

Ø   Prof. Chia-Yu Lin (Yuan Ze University, Taiwan)

Ø   Dr. Bon-Yeh Lin (Chunghwa Telecom Co. Ltd., Taiwan)

Ø   Prof. Jianbin Qin (Shenzhen University, China)

Ø   Prof. Liang-Hung Wang (Fuzhou University, China)

Ø   Prof. Fangying Song (Fuzhou University, China)

Ø   Prof. Genggeng Liu (Fuzhou University, China)

Ø   Prof. Chan-Liang Chung (Fuzhou University, China)

Ø   Prof. Lianrong Pu (Fuzhou University, China)

Ø   Dr. Ling Wu (Fuzhou University, China)

Ø   Dr. Xiaoyan Li (Fuzhou University, China)

Ø   Prof. Mingyang Pan (Dalian Maritime University, China)

Ø   Prof. Cheng Shi (Xi'an University of Technology, China)

Ø   Prof. Chih-Min Yu (Yango University, China)

Ø   Prof. Lei Xiong (Guangzhou Academy of Fine Arts, China)

Ø   Prof. Bo-Wei Zhu (Macau University of Science and Technology, Macau)

Ø   Dr. Insaf Ullah (Hamdard University, Pakistan)

 

Publication

The papers accepted by DASFAA 2021 workshop will be published in a combined volume of Lecture Notes in Computer Science series published by Springer, and indexed by both LNCS and DBLP.

 

Submission and Publication Information

Paper submission must be in English. All papers will be double-blind reviewed by the Program Committee based on technical quality, relevance to DASFAA, originality, significance, and clarity. All paper submissions will be handled electronically. Any submitted paper violating the length, file type, or formatting requirements will be rejected without review. Each submitted paper should include an abstract up to 200 words and be no longer than 16 pages (including references, appendices, etc.) in LNCS (Lecture Notes in Computer Science) format. We encourage authors to cite related work comprehensively. When citing conference papers, please also consider citing their extended journal versions if applicable. All papers need to be submitted electronically through the conference website (https://easychair.org/conferences/?conf=mldsa2021) with PDF format.

 

The submitted papers must not be previously published anywhere and must not be under consideration by any other conference or journal during the DASFAA review process. If the paper was accepted, at least one author will complete the regular registration and attend the conference to present the paper. For no-show authors, their papers will not be included in the proceedings. Accepted papers will be published in the conference proceedings.

 

Formatting Template

Please use one of the following templates for the LNCS (Lecture Notes in Computer Science) format: https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines

 

Contact

Prof. Chi-Hua Chen, Email: chihua0826@gmail.com