Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles

The damaging effects of cyberattacks to an industry like the Cooperative Connected and Automated Mobility (CCAM) can be tremendous. From the least important to the worst ones, one can mention for example the damage in the reputation of vehicle manufacturers, the increased denial of customers to adopt CCAM, the loss of working hours (having direct impact on the European GDP), material damages, increased environmental pollution due e.g., to traffic jams or malicious modifications in sensors’ firmware, and ultimately, the great danger for human lives, either they are drivers, passengers or pedestrians. Connected vehicles will soon become a reality on our roads, bringing along new services and capabilities, but also technical challenges and security threats. To overcome these risks, the CARAMEL project has developed several anti-hacking solutions for the new generation of vehicles. CARAMEL (Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles), a research project co-funded by the European Union under the Horizon 2020 framework programme, is a project consortium with 15 organizations from 8 European countries together with 3 Korean partners. The project applies a proactive approach based on Artificial Intelligence and Machine Learning techniques to detect and prevent potential cybersecurity threats to autonomous and connected vehicles. This approach has been addressed based on four fundamental pillars, namely: Autonomous Mobility, Connected Mobility, Electromobility, and Remote Control Vehicle. This book presents theory and results from each of these technical directions

Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles

The damaging effects of cyberattacks to an industry like the Cooperative Connected and Automated Mobility (CCAM) can be tremendous. From the least important to the worst ones, one can mention for example the damage in the reputation of vehicle manufacturers, the increased denial of customers to adopt CCAM, the loss of working hours (having direct impact on the European GDP), material damages, increased environmental pollution due e.g., to traffic jams or malicious modifications in sensors’ firmware, and ultimately, the great danger for human lives, either they are drivers, passengers or pedestrians. Connected vehicles will soon become a reality on our roads, bringing along new services and capabilities, but also technical challenges and security threats. To overcome these risks, the CARAMEL project has developed several anti-hacking solutions for the new generation of vehicles. CARAMEL (Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles), a research project co-funded by the European Union under the Horizon 2020 framework programme, is a project consortium with 15 organizations from 8 European countries together with 3 Korean partners. The project applies a proactive approach based on Artificial Intelligence and Machine Learning techniques to detect and prevent potential cybersecurity threats to autonomous and connected vehicles. This approach has been addressed based on four fundamental pillars, namely: Autonomous Mobility, Connected Mobility, Electromobility, and Remote Control Vehicle. This book presents theory and results from each of these technical directions

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

2016-2017 Graduate Catalog

https://digitalcommons.sacredheart.edu/g_cat/1020/thumbnail.jp

Multi-attribute tradespace exploration for survivability

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 235-249).Survivability is the ability of a system to minimize the impact of a finite-duration disturbance on value delivery (i.e., stakeholder benefit at cost), achieved through (1) the reduction of the likelihood or magnitude of a disturbance, (2) the satisfaction of a minimally acceptable level of value delivery during and after a disturbance, and/or (3) a timely recovery. Traditionally specified as a requirement in military systems, survivability is an increasingly important consideration for all engineering systems given the proliferation of natural and artificial threats. Although survivability is an emergent system property that arises from interactions between a system and its environment, conventional approaches to survivability engineering are reductionist in nature. Furthermore, current methods neither accommodate dynamic threat environments nor facilitate stakeholder communication for conducting trade-offs among system lifecycle cost, mission utility, and operational survivability. Multi-Attribute Tradespace Exploration (MATE) for Survivability is introduced as a system analysis methodology to improve the generation and evaluation of survivable alternatives during conceptual design. MATE for Survivability applies decision theory to the parametric modeling of thousands of design alternatives across representative distributions of disturbance environments. To improve the generation of survivable alternatives, seventeen empirically-validated survivability design principles are introduced. The general set of design principles allows the consideration of structural and behavioral strategies for mitigating the impact of disturbances over the lifecycle of a given encounter.(cont.) To improve the evaluation of survivability, value-based metrics are introduced for the assessment of survivability as a dynamic, continuous, and path-dependent system property. Two of these metrics, time-weighted average utility loss and threshold availability, are used to evaluate survivability based on the relationship between stochastic utility trajectories of system state and stakeholder expectations across nominal and perturbed environments. Finally, the survivability "tear(drop)" tradespace is introduced to enable the identification of inherently survivable architectures that efficiently balance performance metrics of cost, utility, and survivability. The internal validity and prescriptive value of the design principles, metrics, and tradespaces comprising MATE for Survivability are established through applications to the designs of an orbital transfer vehicle and a satellite radar system.by Matthew G. Richards.Ph.D

2020-2021 Graduate Catalog

https://digitalcommons.sacredheart.edu/g_cat/1066/thumbnail.jp

2019-2020 Graduate Catalog

https://digitalcommons.sacredheart.edu/g_cat/1063/thumbnail.jp

Undergraduate and Graduate Course Descriptions, 2016 Fall

Wright State University undergraduate and graduate course descriptions from Fall 2016

Undergraduate and Graduate Course Descriptions, 2017 Fall

Wright State University undergraduate and graduate course descriptions from Fall 2017

Undergraduate and Graduate Course Descriptions, 2017 Fall

Wright State University undergraduate and graduate course descriptions from Fall 2017