The workshops at the DSN 2022 provide a forum for groups of researchers to discuss topics in dependability-related research and practice. DSN workshops can be quite diverse. They serve as incubators for scientific communities that form and share a particular research agenda. They also provide opportunities for researchers to exchange and discuss scientific ideas at an early stage, before they have matured to warrant conference or journal publication.
Website: http://dcds.lasige.di.fc.ul.pt/
Description:
The workshop aims at providing researchers with a forum to exchange and discuss scientific contributions and open challenges, both theoretical and practical, related to the use of data-centric approaches that promote the dependability and cybersecurity of computing systems. We want to foster joint work and knowledge exchange between the dependability and security communities, and researchers and practitioners from areas such as machine and statistical learning, and data science and visualization. The workshop provides a forum for discussing novel trends in data-centric processing technologies and the role of such technologies in the development of resilient systems. It aims to discuss novel approaches for processing and analyzing data generated by the systems as well as information gathered from open sources, leveraging from data science, machine and statistical learning techniques, and visualization. The workshop shall contribute to identify new application areas as well as open and future research problems, for data-centric approaches to system dependability and security.
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Website: https://aits2022.github.io/
Description:
With the development of the Internet, cyber security becomes more and more important. Facing increasing cyber-attacks, the traditional methods to protect information systems are becoming lagging and powerless. Fortunately, artificial intelligent technology offers a completely new and challenging opportunity to the security practitioners. The aim of Internet Workshop on Artificial Intelligence To Security (AITS 2022) is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share knowledge and results in theory, methodology and applications in cyberspace security by artificial intelligent technology.
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Website: https://dependablesecureml.github.io/index.html
Description:
Machine learning (ML) is increasingly used in critical domains such as health and wellness, criminal sentencing recommendations, commerce, transportation, human capital management, entertainment, and communication. The design of ML systems has mainly focused on developing models, algorithms, and datasets on which they are trained to demonstrate high accuracy for specific tasks such as object recognition and classification. Machine learning algorithms typically construct a model by training on a labeled training dataset and their performance is assessed based on the accuracy in predicting labels for unseen (but often similar) testing data. This is based on the assumption that the training dataset is representative of the inputs that the system will face in deployment. However, in practice there are a wide variety of unexpected accidental, as well as adversarially-crafted, perturbations on the ML inputs that might lead to violations of this assumption. ML algorithms are also often over-confident about their predictions when processing such unexpected inputs. This makes it difficult to deploy them in safety critical settings where one needs to be able to rely on the ML predictions to make decisions or revert back to a failsafe mode. Further, ML algorithms are often executed on special-purpose hardware accelerators, which may themselves be subject to faults. Thus, there is a growing concern regarding the reliability, safety, security, and accountability of machine learning systems.
The DSN Workshop on Dependable and Secure Machine Learning (DSML) is an open forum for researchers, practitioners, and regulatory experts, to present and discuss innovative ideas and practical techniques and tools for producing dependable and secure ML systems. A major goal of the workshop is to draw the attention of the research community to the problem of establishing guarantees of reliability, security, safety, and robustness for systems that incorporate increasingly complex ML models, and to the challenge of determining whether such systems can comply with requirements for safety-critical systems. A further goal is to build a research community at the intersection of machine learning and dependable and secure computing.
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Website: https://sites.google.com/view/ssiv
Description:
This will be the eighth edition of the SSIV workshop, aimed at continuing the success of previous editions. The vast range of open challenges to achieve Safety and Security in Intelligent Vehicles (with or without connection with the Internet) is a fundamental reason that justifies the numerous research initiatives and wide discussion on these matters, which we are currently observing everywhere.
The successful pairing of man and machine, represented by robotics solutions that augment humans, has the potential to make our workforce safer and more productive and provide a non-conventional way of transportation. Therefore, the workshop will keep its focus on exploring the challenges and interdependencies between security, real-time, safety and certification, which emerge when introducing networked, autonomous and cooperative functionalities.
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