910 research outputs found
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Detecting cyber-physical threats in an autonomous robotic vehicle using Bayesian Networks
Robotic vehicles and especially autonomous robotic vehicles can be attractive targets for attacks that cross the cyber-physical divide, that is cyber attacks or sensory channel attacks affecting the ability to navigate or complete a mission. Detection of such threats is typically limited to knowledge-based and vehicle-specific methods, which are applicable to only specific known attacks, or methods that require computation power that is prohibitive for resource-constrained vehicles. Here, we present a method based on Bayesian Networks that can not only tell whether an autonomous vehicle is under attack, but also whether the attack has originated from the cyber or the physical domain. We demonstrate the feasibility of the approach on an autonomous robotic vehicle built in accordance with the Generic Vehicle Architecture specification and equipped with a variety of popular communication and sensing technologies. The results of experiments involving command injection, rogue node and magnetic interference attacks show that the approach is promising
Survey of Bayesian Networks Applications to Intelligent Autonomous Vehicles
This article reviews the applications of Bayesian Networks to Intelligent
Autonomous Vehicles (IAV) from the decision making point of view, which
represents the final step for fully Autonomous Vehicles (currently under
discussion). Until now, when it comes making high level decisions for
Autonomous Vehicles (AVs), humans have the last word. Based on the works cited
in this article and analysis done here, the modules of a general decision
making framework and its variables are inferred. Many efforts have been made in
the labs showing Bayesian Networks as a promising computer model for decision
making. Further research should go into the direction of testing Bayesian
Network models in real situations. In addition to the applications, Bayesian
Network fundamentals are introduced as elements to consider when developing
IAVs with the potential of making high level judgement calls.Comment: 34 pages, 2 figures, 3 table
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Cloud-based cyber-physical intrusion detection for vehicles using Deep Learning
Detection of cyber attacks against vehicles is of growing interest. As vehicles typically afford limited processing resources, proposed solutions are rule-based or lightweight machine learning techniques. We argue that this limitation can be lifted with computational offloading commonly used for resource-constrained mobile devices. The increased processing resources available in this manner allow access to more advanced techniques. Using as case study a small four-wheel robotic land vehicle, we demonstrate the practicality and benefits of offloading the continuous task of intrusion detection that is based on deep learning. This approach achieves high accuracy much more consistently than with standard machine learning techniques and is not limited to a single type of attack or the in-vehicle CAN bus as previous work. As input, it uses data captured in real-time that relate to both cyber and physical processes, which it feeds as time series data to a neural network architecture. We use both a deep multilayer perceptron and a recurrent neural network architecture, with the latter benefitting from a long-short term memory hidden layer, which proves very useful for learning the temporal context of different attacks. We employ denial of service, command injection and malware as examples of cyber attacks that are meaningful for a robotic vehicle. The practicality of the latter depends on the resources afforded onboard and remotely, as well as the reliability of the communication means between them. Using detection latency as the criterion, we have developed a mathematical model to determine when computation offloading is beneficial given parameters related to the operation of the network and the processing demands of the deep learning model. The more reliable the network and the greater the processing demands, the greater the reduction in detection latency achieved through offloading
A Survey on Trust Metrics for Autonomous Robotic Systems
This paper surveys the area of Trust Metrics related to security for
autonomous robotic systems. As the robotics industry undergoes a transformation
from programmed, task oriented, systems to Artificial Intelligence-enabled
learning, these autonomous systems become vulnerable to several security risks,
making a security assessment of these systems of critical importance.
Therefore, our focus is on a holistic approach for assessing system trust which
requires incorporating system, hardware, software, cognitive robustness, and
supplier level trust metrics into a unified model of trust. We set out to
determine if there were already trust metrics that defined such a holistic
system approach. While there are extensive writings related to various aspects
of robotic systems such as, risk management, safety, security assurance and so
on, each source only covered subsets of an overall system and did not
consistently incorporate the relevant costs in their metrics. This paper
attempts to put this prior work into perspective, and to show how it might be
extended to develop useful system-level trust metrics for evaluating complex
robotic (and other) systems
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A taxonomy and survey of cyber-physical intrusion detection approaches for vehicles
With the growing threat of cyber and cyber-physical attacks against automobiles, drones, ships, driverless pods and other vehicles, there is also a growing need for intrusion detection approaches that can facilitate defence against such threats. Vehicles tend to have limited processing resources and are energy-constrained. So, any security provision needs to abide by these limitations. At the same time, attacks against vehicles are very rare, often making knowledge-based intrusion detection systems less practical than behaviour-based ones, which is the reverse of what is seen in conventional computing systems. Furthermore, vehicle design and implementation can differ wildly between different types or different manufacturers, which can lead to intrusion detection designs that are vehicle-specific. Equally importantly, vehicles are practically defined by their ability to move, autonomously or not. Movement, as well as other physical manifestations of their operation may allow cyber security breaches to lead to physical damage, but can also be an opportunity for detection. For example, physical sensing can contribute to more accurate or more rapid intrusion detection through observation and analysis of physical manifestations of a security breach. This paper presents a classification and survey of intrusion detection systems designed and evaluated specifically on vehicles and networks of vehicles. Its aim is to help identify existing techniques that can be adopted in the industry, along with their advantages and disadvantages, as well as to identify gaps in the literature, which are attractive and highly meaningful areas of future research
BIBLIOMETRIC STUDY ON THE DEVELOPMENT AND IMPLEMENTATION OF CYBERSECURITY IN AUTONOMOUS VEHICLES
The main objective was to examine the trajectory of scientific research in this domain, identify the most influential publications related to cybersecurity in autonomous vehicles and pinpoint research opportunities, supported by the PRISMA method. Additionally, the study explores cybersecurity themes in autonomous vehicles, emphasizing the significance of concepts like blockchain, machine learning, and deep learning essential in formulating business strategies. Furthermore, the research identifies influential scientific publications, predominant journals, the most productive countries, and authors with the most publications on cybersecurity in autonomous vehicles. It identifies research opportunities organized into two distinct clusters to provide a comprehensive understanding of the current state of research in this field and offer insights for companies and academics interested in contributing to future advancements in the cybersecurity of autonomous vehicles. The article demonstrates that cybersecurity is a fundamental area for the development and implementation of secure and reliable autonomous vehicles.info:eu-repo/semantics/publishedVersio
BIBLIOMETRIC STUDY ON THE DEVELOPMENT AND IMPLEMENTATION OF CYBERSECURITY IN AUTONOMOUS VEHICLES
The main objective was to examine the trajectory of scientific research in this domain, identify the most influential publications related to cybersecurity in autonomous vehicles and pinpoint research opportunities, supported by the PRISMA method. Additionally, the study explores cybersecurity themes in autonomous vehicles, emphasizing the significance of concepts like blockchain, machine learning, and deep learning essential in formulating business strategies. Furthermore, the research identifies influential scientific publications, predominant journals, the most productive countries, and authors with the most publications on cybersecurity in autonomous vehicles. It identifies research opportunities organized into two distinct clusters to provide a comprehensive understanding of the current state of research in this field and offer insights for companies and academics interested in contributing to future advancements in the cybersecurity of autonomous vehicles. The article demonstrates that cybersecurity is a fundamental area for the development and implementation of secure and reliable autonomous vehicles.info:eu-repo/semantics/publishedVersio
Autonomous Vehicles an overview on system, cyber security, risks, issues, and a way forward
This chapter explores the complex realm of autonomous cars, analyzing their
fundamental components and operational characteristics. The initial phase of
the discussion is elucidating the internal mechanics of these automobiles,
encompassing the crucial involvement of sensors, artificial intelligence (AI)
identification systems, control mechanisms, and their integration with
cloud-based servers within the framework of the Internet of Things (IoT). It
delves into practical implementations of autonomous cars, emphasizing their
utilization in forecasting traffic patterns and transforming the dynamics of
transportation. The text also explores the topic of Robotic Process Automation
(RPA), illustrating the impact of autonomous cars on different businesses
through the automation of tasks. The primary focus of this investigation lies
in the realm of cybersecurity, specifically in the context of autonomous
vehicles. A comprehensive analysis will be conducted to explore various risk
management solutions aimed at protecting these vehicles from potential threats
including ethical, environmental, legal, professional, and social dimensions,
offering a comprehensive perspective on their societal implications. A
strategic plan for addressing the challenges and proposing strategies for
effectively traversing the complex terrain of autonomous car systems,
cybersecurity, hazards, and other concerns are some resources for acquiring an
understanding of the intricate realm of autonomous cars and their ramifications
in contemporary society, supported by a comprehensive compilation of resources
for additional investigation.
Keywords: RPA, Cyber Security, AV, Risk, Smart Car
Performance of Machine Learning and Big Data Analytics paradigms in Cybersecurity and Cloud Computing Platforms
The purpose of the research is to evaluate Machine Learning and Big Data Analytics paradigms for use in Cybersecurity. Cybersecurity refers to a combination of technologies, processes and operations that are framed to protect information systems, computers, devices, programs, data and networks from internal or external threats, harm, damage, attacks or unauthorized access. The main characteristic of Machine Learning (ML) is the automatic data analysis of large data sets and production of models for the general relationships found among data. ML algorithms, as part of Artificial Intelligence, can be clustered into supervised, unsupervised, semi-supervised, and reinforcement learning algorithms
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