1,383 research outputs found
Security of GPS/INS based On-road Location Tracking Systems
Location information is critical to a wide-variety of navigation and tracking
applications. Today, GPS is the de-facto outdoor localization system but has
been shown to be vulnerable to signal spoofing attacks. Inertial Navigation
Systems (INS) are emerging as a popular complementary system, especially in
road transportation systems as they enable improved navigation and tracking as
well as offer resilience to wireless signals spoofing, and jamming attacks. In
this paper, we evaluate the security guarantees of INS-aided GPS tracking and
navigation for road transportation systems. We consider an adversary required
to travel from a source location to a destination, and monitored by a INS-aided
GPS system. The goal of the adversary is to travel to alternate locations
without being detected. We developed and evaluated algorithms that achieve such
goal, providing the adversary significant latitude. Our algorithms build a
graph model for a given road network and enable us to derive potential
destinations an attacker can reach without raising alarms even with the
INS-aided GPS tracking and navigation system. The algorithms render the
gyroscope and accelerometer sensors useless as they generate road trajectories
indistinguishable from plausible paths (both in terms of turn angles and roads
curvature). We also designed, built, and demonstrated that the magnetometer can
be actively spoofed using a combination of carefully controlled coils. We
implemented and evaluated the impact of the attack using both real-world and
simulated driving traces in more than 10 cities located around the world. Our
evaluations show that it is possible for an attacker to reach destinations that
are as far as 30 km away from the true destination without being detected. We
also show that it is possible for the adversary to reach almost 60-80% of
possible points within the target region in some cities
Analysis of cyber risk and associated concentration of research (ACR)² in the security of vehicular edge clouds
Intelligent Transportation Systems (ITS) is a rapidly growing research space with many issues and challenges. One of the major concerns is to successfully integrate connected technologies, such as cloud infrastructure and edge cloud, into ITS. Security has been identified as one of the greatest challenges for the ITS, and security measures require consideration from design to implementation. This work focuses on providing an analysis of cyber risk and associated concentration of research (ACR2). The introduction of ACR2 approach can be used to consider research challenges in VEC and open up further investigation into those threats that are important but under-researched. That is, the approach can identify very high or high risk areas that have a low research concentration. In this way, this research can lay the foundations for the development of further work in securing the future of ITS
CARAMEL: results on a secure architecture for connected and autonomous vehicles detecting GPS spoofing attacks
The main goal of the H2020-CARAMEL project is to address the cybersecurity gaps introduced by the new technological domains adopted by modern vehicles applying, among others, advanced Artificial Intelligence and Machine Learning techniques. As a result, CARAMEL enhances the protection against threats related to automated driving, smart charging of Electric Vehicles, and communication among vehicles or between vehicles and the roadside infrastructure. This work focuses on the latter and presents the CARAMEL architecture aiming at assessing the integrity of the information transmitted by vehicles, as well as at improving the security and privacy of communication for connected and autonomous driving. The proposed architecture includes: (1) multi-radio access technology capabilities, with simultaneous 802.11p and LTE-Uu support, enabled by the connectivity infrastructure; (2) a MEC platform, where, among others, algorithms for detecting attacks are implemented; (3) an intelligent On-Board Unit with anti-hacking features inside the vehicle; (4) a Public Key Infrastructure that validates in real-time the integrity of vehicle’s data transmissions. As an indicative application, the interaction between the entities of the CARAMEL architecture is showcased in case of a GPS spoofing attack scenario. Adopted attack detection techniques exploit robust in-vehicle and cooperative approaches that do not rely on encrypted GPS signals, but only on measurements available in the CARAMEL architecture.This work was supported by the European Union’s H2020 research and innovation programme under the CARAMEL
project (Grant agreement No. 833611). The work of Christian Vitale, Christos Laoudias and Georgios Ellinas was also
supported by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 739551 (KIOS
CoE) and from the Republic of Cyprus through the Directorate General for European Programmes, Coordination,
and Development. The work of Jordi Casademont and Pouria Sayyad Khodashenas was also supported by FEDER
and Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya
through projects Fem IoT and SGR 2017-00376 and by the ERDFPeer ReviewedPostprint (author's final draft
GPS Anomaly Detection And Machine Learning Models For Precise Unmanned Aerial Systems
The rapid development and deployment of 5G/6G networks have brought numerous benefits such as faster speeds, enhanced capacity, improved reliability, lower latency, greater network efficiency, and enablement of new applications. Emerging applications of 5G impacting billions of devices and embedded electronics also pose cyber security vulnerabilities. This thesis focuses on the development of Global Positioning Systems (GPS) Based Anomaly Detection and corresponding algorithms for Unmanned Aerial Systems (UAS). Chapter 1 provides an overview of the thesis background and its objectives. Chapter 2 presents an overview of the 5G architectures, their advantages, and potential cyber threat types. Chapter 3 addresses the issue of GPS dropouts by taking the use case of the Dallas-Fort Worth (DFW) airport. By analyzing data from surveillance drones in the (DFW) area, its message frequency, and statistics on time differences between GPS messages were examined. Chapter 4 focuses on modeling and detecting false data injection (FDI) on GPS. Specifically, three scenarios, including Gaussian noise injection, data duplication, data manipulation are modeled. Further, multiple detection schemes that are Clustering-based and reinforcement learning techniques are deployed and detection accuracy were investigated. Chapter 5 shows the results of Chapters 3 and 4. Overall, this research provides a categorization and possible outlier detection to minimize the GPS interference for UAS enhancing the security and reliability of UAS operations
Systematic Review on Security and Privacy Requirements in Edge Computing: State of the Art and Future Research Opportunities
Edge computing is a promising paradigm that enhances the capabilities of cloud computing. In order to continue patronizing the computing services, it is essential to conserve a good atmosphere free from all kinds of security and privacy breaches. The security and privacy issues associated with the edge computing environment have narrowed the overall acceptance of the technology as a reliable paradigm. Many researchers have reviewed security and privacy issues in edge computing, but not all have fully investigated the security and privacy requirements. Security and privacy requirements are the objectives that indicate the capabilities as well as functions a system performs in eliminating certain security and privacy vulnerabilities. The paper aims to substantially review the security and privacy requirements of the edge computing and the various technological methods employed by the techniques used in curbing the threats, with the aim of helping future researchers in identifying research opportunities. This paper investigate the current studies and highlights the following: (1) the classification of security and privacy requirements in edge computing, (2) the state of the art techniques deployed in curbing the security and privacy threats, (3) the trends of technological methods employed by the techniques, (4) the metrics used for evaluating the performance of the techniques, (5) the taxonomy of attacks affecting the edge network, and the corresponding technological trend employed in mitigating the attacks, and, (6) research opportunities for future researchers in the area of edge computing security and privacy
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