Special Session on TMLAE, IEEE DASC 2022 : Special Session on Trustworthiness of Machine Learning in Adversarial Environments, 20th IEEE International Conference on Dependable, Autonomic & Secure Computing

Call for Papers
Special Session on Trustworthiness of Machine Learning in Adversarial Environments (TMLAE) In conjunction with the
20th IEEE International Conference on Dependable, Autonomic & Secure Computing (DASC 2022)

In recent years, machine learning and deep learning algorithms have been the frontier of Artificial Intelligence (AI) that reshape the current landscape of computing, which have achieved huge success in various domains, including smart transportation, smart manufacturing, smart healthcare, business, smart cities, modern power systems, social media, etc. However, AI system that are implemented by machine learning models suffer from adversarial attack vulnerability. Adversarial attacks aim at deceiving the AI system by inserting adversarial examples into the machine learning models to make false and/or inaccurate predictions. The aim of this special session is to establish a venue for scientists and engineers from academia, government, and industry to present and discuss latest advances and technologies on adversarial machine learning theories and applications, and related cyber security issues. The scope of this proposed special session is study and address adversarial machine learning techniques used in dealing with cybersecurity issues in various applications, as well as a wide range of related issues from machine learning, deep learning, AI, and cybersecurity in the following list of topics:

• Adversarial Machine Learning and Reinforcement Learning
• Adversarial attacks and defenses in Internet of Things/Cyber-physical systems
• Adversarial attacks and defenses in software systems
• Adversarial attacks and defenses in malware detection and intrusion environments
• Data poisoning and evasion attacks
• Advanced techniques for generating adversarial examples
• Advanced defense mechanisms for adversarial attacks
• Vulnerability and security of machine learning/deep learning models
• AI assurance and security
• Secure machine learning systems in context of software security development
• New benchmark datasets for adversarial machine learning
• Industrial practices on adversarial machine learning and cybersecurity

You are invited to submit a 4-6 pages original paper according to the DASC 2022 submission policy using the conference website. This special session will be held in the 20th IEEE DASC 2022, September 12-15, Calabria, Italy. All papers accepted in this special session will be included in the DASC 2022 conference proceedings published by IEEE. See details of submission policy via http://cyber-science.org/2022/cyberscitech/papersubmissions/.

Important Dates
Paper Submission: June 1, 2022
Author Notification: July 1, 2022
Camera- ready Submission: July 15, 2022

Program Chair
Prof. Yi Wang Manhattan College, USA [email protected]

Program Cochairs:
Prof. Miaomiao Zhang Manhattan College, USA [email protected]
Prof. Bingyang Wei Texas Christian University, USA [email protected]


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