The workshops at DSN'2024 aim to provide a forum where researchers can gather and engage in discussions about various aspects of dependability-related research and its practical applications. These workshops cover a wide range of topics, serving as incubators for specific scientific communities with shared research goals. They also offer a valuable opportunity for researchers to exchange and explore scientific ideas in their early stages, well before reaching the level of maturity required for conference or journal publication.
Website: https://www.dccs2024.com/
Description:
In the era of Industry 4.0, software applications, such as cyber-physical systems (CPS), server systems, machine learning-based systems, databased systems, and real-time systems, are playing an increasingly important role in both industrial world and our daily lives. These systems tend to be composed of many different components which may interact with each other and exhibit complex behaviors. Also, in these systems, dependability is critical, as failures or requirement violations of these systems may lead to disruptive results, even the loss of human lives. Nowadays, extensive studies, including both formal methods and engineering techniques, have been conducted, aiming to improve the systems’ safety, reliability, maintainability, availability, etc. However, for complex systems, the gap between the huge number of interacting components and the salability of current approaches still exists. Nevertheless, we believe the demand of dependable complex systems motivates the development of novel techniques or feasible adaptations of existing techniques, through addressing more issues such as components’ interactions or real-time scheduling. DCCS seeks to provide an international venue to discuss the advanced discoveries and emerging trends related to the dependability of complex systems in both the academic society and industrial world. This workshop will not only explore how we can apply the emerging dependability theories and techniques to model and evaluate the complex systems, but also the methods or tools for localizing and fixing the system vulnerabilities to improve the system dependability.
Website: https://dependablesecureml.github.io/
Description:
Machine learning (ML) is increasingly used in critical domains such as health and wellness, criminal sentencing recommendations, commerce, transportation, human capital management, entertainment, space technology, 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. ML algorithms typically construct a model by training on a labelled 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 many unexpected accidental, and 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 rely on the ML predictions to make decisions or revert to a failsafe mode. Further, ML algorithms are often executed on special-purpose hardware accelerators, which could be subject to faults and attacks. Thus, there is a growing concern regarding the reliability, safety, security & privacy, and accountability of ML-assisted systems.
Website: https://sites.google.com/icmc.usp.br/ssiv
Description:
Over the last years, aerial and ground vehicles as well as mobile robot systems have been receiving an increased number of electronic components, connected through wireless networks and running embedded software. This strong integration between dedicated computing devices and the physical environment, composes a Cyber-Physical System (CPS). CPS have thus become part of common vehicles, accessible to everyone, such as automobiles or unmanned aerial vehicles (UAVs). Furthermore, as processing power increases and software becomes more sophisticated, these vehicles gain the ability to perform complex operations, becoming more autonomous, efficient, adaptable, comfortable, safe and usable. These are known as Intelligent Vehicles (IV).
As processing capabilities and software sophistication continue to advance, these vehicles are evolving into more autonomous, efficient, adaptable, comfortable, safe, and usable entities. Unmanned aerial vehicles (UAVs), in particular, are offering flexible support for diverse missions, enabling deployment in scenarios that were previously challenging or inaccessible. This is achieved through seamless integration with sensor networks capable of monitoring a wide range of applications. On the road, automobiles now provide features such as active safety, adaptive cruise control, park assistance, automatic climate control, navigation support, and, in the near future, vehicle-to-vehicle communication. With networking capabilities, the myriad of devices within vehicles are seamlessly integrated into the Internet of Things (IoT) landscape.
Given their potential impact on human lives and high-value assets, these systems are classified as critical. Safety emerges as a paramount concern for both developers and users. However, the combination of high mobility and wireless communications has further increased the exposure of such systems to malicious threats and to faults deriving from uncertain connectivity or communication timeliness. Non-functional requirements like security and real-time operation have thus become harder to fulfill, creating new challenges to these safety-critical embedded systems. The environment of humans will continue to evolve to interactive IoT that is going to include mobile (flying, driving, floating, rolling, diving, walking, etc) objects that raise numerous challenging issues. Observing the current trend in the development of self-driving cars, one can only infer that artificial intelligence (through machine learning) is going to play a crucial role in future intelligent vehicles. However, the complexity of such algorithms decreases their level of trust and integrating them in critical systems is a far-reaching research issue. Although advanced hardware components like multi-core processors, GPUs, or FPGAs are a formidable opportunity to deploy complex functionalities in intelligent vehicles, they raise new challenges for certification, verification of real-time properties, safety, and security.
This milestone marks the remarkable tenth edition of the workshop, a testament to the enduring success and impact cultivated through the preceding events. As we embark on this historic edition, it becomes increasingly apparent that the landscape of challenges in ensuring Safety and Security in Intelligent Vehicles, whether connected to the Internet or not, is expanding at an unprecedented pace. The wealth of open challenges underscores the critical need for sustained research initiatives and in-depth discussions on these matters—a need that has gained momentum and resonance on a global scale. As we celebrate a decade of collective effort, the 10th edition serves as a poignant reminder of the continuous evolution and significance of our shared commitment to advancing the field.
Therefore, the workshop will keep its focus on exploring the challenges and interdependencies between security, safety, real-time, and certification/standards, which emerge when introducing networked, autonomous, and cooperative functionalities. SSIV aims at joining together in an active debate, researchers, and practitioners from several communities, namely dependability and security, real-time and embedded systems, intelligent transportation, and mobile robot systems.
A non-exhaustive list of topics of interest include the following:
Website: https://verdi-workshop.github.io/2024/
Description:
Cyber-Physical Systems (CPS) are a class of engineering systems where computation and communication interact with physical processes, providing complex, situation-aware, and often safety-, security-, or mission-critical ecosystems and services. The fast increase and availability of communication bandwidth and computational power, as well as emerging computing paradigms such as Cloud Computing, Edge Computing, and Deep Learning, are pushing forward CPS research and development, and establishing them as promising engineering solutions to address challenges arising in areas as diverse as aerospace, automotive, energy, disaster response, health care, smart farming, manufacturing, city management, among others.
A key property that CPS are expected to exhibit is that of dependability, that is, the ability to provide services that can be trusted within well determined time-periods, and equally important, that those service guarantees hold even when the system is subject to faults and attacks. A key ingredient to ensure dependability is thus to successfully apply verification & validation (V&V) techniques and attest the desired levels of safety, security, and privacy. Here V&V refers to the process of determining whether the requirements for a system or component are complete and correct, the products of each development phase fulfil the requirements or conditions imposed by the previous phase, and the final system or component complies with the specified requirements.1 This is a challenging task that comes with significant time and cost implications for all the organizations involved in the build-up and evaluation of CPS. This challenge becomes even more demanding with the incorporation of more and more AI components (software and hardware) enabled by the notion of Compute Continuum into the operational capabilities of CPS for handling tasks that are increasingly complex.
The VERDI workshop aims at serving as a discussion forum focused on the area of V&V as a means to guarantee dependability of complex, potentially automated/autonomous CPS. We welcome submissions in IEEE two-column conference style in two formats: short papers (up to 4 pages) and full papers (up to 8 pages). The workshop covers all aspects related to the dependability evaluation (with special focus on safety and security) of safety-critical CPS using techniques such as fault/attack-injection, static and/or dynamic formal verification, semi-formal analysis, simulation, and testing. Topics include, but are not limited to:
Zhi Zhang, University of Western Australia, Australia
Rizwan Asghar, University of Surrey, UK
For further information please send an email to workshops@dsn.org