Vulnerability Analysis and Adversarial Learning (VAAL 2022)

Scope

Welcome to VAAL 2022, the 4th workshop on Vulnerability Analysis and Adversarial Learning. The workshop focus on vulnerability analysis of information systems, especially intelligent systems. Furthermore, we follow closely how these vulnerabilities being used, i.e. attack and defense toward intelligent algorithms /models/systems and etc.

Traditionally, vulnerabilities come from unreasonable software design, non-standard programming and etc. Recently,advance of vulnerability analysis in intelligent systems, especially in machine learning algorithms, get more and more attention.

Vulnerabilities of machine learning algorithms or models is the foundation of Adversarial Learning, which is a novel research area that lies at the intersection of machine learning and computer security. It aims at gaining a deeper understanding of the security properties of current machine learning algorithms against carefully targeted attacks, and at developing suitable countermeasure for the design of more secure learning algorithm.

Research on vulnerability analysis and adversarial learning have got increasing research attention in recent years. And thus, we launched the fourth workshop on VAAL. This workshop aims to increase potential collaborations and partnerships by bring together academic researchers and industry practitioners from information system vulnerability analysis andadversarial learning with the objectives to present updated research efforts and progresses on foundational and emerging topics of VAAL, exchange new ideas and identify future research directions.


WORKSHOP AREAS

Topic interest include but not limited to:

1.Information systemvulnerability analysis
2.Vulnerability analysis theory and method
3.Vulnerability mechanism model and pattern
4.Machine learning for vulnerability analysis
5.Vulnerability analysis of AI algorithms /models/systems
6.Formal theory for adversarial leaning
7.Evaluation metrics for adversarial learning
8.Evaluation metrics for adversarial learning


PAPER SUBMISSION

All submissions should be written in English and submitted via our submission system: https://cmt3.research.microsoft.com/VAAL2022. A paper submitted to VAAL 2022 cannot be under review for any other conference or journal during the entire period that it is considered for VAAL 2022, and must be substantially different from any previously published work.Submissions are reviewed in a single-blind manner.Please note that all submissions must strictly adhere to the IEEE templates asprovided at http://www.ieeedsc.org/2022/submission.html. The templates also act as a guideline regarding formatting. In particular, all submissions must use either the LATEXtemplate or the MS-Word template. Please follow exactly the instructions below to ensure that your submission can ultimately be included in the proceedings.


IMPORTANT DATES

Full paper due: April 30, 2022
Acceptance notification: June 18, 2022
Camera-ready copy: June 26, 2022
Conference Date: July 11-13, 2022

ORGANIZATION

Workshop General Chairs

Xiaohui KuangNational Key Laboratory of Science and Technology on Information System Security,China
Hongfang Yu University of Electronic Science and Technology of China, China

Workshop Co-Chair

Zheli Liu, Nankai University, China
Yu Jiang, Tsinghua University, China
Yuanping Nie, National Key Laboratory of Science and Technology on Information System Security, China

Program Committee

Zhi Wang, Nankai University, China
Jie Liang, Tsinghua University, China
Hongfang Yu, University of Electronic Science and Technology of China, China
Changqiao Xu, Beijing University of Posts and Telecommunications, China
Yue Yu, National University of Defense Technology, China
Zhendong Wu, National Key Laboratory of Science and Technology on Information System Security, China
Hua Chen, National Key Laboratory of Science and Technology on Information System Security, China
Chenhao Lin, Xian Jiaotong University, China
Yufei Chen, Xian Jiaotong University, China
Cheng Qian, National Key Laboratory of Science and Technology on Information System Security, China
Ting Bao, National Key Laboratory of Science and Technology on Information System Security, China



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