Topic
Cyber-physical system
About: Cyber-physical system is a research topic. Over the lifetime, 11096 publications have been published within this topic receiving 162489 citations. The topic is also known as: CPS.
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01 Jan 2017
TL;DR: With this book industrial production systems engineering researchers will get a better understanding of the challenges and requirements of multi-disciplinary engineering that will guide them in future research and development activities.
77 citations
TL;DR: In this article, the concept of feature-based manufacturing for adaptive equipment control and resource-task matching in distributed and collaborative CPS manufacturing environments is presented, based on the combination of product manufacturing features and event-driven Function Blocks (FB) of the IEC 61499 standard.
77 citations
TL;DR: This work classifies applicable cyber threats according to a novel taxonomy, focusing not only on the attack vectors that can be used, but also the potential impact on the systems and ultimately on the occupants and their domestic life.
77 citations
TL;DR: This survey aims to provide researchers with a state-of-the-art overview of various techniques for multi-camera coordination and control (MC3) that have been adopted in surveillance systems.
Abstract: The use of multiple heterogeneous cameras is becoming more common in today's surveillance systems. In order to perform surveillance tasks, effective coordination and control in multi-camera systems is very important, and is catching significant research attention these days. This survey aims to provide researchers with a state-of-the-art overview of various techniques for multi-camera coordination and control (MC3) that have been adopted in surveillance systems. The existing literature on MC3 is presented through several classifications based on the applicable architectures, frameworks and the associated surveillance tasks. Finally, a discussion on the open problems in surveillance area that can be solved effectively using MC3 and the future directions in MC3 research is presented
77 citations
TL;DR: The good, the bad, and the ugly use of machine learning for cybersecurity and CPS/IoT are considered; existing mechanisms with the potential to improve target acquisition and existing threat patterns are considered, as well as those that can enable novel attacks yet to be seen.
Abstract: The advancement of the Internet of Things (IoT) has allowed for unprecedented data collection, automation, and remote sensing and actuation, transforming autonomous systems and bringing smart command and control into numerous cyber physical systems (CPS) that our daily lives depend on. Simultaneously, dramatic improvements in machine learning and deep neural network architectures have enabled unprecedented analytical capabilities, which we see in increasingly common applications and production technologies, such as self-driving vehicles and intelligent mobile applications. Predictably, these technologies have seen rapid adoption, which has left many implementations vulnerable to threats unforeseen or undefended against. Moreover, such technologies can be used by malicious actors, and the potential for cyber threats, attacks, intrusions, and obfuscation that are only just being considered, applied, and countered. In this paper, we consider the good, the bad, and the ugly use of machine learning for cybersecurity and CPS/IoT. In detail, we consider the numerous benefits (good use) that machine learning has brought, both in general, and specifically for security and CPS/IoT, such as the improvement of intrusion detection mechanisms and decision accuracy in CPS/IoT. More pressing, we consider the vulnerabilities of machine learning (bad use) from the perspectives of security and CPS/IoT, including the ways in which machine learning systems can be compromised, misled, and subverted at all stages of the machine learning life-cycle (data collection, pre-processing, training, validation, implementation, etc.). Finally, the most concerning, a growing trend has been the utilization of machine learning in the execution of cyberattacks and intrusions (ugly use). Thus, we consider existing mechanisms with the potential to improve target acquisition and existing threat patterns, as well as those that can enable novel attacks yet to be seen.
77 citations