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MobileTrust: Secure Knowledge Integration in VANETs
Vehicular Ad hoc NETworks (VANET) are becoming popular due to the emergence of the Internet of Things and ambient intelligence applications. In such networks, secure resource sharing functionality is accomplished by incorporating trust schemes. Current solutions adopt peer-to-peer technologies that can cover the large operational area. However, these systems fail to capture some inherent properties of VANETs, such as fast and ephemeral interaction, making robust trust evaluation of crowdsourcing challenging. In this article, we propose MobileTrust—a hybrid trust-based system for secure resource sharing in VANETs. The proposal is a breakthrough in centralized trust computing that utilizes cloud and upcoming 5G technologies to provide robust trust establishment with global scalability. The ad hoc communication is energy-efficient and protects the system against threats that are not countered by the current settings. To evaluate its performance and effectiveness, MobileTrust is modelled in the SUMO simulator and tested on the traffic features of the small-size German city of Eichstatt. Similar schemes are implemented in the same platform to provide a fair comparison. Moreover, MobileTrust is deployed on a typical embedded system platform and applied on a real smart car installation for monitoring traffic and road-state parameters of an urban application. The proposed system is developed under the EU-founded THREAT-ARREST project, to provide security, privacy, and trust in an intelligent and energy-aware transportation scenario, bringing closer the vision of sustainable circular economy
Game Theory Meets Network Security: A Tutorial at ACM CCS
The increasingly pervasive connectivity of today's information systems brings
up new challenges to security. Traditional security has accomplished a long way
toward protecting well-defined goals such as confidentiality, integrity,
availability, and authenticity. However, with the growing sophistication of the
attacks and the complexity of the system, the protection using traditional
methods could be cost-prohibitive. A new perspective and a new theoretical
foundation are needed to understand security from a strategic and
decision-making perspective. Game theory provides a natural framework to
capture the adversarial and defensive interactions between an attacker and a
defender. It provides a quantitative assessment of security, prediction of
security outcomes, and a mechanism design tool that can enable
security-by-design and reverse the attacker's advantage. This tutorial provides
an overview of diverse methodologies from game theory that includes games of
incomplete information, dynamic games, mechanism design theory to offer a
modern theoretic underpinning of a science of cybersecurity. The tutorial will
also discuss open problems and research challenges that the CCS community can
address and contribute with an objective to build a multidisciplinary bridge
between cybersecurity, economics, game and decision theory
Modeling and Detecting False Data Injection Attacks against Railway Traction Power Systems
Modern urban railways extensively use computerized sensing and control
technologies to achieve safe, reliable, and well-timed operations. However, the
use of these technologies may provide a convenient leverage to cyber-attackers
who have bypassed the air gaps and aim at causing safety incidents and service
disruptions. In this paper, we study false data injection (FDI) attacks against
railways' traction power systems (TPSes). Specifically, we analyze two types of
FDI attacks on the train-borne voltage, current, and position sensor
measurements - which we call efficiency attack and safety attack -- that (i)
maximize the system's total power consumption and (ii) mislead trains' local
voltages to exceed given safety-critical thresholds, respectively. To
counteract, we develop a global attack detection (GAD) system that serializes a
bad data detector and a novel secondary attack detector designed based on
unique TPS characteristics. With intact position data of trains, our detection
system can effectively detect the FDI attacks on trains' voltage and current
measurements even if the attacker has full and accurate knowledge of the TPS,
attack detection, and real-time system state. In particular, the GAD system
features an adaptive mechanism that ensures low false positive and negative
rates in detecting the attacks under noisy system measurements. Extensive
simulations driven by realistic running profiles of trains verify that a TPS
setup is vulnerable to the FDI attacks, but these attacks can be detected
effectively by the proposed GAD while ensuring a low false positive rate.Comment: IEEE/IFIP DSN-2016 and ACM Trans. on Cyber-Physical System
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