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2021-05-25
Segovia, Mariana, Rubio-Hernan, Jose, Cavalli, Ana R., Garcia-Alfaro, Joaquin.  2020.  Cyber-Resilience Evaluation of Cyber-Physical Systems. 2020 IEEE 19th International Symposium on Network Computing and Applications (NCA). :1—8.
Cyber-Physical Systems (CPS) use computational resources to control physical processes and provide critical services. For this reason, an attack in these systems may have dangerous consequences in the physical world. Hence, cyber- resilience is a fundamental property to ensure the safety of the people, the environment and the controlled physical processes. In this paper, we present metrics to quantify the cyber-resilience level based on the design, structure, stability, and performance under the attack of a given CPS. The metrics provide reference points to evaluate whether the system is better prepared or not to face the adversaries. This way, it is possible to quantify the ability to recover from an adversary using its mathematical model based on actuators saturation. Finally, we validate our approach using a numeric simulation on the Tennessee Eastman control challenge problem.
2021-01-28
Nweke, L. O., Weldehawaryat, G. Kahsay, Wolthusen, S. D..  2020.  Adversary Model for Attacks Against IEC 61850 Real-Time Communication Protocols. 2020 16th International Conference on the Design of Reliable Communication Networks DRCN 2020. :1—8.

Adversarial models are well-established for cryptographic protocols, but distributed real-time protocols have requirements that these abstractions are not intended to cover. The IEEE/IEC 61850 standard for communication networks and systems for power utility automation in particular not only requires distributed processing, but in case of the generic object oriented substation events and sampled value (GOOSE/SV) protocols also hard real-time characteristics. This motivates the desire to include both quality of service (QoS) and explicit network topology in an adversary model based on a π-calculus process algebraic formalism based on earlier work. This allows reasoning over process states, placement of adversarial entities and communication behaviour. We demonstrate the use of our model for the simple case of a replay attack against the publish/subscribe GOOSE/SV subprotocol, showing bounds for non-detectability of such an attack.

2020-08-03
Xiong, Chen, Chen, Hua, Cai, Ming, Gao, Jing.  2019.  A Vehicle Trajectory Adversary Model Based on VLPR Data. 2019 5th International Conference on Transportation Information and Safety (ICTIS). :903–912.
Although transport agency has employed desensitization techniques to deal with the privacy information when publicizing vehicle license plate recognition (VLPR) data, the adversaries can still eavesdrop on vehicle trajectories by certain means and further acquire the associated person and vehicle information through background knowledge. In this work, a privacy attacking method by using the desensitized VLPR data is proposed to link the vehicle trajectory. First the road average speed is evaluated by analyzing the changes of traffic flow, which is used to estimate the vehicle's travel time to the next VLPR system. Then the vehicle suspicion list is constructed through the time relevance of neighboring VLPR systems. Finally, since vehicles may have the same features like color, type, etc, the target trajectory will be located by filtering the suspected list by the rule of qualified identifier (QI) attributes and closest time method. Based on the Foshan City's VLPR data, the method is tested and results show that correct vehicle trajectory can be linked, which proves that the current VLPR data publication way has the risk of privacy disclosure. At last, the effects of related parameters on the proposed method are discussed and effective suggestions are made for publicizing VLPR date in the future.
2020-07-16
Mace, J.C., Morisset, C., Pierce, K., Gamble, C., Maple, C., Fitzgerald, J..  2018.  A multi-modelling based approach to assessing the security of smart buildings. Living in the Internet of Things: Cybersecurity of the IoT – 2018. :1—10.

Smart buildings are controlled by multiple cyber-physical systems that provide critical services such as heating, ventilation, lighting and access control. These building systems are becoming increasingly vulnerable to both cyber and physical attacks. We introduce a multi-model methodology for assessing the security of these systems, which utilises INTO-CPS, a suite of modelling, simulation, and analysis tools for designing cyber-physical systems. Using a fan coil unit case study we show how its security can be systematically assessed when subjected to Man-in-the-Middle attacks on the data connections between system components. We suggest our methodology would enable building managers and security engineers to design attack countermeasures and refine their effectiveness.

2018-05-30
Su, C., Santoso, B., Li, Y., Deng, R. H., Huang, X..  2017.  Universally Composable RFID Mutual Authentication. IEEE Transactions on Dependable and Secure Computing. 14:83–94.

Universally Composable (UC) framework provides the strongest security notion for designing fully trusted cryptographic protocols, and it is very challenging on applying UC security in the design of RFID mutual authentication protocols. In this paper, we formulate the necessary conditions for achieving UC secure RFID mutual authentication protocols which can be fully trusted in arbitrary environment, and indicate the inadequacy of some existing schemes under the UC framework. We define the ideal functionality for RFID mutual authentication and propose the first UC secure RFID mutual authentication protocol based on public key encryption and certain trusted third parties which can be modeled as functionalities. We prove the security of our protocol under the strongest adversary model assuming both the tags' and readers' corruptions. We also present two (public) key update protocols for the cases of multiple readers: one uses Message Authentication Code (MAC) and the other uses trusted certificates in Public Key Infrastructure (PKI). Furthermore, we address the relations between our UC framework and the zero-knowledge privacy model proposed by Deng et al. [1].