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Alabadi, Montdher, Albayrak, Zafer.  2020.  Q-Learning for Securing Cyber-Physical Systems : A survey. 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). :1–13.
A cyber-physical system (CPS) is a term that implements mainly three parts, Physical elements, communication networks, and control systems. Currently, CPS includes the Internet of Things (IoT), Internet of Vehicles (IoV), and many other systems. These systems face many security challenges and different types of attacks, such as Jamming, DDoS.CPS attacks tend to be much smarter and more dynamic; thus, it needs defending strategies that can handle this level of intelligence and dynamicity. Last few years, many researchers use machine learning as a base solution to many CPS security issues. This paper provides a survey of the recent works that utilized the Q-Learning algorithm in terms of security enabling and privacy-preserving. Different adoption of Q-Learning for security and defending strategies are studied. The state-of-the-art of Q-learning and CPS systems are classified and analyzed according to their attacks, domain, supported techniques, and details of the Q-Learning algorithm. Finally, this work highlight The future research trends toward efficient utilization of Q-learning and deep Q-learning on CPS security.
Bogosyan, Seta, Gokasan, Metin.  2020.  Novel Strategies for Security-hardened BMS for Extremely Fast Charging of BEVs. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC). :1–7.

The increased power capacity and networking requirements in Extremely Fast Charging (XFC) systems for battery electric vehicles (BEVs) and the resulting increase in the adversarial attack surface call for security measures to be taken in the involved cyber-physical system (CPS). Within this system, the security of the BEV's battery management system (BMS) is of critical importance as the BMS is the first line of defense between the vehicle and the charge station. This study proposes an optimal control and moving-target defense (MTD) based novel approach for the security of the vehicle BMS) focusing on the charging process, during which a compromised vehicle may contaminate the XFC station and the whole grid. This paper is part of our ongoing research, which is one of the few, if not the first, reported studies in the literature on security-hardened BMS, aiming to increase the security and performance of operations between the charging station, the BMS and the battery system of electric vehicles. The developed MTD based switching strategy makes use of redundancies in the controller and feedback design. The performed simulations demonstrate an increased unpredictability and acceptable charging performance under adversarial attacks.

Lyshevski, S. E., Aved, A., Morrone, P..  2020.  Information-Centric Cyberattack Analysis and Spatiotemporal Networks Applied to Cyber-Physical Systems. 2020 IEEE Microwave Theory and Techniques in Wireless Communications (MTTW). 1:172—177.

Cyber-physical systems (CPS) depend on cybersecurity to ensure functionality, data quality, cyberattack resilience, etc. There are known and unknown cyber threats and attacks that pose significant risks. Information assurance and information security are critical. Many systems are vulnerable to intelligence exploitation and cyberattacks. By investigating cybersecurity risks and formal representation of CPS using spatiotemporal dynamic graphs and networks, this paper investigates topics and solutions aimed to examine and empower: (1) Cybersecurity capabilities; (2) Information assurance and system vulnerabilities; (3) Detection of cyber threat and attacks; (4) Situational awareness; etc. We introduce statistically-characterized dynamic graphs, novel entropy-centric algorithms and calculi which promise to ensure near-real-time capabilities.

Venkitasubramaniam, P., Yao, J., Pradhan, P..  2015.  Information-Theoretic Security in Stochastic Control Systems. Proceedings of the IEEE. 103:1914–1931.
Infrastructural systems such as the electricity grid, healthcare, and transportation networks today rely increasingly on the joint functioning of networked information systems and physical components, in short, on cyber-physical architectures. Despite tremendous advances in cryptography, physical-layer security and authentication, information attacks, both passive such as eavesdropping, and active such as unauthorized data injection, continue to thwart the reliable functioning of networked systems. In systems with joint cyber-physical functionality, the ability of an adversary to monitor transmitted information or introduce false information can lead to sensitive user data being leaked or result in critical damages to the underlying physical system. This paper investigates two broad challenges in information security in cyber-physical systems (CPSs): preventing retrieval of internal physical system information through monitored external cyber flows, and limiting the modification of physical system functioning through compromised cyber flows. A rigorous analytical framework grounded on information-theoretic security is developed to study these challenges in a general stochastic control system abstraction-a theoretical building block for CPSs-with the objectives of quantifying the fundamental tradeoffs between information security and physical system performance, and through the process, designing provably secure controller policies. Recent results are presented that establish the theoretical basis for the framework, in addition to practical applications in timing analysis of anonymous systems, and demand response systems in a smart electricity grid.
Tai, J., Alsmadi, I., Zhang, Y., Qiao, F..  2020.  Machine Learning Methods for Anomaly Detection in Industrial Control Systems. 2020 IEEE International Conference on Big Data (Big Data). :2333—2339.

This paper examines multiple machine learning models to find the model that best indicates anomalous activity in an industrial control system that is under a software-based attack. The researched machine learning models are Random Forest, Gradient Boosting Machine, Artificial Neural Network, and Recurrent Neural Network classifiers built-in Python and tested against the HIL-based Augmented ICS dataset. Although the results showed that Random Forest, Gradient Boosting Machine, Artificial Neural Network, and Long Short-Term Memory classification models have great potential for anomaly detection in industrial control systems, we found that Random Forest with tuned hyperparameters slightly outperformed the other models.

Gressl, L., Krisper, M., Steger, C., Neffe, U..  2020.  Towards Security Attack and Risk Assessment during Early System Design. 2020 International Conference on Cyber Security and Protection of Digital Services (Cyber Security). :1—8.

The advent of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) enabled a new class of smart and interactive devices. With their continuous connectivity and their access to valuable information in both the digital and physical world, they are attractive targets for security attackers. Hence, with their integration into both the industry and consumer devices, they added a new surface for cybersecurity attacks. These potential threats call for special care of security vulnerabilities during the design of IoT devices and CPS. The design of secure systems is a complex task, especially if they must adhere to other constraints, such as performance, power consumption, and others. A range of design space exploration tools have been proposed in academics, which aim to support system designers in their task of finding the optimal selection of hardware components and task mappings. Said tools offer a limited way of modeling attack scenarios as constraints for a system under design. The framework proposed in this paper aims at closing this gap, offering system designers a way to consider security attacks and security risks during the early design phase. It offers designers to model security constraints from the view of potential attackers, assessing the probability of successful security attacks and security risk. The framework's feasibility and performance is demonstrated by revisiting a potential system design of an industry partner.

Jin, Z., Yu, P., Guo, S. Y., Feng, L., Zhou, F., Tao, M., Li, W., Qiu, X., Shi, L..  2020.  Cyber-Physical Risk Driven Routing Planning with Deep Reinforcement-Learning in Smart Grid Communication Networks. 2020 International Wireless Communications and Mobile Computing (IWCMC). :1278—1283.
In modern grid systems which is a typical cyber-physical System (CPS), information space and physical space are closely related. Once the communication link is interrupted, it will make a great damage to the power system. If the service path is too concentrated, the risk will be greatly increased. In order to solve this problem, this paper constructs a route planning algorithm that combines node load pressure, link load balance and service delay risk. At present, the existing intelligent algorithms are easy to fall into the local optimal value, so we chooses the deep reinforcement learning algorithm (DRL). Firstly, we build a risk assessment model. The node risk assessment index is established by using the node load pressure, and then the link risk assessment index is established by using the average service communication delay and link balance degree. The route planning problem is then solved by a route planning algorithm based on DRL. Finally, experiments are carried out in a simulation scenario of a power grid system. The results show that our method can find a lower risk path than the original Dijkstra algorithm and the Constraint-Dijkstra algorithm.
Khoury, J., Nassar, M..  2020.  A Hybrid Game Theory and Reinforcement Learning Approach for Cyber-Physical Systems Security. NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium. :1—9.
Cyber-Physical Systems (CPS) are monitored and controlled by Supervisory Control and Data Acquisition (SCADA) systems that use advanced computing, sensors, control systems, and communication networks. At first, CPS and SCADA systems were protected and secured by isolation. However, with recent industrial technology advances, the increased connectivity of CPSs and SCADA systems to enterprise networks has uncovered them to new cybersecurity threats and made them a primary target for cyber-attacks with the potential of causing catastrophic economic, social, and environmental damage. Recent research focuses on new methodologies for risk modeling and assessment using game theory and reinforcement learning. This paperwork proposes to frame CPS security on two different levels, strategic and battlefield, by meeting ideas from game theory and Multi-Agent Reinforcement Learning (MARL). The strategic level is modeled as imperfect information, extensive form game. Here, the human administrator and the malware author decide on the strategies of defense and attack, respectively. At the battlefield level, strategies are implemented by machine learning agents that derive optimal policies for run-time decisions. The outcomes of these policies manifest as the utility at a higher level, where we aim to reach a Nash Equilibrium (NE) in favor of the defender. We simulate the scenario of a virus spreading in the context of a CPS network. We present experiments using the MiniCPS simulator and the OpenAI Gym toolkit and discuss the results.
Niu, L., Ramasubramanian, B., Clark, A., Bushnell, L., Poovendran, R..  2020.  Control Synthesis for Cyber-Physical Systems to Satisfy Metric Interval Temporal Logic Objectives under Timing and Actuator Attacks*. 2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS). :162–173.
This paper studies the synthesis of controllers for cyber-physical systems (CPSs) that are required to carry out complex tasks that are time-sensitive, in the presence of an adversary. The task is specified as a formula in metric interval temporal logic (MITL). The adversary is assumed to have the ability to tamper with the control input to the CPS and also manipulate timing information perceived by the CPS. In order to model the interaction between the CPS and the adversary, and also the effect of these two classes of attacks, we define an entity called a durational stochastic game (DSG). DSGs probabilistically capture transitions between states in the environment, and also the time taken for these transitions. With the policy of the defender represented as a finite state controller (FSC), we present a value-iteration based algorithm that computes an FSC that maximizes the probability of satisfying the MITL specification under the two classes of attacks. A numerical case-study on a signalized traffic network is presented to illustrate our results.
Staffa, M., Mazzeo, G., Sgaglione, L..  2018.  Hardening ROS via Hardware-assisted Trusted Execution Environment. 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). :491—494.

In recent years, humanoid robots have become quite ubiquitous finding wide applicability in many different fields, spanning from education to entertainment and assistance. They can be considered as more complex cyber-physical systems (CPS) and, as such, they are exposed to the same vulnerabilities. This can be very dangerous for people acting that close with these robots, since attackers by exploiting their vulnerabilities, can not only violate people's privacy, but, more importantly, they can command the robot behavior causing them bodily harm, thus leading to devastating consequences. In this paper, we propose a solution not yet investigated in this field, which relies on the use of secure enclaves, which in our opinion could represent a valuable solution for coping with most of the possible attacks, while suggesting developers to adopt such a precaution during the robot design phase.

Han, Y., Zhang, W., Wei, J., Liu, X., Ye, S..  2019.  The Study and Application of Security Control Plan Incorporating Frequency Stability (SCPIFS) in CPS-Featured Interconnected Asynchronous Grids. 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia). :349—354.

The CPS-featured modern asynchronous grids interconnected with HVDC tie-lines facing the hazards from bulk power imbalance shock. With the aid of cyber layer, the SCPIFS incorporates the frequency stability constrains is put forwarded. When there is bulk power imbalance caused by HVDC tie-lines block incident or unplanned loads increasing, the proposed SCPIFS ensures the safety and frequency stability of both grids at two terminals of the HVDC tie-line, also keeps the grids operate economically. To keep frequency stability, the controllable variables in security control strategy include loads, generators outputs and the power transferred in HVDC tie-lines. McCormick envelope method and ADMM are introduced to solve the proposed SCPIFS optimization model. Case studies of two-area benchmark system verify the safety and economical benefits of the SCPFS. HVDC tie-line transferred power can take the advantage of low cost generator resource of both sides utmost and avoid the load shedding via tuning the power transferred through the operating tie-lines, thus the operation of both connected asynchronous grids is within the limit of frequency stability domain.

Pearce, Hammond, Pinisetty, Srinivas, Roop, Partha S., Kuo, Matthew M. Y., Ukil, Abhisek.  2020.  Smart I/O Modules for Mitigating Cyber-Physical Attacks on Industrial Control Systems. IEEE Transactions on Industrial Informatics. 16:4659—4669.

Cyber-physical systems (CPSs) are implemented in many industrial and embedded control applications. Where these systems are safety-critical, correct and safe behavior is of paramount importance. Malicious attacks on such CPSs can have far-reaching repercussions. For instance, if elements of a power grid behave erratically, physical damage and loss of life could occur. Currently, there is a trend toward increased complexity and connectivity of CPS. However, as this occurs, the potential attack vectors for these systems grow in number, increasing the risk that a given controller might become compromised. In this article, we examine how the dangers of compromised controllers can be mitigated. We propose a novel application of runtime enforcement that can secure the safety of real-world physical systems. Here, we synthesize enforcers to a new hardware architecture within programmable logic controller I/O modules to act as an effective line of defence between the cyber and the physical domains. Our enforcers prevent the physical damage that a compromised control system might be able to perform. To demonstrate the efficacy of our approach, we present several benchmarks, and show that the overhead for each system is extremely minimal.

Yousefzadeh, Saba, Basharkhah, Katayoon, Nosrati, Nooshin, Sadeghi, Rezgar, Raik, Jaan, Jenihhin, Maksim, Navabi, Zainalabedin.  2019.  An Accelerator-based Architecture Utilizing an Efficient Memory Link for Modern Computational Requirements. 2019 IEEE East-West Design Test Symposium (EWDTS). :1—6.

Hardware implementation of many of today's applications such as those in automotive, telecommunication, bio, and security, require heavy repeated computations, and concurrency in the execution of these computations. These requirements are not easily satisfied by existing embedded systems. This paper proposes an embedded system architecture that is enhanced by an array of accelerators, and a bussing system that enables concurrency in operation of accelerators. This architecture is statically configurable to configure it for performing a specific application. The embedded system architecture and architecture of the configurable accelerators are discussed in this paper. A case study examines an automotive application running on our proposed system.

Gupta, Priyanka, Garg, Gagan.  2019.  Handling concurrent requests in a secret sharing based storage system using Petri Nets. 2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS). :1—6.

Data can be stored securely in various storage servers. But in the case of a server failure, or data theft from a certain number of servers, the remaining data becomes inadequate for use. Data is stored securely using secret sharing schemes, so that data can be reconstructed even if some of the servers fail. But not much work has been carried out in the direction of updation of this data. This leads to the problem of updation when two or more concurrent requests arrive and thus, it results in inconsistency. Our work proposes a novel method to store data securely with concurrent update requests using Petri Nets, under the assumption that the number of nodes is very large and the requests for updates are very frequent.

Ibrahim, Romani Farid.  2019.  Mobile Transaction Processing for a Distributed War Environment. 2019 14th International Conference on Computer Science Education (ICCSE). :856—862.

The battlefield environment differs from the natural environment in terms of irregular communications and the possibility of destroying communication and medical units by enemy forces. Information that can be collected in a war environment by soldiers is important information and must reach top-level commanders in time for timely decisions making. Also, ambulance staff in the battlefield need to enter the data of injured soldiers after the first aid, so that the information is available for the field hospital staff to prepare the needs for incoming injured soldiers.In this research, we propose two transaction techniques to handle these issues and use different concurrency control protocols, depending on the nature of the transaction and not a one concurrency control protocol for all types of transactions. Message transaction technique is used to collect valuable data from the battlefield by soldiers and allows top-level commanders to view it according to their permissions by logging into the system, to help them make timely decisions. In addition, use the capabilities of DBMS tools to organize data and generate reports, as well as for future analysis. Medical service unit transactional workflow technique is used to provides medical information to the medical authorities about the injured soldiers and their status, which helps them to prepare the required needs before the wounded soldiers arrive at the hospitals. Both techniques handle the disconnection problem during transaction processing.In our approach, the transaction consists of four phases, reading, editing, validation, and writing phases, and its processing is based on the optimistic concurrency control protocol, and the rules of actionability that describe how a transaction behaves if a value-change is occurred on one or more of its attributes during its processing time by other transactions.

André, Étienne, Lime, Didier, Ramparison, Mathias, Stoelinga, Mariëlle.  2019.  Parametric Analyses of Attack-Fault Trees. 2019 19th International Conference on Application of Concurrency to System Design (ACSD). :33—42.

Risk assessment of cyber-physical systems, such as power plants, connected devices and IT-infrastructures has always been challenging: safety (i.e., absence of unintentional failures) and security (i. e., no disruptions due to attackers) are conditions that must be guaranteed. One of the traditional tools used to help considering these problems is attack trees, a tree-based formalism inspired by fault trees, a well-known formalism used in safety engineering. In this paper we define and implement the translation of attack-fault trees (AFTs) to a new extension of timed automata, called parametric weighted timed automata. This allows us to parametrize constants such as time and discrete costs in an AFT and then, using the model-checker IMITATOR, to compute the set of parameter values such that a successful attack is possible. Using the different sets of parameter values computed, different attack and fault scenarios can be deduced depending on the budget, time or computation power of the attacker, providing helpful data to select the most efficient counter-measure.

Meng, Ruijie, Zhu, Biyun, Yun, Hao, Li, Haicheng, Cai, Yan, Yang, Zijiang.  2019.  CONVUL: An Effective Tool for Detecting Concurrency Vulnerabilities. 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE). :1154—1157.

Concurrency vulnerabilities are extremely harmful and can be frequently exploited to launch severe attacks. Due to the non-determinism of multithreaded executions, it is very difficult to detect them. Recently, data race detectors and techniques based on maximal casual model have been applied to detect concurrency vulnerabilities. However, the former are ineffective and the latter report many false negatives. In this paper, we present CONVUL, an effective tool for concurrency vulnerability detection. CONVUL is based on exchangeable events, and adopts novel algorithms to detect three major kinds of concurrency vulnerabilities. In our experiments, CONVUL detected 9 of 10 known vulnerabilities, while other tools only detected at most 2 out of these 10 vulnerabilities. The 10 vulnerabilities are available at

Ravikumar, Gelli, Hyder, Burhan, Govindarasu, Manimaran.  2019.  Efficient Modeling of HIL Multi-Grid System for Scalability Concurrency in CPS Security Testbed. 2019 North American Power Symposium (NAPS). :1—6.
Cyber-event-triggered power grid blackout compels utility operators to intensify cyber-aware and physics-constrained recovery and restoration process. Recently, coordinated cyber attacks on the Ukrainian grid witnessed such a cyber-event-triggered power system blackout. Various cyber-physical system (CPS) testbeds have attempted with multitude designs to analyze such interdependent events and evaluate remedy measures. However, resource constraints and modular integration designs have been significant barriers while modeling large-scale grid models (scalability) and multi-grid isolated models (concurrency) under a single real-time execution environment for the hardware-in-the-loop (HIL) CPS security testbeds. This paper proposes a meticulous design and effective modeling for simulating large-scale grid models and multi-grid isolated models in a HIL realtime digital simulator environment integrated with industry-grade hardware and software systems. We have used our existing HIL CPS security testbed to demonstrate scalability by the realtime performance of a Texas-2000 bus US synthetic grid model and concurrency by the real-time performance of simultaneous ten IEEE-39 bus grid models and an IEEE-118 bus grid model. The experiments demonstrated significant results by 100% realtime performance with zero overruns, low latency while receiving and executing control signals from SEL Relays via IEC-61850 protocol and low latency while computing and transmitting grid data streams including stability measures via IEEE C37.118 synchrophasor data protocol to SEL Phasor Data Concentrators.
Zaman, Tarannum Shaila, Han, Xue, Yu, Tingting.  2019.  SCMiner: Localizing System-Level Concurrency Faults from Large System Call Traces. 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE). :515—526.

Localizing concurrency faults that occur in production is hard because, (1) detailed field data, such as user input, file content and interleaving schedule, may not be available to developers to reproduce the failure; (2) it is often impractical to assume the availability of multiple failing executions to localize the faults using existing techniques; (3) it is challenging to search for buggy locations in an application given limited runtime data; and, (4) concurrency failures at the system level often involve multiple processes or event handlers (e.g., software signals), which can not be handled by existing tools for diagnosing intra-process(thread-level) failures. To address these problems, we present SCMiner, a practical online bug diagnosis tool to help developers understand how a system-level concurrency fault happens based on the logs collected by the default system audit tools. SCMiner achieves online bug diagnosis to obviate the need for offline bug reproduction. SCMiner does not require code instrumentation on the production system or rely on the assumption of the availability of multiple failing executions. Specifically, after the system call traces are collected, SCMiner uses data mining and statistical anomaly detection techniques to identify the failure-inducing system call sequences. It then maps each abnormal sequence to specific application functions. We have conducted an empirical study on 19 real-world benchmarks. The results show that SCMiner is both effective and efficient at localizing system-level concurrency faults.

Li, Yue.  2019.  Finding Concurrency Exploits on Smart Contracts. 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion). :144—146.

Smart contracts have been widely used on Ethereum to enable business services across various application domains. However, they are prone to different forms of security attacks due to the dynamic and non-deterministic blockchain runtime environment. In this work, we highlighted a general miner-side type of exploit, called concurrency exploit, which attacks smart contracts via generating malicious transaction sequences. Moreover, we designed a systematic algorithm to automatically detect such exploits. In our preliminary evaluation, our approach managed to identify real vulnerabilities that cannot be detected by other tools in the literature.

Madhan, E.S., Ghosh, Uttam, Tosh, Deepak K., Mandal, K., Murali, E., Ghosh, Soumalya.  2019.  An Improved Communications in Cyber Physical System Architecture, Protocols and Applications. 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON). :1–6.
In recent trends, Cyber-Physical Systems (CPS) and Internet of Things interpret an evolution of computerized integration connectivity. The specific research challenges in CPS as security, privacy, data analytics, participate sensing, smart decision making. In addition, The challenges in Wireless Sensor Network (WSN) includes secure architecture, energy efficient protocols and quality of services. In this paper, we present an architectures of CPS and its protocols and applications. We propose software related mobile sensing paradigm namely Mobile Sensor Information Agent (MSIA). It works as plug-in based for CPS middleware and scalable applications in mobile devices. The working principle MSIA is acts intermediary device and gathers data from a various external sensors and its upload to cloud on demand. CPS needs tight integration between cyber world and man-made physical world to achieve stability, security, reliability, robustness, and efficiency in the system. Emerging software-defined networking (SDN) can be integrated as the communication infrastructure with CPS infrastructure to accomplish such system. Thus we propose a possible SDN-based CPS framework to improve the performance of the system.
Butun, Ismail, Österberg, Patrik, Gidlund, Mikael.  2019.  Preserving Location Privacy in Cyber-Physical Systems. 2019 IEEE Conference on Communications and Network Security (CNS). :1–6.
The trending technological research platform is Internet of Things (IoT)and most probably it will stay that way for a while. One of the main application areas of IoT is Cyber-Physical Systems (CPSs), in which IoT devices can be leveraged as actuators and sensors in accordance with the system needs. The public acceptance and adoption of CPS services and applications will create a huge amount of privacy issues related to the processing, storage and disclosure of the user location information. As a remedy, our paper proposes a methodology to provide location privacy for the users of CPSs. Our proposal takes advantage of concepts such as mix-zone, context-awareness, and location-obfuscation. According to our best knowledge, the proposed methodology is the first privacy-preserving location service for CPSs that offers adaptable privacy levels related to the current context of the user.
Chen, Yuqi, Poskitt, Christopher M., Sun, Jun.  2018.  Learning from Mutants: Using Code Mutation to Learn and Monitor Invariants of a Cyber-Physical System. 2018 IEEE Symposium on Security and Privacy (SP). :648–660.
Cyber-physical systems (CPS) consist of sensors, actuators, and controllers all communicating over a network; if any subset becomes compromised, an attacker could cause significant damage. With access to data logs and a model of the CPS, the physical effects of an attack could potentially be detected before any damage is done. Manually building a model that is accurate enough in practice, however, is extremely difficult. In this paper, we propose a novel approach for constructing models of CPS automatically, by applying supervised machine learning to data traces obtained after systematically seeding their software components with faults ("mutants"). We demonstrate the efficacy of this approach on the simulator of a real-world water purification plant, presenting a framework that automatically generates mutants, collects data traces, and learns an SVM-based model. Using cross-validation and statistical model checking, we show that the learnt model characterises an invariant physical property of the system. Furthermore, we demonstrate the usefulness of the invariant by subjecting the system to 55 network and code-modification attacks, and showing that it can detect 85% of them from the data logs generated at runtime.
Li, Kai, Kurunathan, Harrison, Severino, Ricardo, Tovar, Eduardo.  2018.  Cooperative Key Generation for Data Dissemination in Cyber-Physical Systems. 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS). :331–332.
Securing wireless communication is significant for privacy and confidentiality of sensing data in Cyber-Physical Systems (CPS). However, due to broadcast nature of radio channels, disseminating sensory data is vulnerable to eavesdropping and message modification. Generating secret keys by extracting the shared randomness in a wireless fading channel is a promising way to improve the communication security. In this poster, we present a novel secret key generation protocol for securing real-time data dissemination in CPS, where the sensor nodes cooperatively generate a shared key by estimating the quantized fading channel randomness. A 2-hop wireless sensor network testbed is built and preliminary experimental results show that the quantization intervals and distance between the nodes lead to a secret bit mismatch.
Sliwa, Benjamin, Haferkamp, Marcus, Al-Askary, Manar, Dorn, Dennis, Wietfeld, Christian.  2018.  A radio-fingerprinting-based vehicle classification system for intelligent traffic control in smart cities. 2018 Annual IEEE International Systems Conference (SysCon). :1–5.
The measurement and provision of precise and up-to-date traffic-related key performance indicators is a key element and crucial factor for intelligent traffic control systems in upcoming smart cities. The street network is considered as a highly-dynamic Cyber Physical System (CPS) where measured information forms the foundation for dynamic control methods aiming to optimize the overall system state. Apart from global system parameters like traffic flow and density, specific data, such as velocity of individual vehicles as well as vehicle type information, can be leveraged for highly sophisticated traffic control methods like dynamic type-specific lane assignments. Consequently, solutions for acquiring these kinds of information are required and have to comply with strict requirements ranging from accuracy over cost-efficiency to privacy preservation. In this paper, we present a system for classifying vehicles based on their radio-fingerprint. In contrast to other approaches, the proposed system is able to provide real-time capable and precise vehicle classification as well as cost-efficient installation and maintenance, privacy preservation and weather independence. The system performance in terms of accuracy and resource-efficiency is evaluated in the field using comprehensive measurements. Using a machine learning based approach, the resulting success ratio for classifying cars and trucks is above 99%.