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Rüth, Jan, Zimmermann, Torsten, Wolsing, Konrad, Hohlfeld, Oliver.  2018.  Digging into Browser-Based Crypto Mining. Proceedings of the Internet Measurement Conference 2018. :70–76.

Mining is the foundation of blockchain-based cryptocurrencies such as Bitcoin rewarding the miner for finding blocks for new transactions. The Monero currency enables mining with standard hardware in contrast to special hardware (ASICs) as often used in Bitcoin, paving the way for in-browser mining as a new revenue model for website operators. In this work, we study the prevalence of this new phenomenon. We identify and classify mining websites in 138M domains and present a new fingerprinting method which finds up to a factor of 5.7 more miners than publicly available block lists. Our work identifies and dissects Coinhive as the major browser-mining stakeholder. Further, we present a new method to associate mined blocks in the Monero blockchain to mining pools and uncover that Coinhive currently contributes 1.18% of mined blocks having turned over 1293 Moneros in June 2018.

Rutard, F., Sigaud, O., Chetouani, M..  2020.  TIRL: Enriching Actor-Critic RL with non-expert human teachers and a Trust Model. 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). :604–611.
Reinforcement learning (RL) algorithms have been demonstrated to be very attractive tools to train agents to achieve sequential tasks. However, these algorithms require too many training data to converge to be efficiently applied to physical robots. By using a human teacher, the learning process can be made faster and more robust, but the overall performance heavily depends on the quality and availability of teacher demonstrations or instructions. In particular, when these teaching signals are inadequate, the agent may fail to learn an optimal policy. In this paper, we introduce a trust-based interactive task learning approach. We propose an RL architecture able to learn both from environment rewards and from various sparse teaching signals provided by non-expert teachers, using an actor-critic agent, a human model and a trust model. We evaluate the performance of this architecture on 4 different setups using a maze environment with different simulated teachers and show that the benefits of the trust model.
Rustgi, Pulkit, Fung, Carol.  2019.  Demo: DroidNet - An Android Permission Control Recommendation System Based on Crowdsourcing. 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). :737–738.
Mobile and web application security, particularly the areas of data privacy, has raised much concerns from the public in recent years. Most applications, or apps for short, are installed without disclosing full information to users and clearly stating what the application has access to, which often raises concern when users become aware of unnecessary information being collected. Unfortunately, most users have little to no technical expertise in regards to what permissions should be turned on and can only rely on their intuition and past experiences to make relatively uninformed decisions. To solve this problem, we developed DroidNet, which is a crowd-sourced Android recommendation tool and framework. DroidNet alleviates privacy concerns and presents users with high confidence permission control recommendations based on the decision from expert users who are using the same apps. This paper explains the general framework, principles, and model behind DroidNet while also providing an experimental setup design which shows the effectiveness and necessity for such a tool.
Rustagi, Taru, Yoo, Kyungjin.  2018.  AR Navigation Solution Using Vector Tiles. Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology. :71:1-71:2.

This study discusses the results and findings of an augmented reality navigation app that was created using vector data uploaded to an online mapping software for indoor navigation. The main objective of this research is to determine the current issues with a solution of indoor navigation that relies on the use of GPS signals, as these signals are sparse in buildings. The data was uploaded in the form of GeoJSON files to MapBox which relayed the data to the app using an API in the form of Tilesets. The application converted the tilesets to a miniaturized map and calculated the navigation path, and then overlaid that navigation line onto the floor via the camera. Once the project setup was completed, multiple navigation paths have been tested numerous times between the different sync points and destination rooms. At the end, their accuracy, ease of access and several other factors, along with their issues, were recorded. The testing revealed that the navigation system was not only accurate despite the lack of GPS signal, but it also detected the device motion precisely. Furthermore, the navigation system did not take much time to generate the navigation path, as the app processed the data tile by tile. The application was also able to accurately measure the ground plane along with the walls, perfectly overlaying the navigation line. However, a few observations indicated various factors affected the accuracy of the navigation, and testing revealed areas where major improvements can be made to improve both accuracy and ease of access.

Russu, Paolo, Demontis, Ambra, Biggio, Battista, Fumera, Giorgio, Roli, Fabio.  2016.  Secure Kernel Machines against Evasion Attacks. Proceeding AISec '16 Proceedings of the 2016 ACM Workshop on Artificial Intelligence and Security Pages 59-69 .

Machine learning is widely used in security-sensitive settings like spam and malware detection, although it has been shown that malicious data can be carefully modified at test time to evade detection. To overcome this limitation, adversary-aware learning algorithms have been developed, exploiting robust optimization and game-theoretical models to incorporate knowledge of potential adversarial data manipulations into the learning algorithm. Despite these techniques have been shown to be effective in some adversarial learning tasks, their adoption in practice is hindered by different factors, including the difficulty of meeting specific theoretical requirements, the complexity of implementation, and scalability issues, in terms of computational time and space required during training. In this work, we aim to develop secure kernel machines against evasion attacks that are not computationally more demanding than their non-secure counterparts. In particular, leveraging recent work on robustness and regularization, we show that the security of a linear classifier can be drastically improved by selecting a proper regularizer, depending on the kind of evasion attack, as well as unbalancing the cost of classification errors. We then discuss the security of nonlinear kernel machines, and show that a proper choice of the kernel function is crucial. We also show that unbalancing the cost of classification errors and varying some kernel parameters can further improve classifier security, yielding decision functions that better enclose the legitimate data. Our results on spam and PDF malware detection corroborate our analysis.

Russu, Paolo, Demontis, Ambra, Biggio, Battista, Fumera, Giorgio, Roli, Fabio.  2016.  Secure Kernel Machines Against Evasion Attacks. Proceedings of the 2016 ACM Workshop on Artificial Intelligence and Security. :59–69.

Machine learning is widely used in security-sensitive settings like spam and malware detection, although it has been shown that malicious data can be carefully modified at test time to evade detection. To overcome this limitation, adversary-aware learning algorithms have been developed, exploiting robust optimization and game-theoretical models to incorporate knowledge of potential adversarial data manipulations into the learning algorithm. Despite these techniques have been shown to be effective in some adversarial learning tasks, their adoption in practice is hindered by different factors, including the difficulty of meeting specific theoretical requirements, the complexity of implementation, and scalability issues, in terms of computational time and space required during training. In this work, we aim to develop secure kernel machines against evasion attacks that are not computationally more demanding than their non-secure counterparts. In particular, leveraging recent work on robustness and regularization, we show that the security of a linear classifier can be drastically improved by selecting a proper regularizer, depending on the kind of evasion attack, as well as unbalancing the cost of classification errors. We then discuss the security of nonlinear kernel machines, and show that a proper choice of the kernel function is crucial. We also show that unbalancing the cost of classification errors and varying some kernel parameters can further improve classifier security, yielding decision functions that better enclose the legitimate data. Our results on spam and PDF malware detection corroborate our analysis.

Russell, S., Abdelzaher, T., Suri, N..  2019.  Multi-Domain Effects and the Internet of Battlefield Things. MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM). :724—730.

This paper reviews the definitions and characteristics of military effects, the Internet of Battlefield Things (IoBT), and their impact on decision processes in a Multi-Domain Operating environment (MDO). The aspects of contemporary military decision-processes are illustrated and an MDO Effect Loop decision process is introduced. We examine the concept of IoBT effects and their implications in MDO. These implications suggest that when considering the concept of MDO, as a doctrine, the technological advances of IoBTs empower enhancements in decision frameworks and increase the viability of novel operational approaches and options for military effects.

Russell, Alexander, Tang, Qiang, Yung, Moti, Zhou, Hong-Sheng.  2017.  Generic Semantic Security Against a Kleptographic Adversary. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. :907–922.

Notable recent security incidents have generated intense interest in adversaries which attempt to subvert–-perhaps covertly–-crypto$\backslash$-graphic algorithms. In this paper we develop (IND-CPA) Semantically Secure encryption in this challenging setting. This fundamental encryption primitive has been previously studied in the "kleptographic setting," though existing results must relax the model by introducing trusted components or otherwise constraining the subversion power of the adversary: designing a Public Key System that is kletographically semantically secure (with minimal trust) has remained elusive to date. In this work, we finally achieve such systems, even when all relevant cryptographic algorithms are subject to adversarial (kleptographic) subversion. To this end we exploit novel inter-component randomized cryptographic checking techniques (with an offline checking component), combined with common and simple software engineering modular programming techniques (applied to the system's black box specification level). Moreover, our methodology yields a strong generic technique for the preservation of any semantically secure cryptosystem when incorporated into the strong kleptographic adversary setting.

Rüsch, Signe, Schürmann, Dominik, Kapitza, Rüdiger, Wolf, Lars.  2017.  Forward Secure Delay-Tolerant Networking. Proceedings of the 12th Workshop on Challenged Networks. :7–12.

Delay-Tolerant Networks exhibit highly asynchronous connections often routed over many mobile hops before reaching its intended destination. The Bundle Security Protocol has been standardized providing properties such as authenticity, integrity, and confidentiality of bundles using traditional Public-Key Cryptography. Other protocols based on Identity-Based Cryptography have been proposed to reduce the key distribution overhead. However, in both schemes, secret keys are usually valid for several months. Thus, a secret key extracted from a compromised node allows for decryption of past communications since its creation. We solve this problem and propose the first forward secure protocol for Delay-Tolerant Networking. For this, we apply the Puncturable Encryption construction designed by Green and Miers, integrate it into the Bundle Security Protocol and adapt its parameters for different highly asynchronous scenarios. Finally, we provide performance measurements and discuss their impact.

Rupasinghe, R. A. A., Padmasiri, D. A., Senanayake, S. G. M. P., Godaliyadda, G. M. R. I., Ekanayake, M. P. B., Wijayakulasooriya, J. V..  2017.  Dynamic clustering for event detection and anomaly identification in video surveillance. 2017 IEEE International Conference on Industrial and Information Systems (ICIIS). :1–6.

This work introduces concepts and algorithms along with a case study validating them, to enhance the event detection, pattern recognition and anomaly identification results in real life video surveillance. The motivation for the work underlies in the observation that human behavioral patterns in general continuously evolve and adapt with time, rather than being static. First, limitations in existing work with respect to this phenomena are identified. Accordingly, the notion and algorithms of Dynamic Clustering are introduced in order to overcome these drawbacks. Correspondingly, we propose the concept of maintaining two separate sets of data in parallel, namely the Normal Plane and the Anomaly Plane, to successfully achieve the task of learning continuously. The practicability of the proposed algorithms in a real life scenario is demonstrated through a case study. From the analysis presented in this work, it is evident that a more comprehensive analysis, closely following human perception can be accomplished by incorporating the proposed notions and algorithms in a video surveillance event.

Ruohonen, Jukka, Šćepanović, Sanja, Hyrynsalmi, Sami, Mishkovski, Igor, Aura, Tuomas, Leppänen, Ville.  2016.  Correlating File-based Malware Graphs Against the Empirical Ground Truth of DNS Graphs. Proccedings of the 10th European Conference on Software Architecture Workshops. :30:1–30:6.

This exploratory empirical paper investigates whether the sharing of unique malware files between domains is empirically associated with the sharing of Internet Protocol (IP) addresses and the sharing of normal, non-malware files. By utilizing a graph theoretical approach with a web crawling dataset from F-Secure, the paper finds no robust statistical associations, however. Unlike what might be expected from the still continuing popularity of shared hosting services, the sharing of IP addresses through the domain name system (DNS) seems to neither increase nor decrease the sharing of malware files. In addition to these exploratory empirical results, the paper contributes to the field of DNS mining by elaborating graph theoretical representations that are applicable for analyzing different network forensics problems.

Ruohonen, Jukka, Leppänen, Ville.  2016.  On the Design of a Simple Network Resolver for DNS Mining. Proceedings of the 17th International Conference on Computer Systems and Technologies 2016. :105–112.

The domain name system (DNS) offers an ideal distributed database for big data mining related to different cyber security questions. Besides infrastructural problems, scalability issues, and security challenges related to the protocol itself, information from DNS is often required also for more nuanced cyber security questions. Against this backdrop, this paper discusses the fundamental characteristics of DNS in relation to cyber security and different research prototypes designed for passive but continuous DNS-based monitoring of domains and addresses. With this discussion, the paper also illustrates a few general software design aspects.

Rungger, Matthias, Zamani, Majid.  2018.  Compositional Construction of Approximate Abstractions of Interconnected Control Systems. IEEE Transactions on Control of Network Systems. 5:116—127.

We consider a compositional construction of approximate abstractions of interconnected control systems. In our framework, an abstraction acts as a substitute in the controller design process and is itself a continuous control system. The abstraction is related to the concrete control system via a so-called simulation function: a Lyapunov-like function, which is used to establish a quantitative bound between the behavior of the approximate abstraction and the concrete system. In the first part of the paper, we provide a small gain type condition that facilitates the compositional construction of an abstraction of an interconnected control system together with a simulation function from the abstractions and simulation functions of the individual subsystems. In the second part of the paper, we restrict our attention to linear control system and characterize simulation functions in terms of controlled invariant, externally stabilizable subspaces. Based on those characterizations, we propose a particular scheme to construct abstractions for linear control systems. We illustrate the compositional construction of an abstraction on an interconnected system consisting of four linear subsystems. We use the abstraction as a substitute to synthesize a controller to enforce a certain linear temporal logic specification.

Runge, Isabel Madeleine, Kolla, Reiner.  2017.  MCGC: A Network Coding Approach for Reliable Large-Scale Wireless Networks. Proceedings of the First ACM International Workshop on the Engineering of Reliable, Robust, and Secure Embedded Wireless Sensing Systems. :16–23.

The application of mobile Wireless Sensor Networks (WSNs) with a big amount of participants poses many challenges. For instance, high transmission loss rates which are caused i.a. by collisions might occur. Additionally, WSNs frequently operate under harsh conditions, where a high probability of link or node failures is inherently given. This leads to reliable data maintenance being a key issue. Existing approaches which were developed to keep data dependably in WSNs often either perform well in highly dynamic or in completely static scenarios, or require complex calculations. Herein, we present the Network Coding based Multicast Growth Codes (MCGC), which represent a solution for reliable data maintenance in large-scale WSNs. MCGC are able to tolerate high fault rates and reconstruct more originally collected data in a shorter period of time than compared existing approaches. Simulation results show performance improvements of up to 75% in comparison to Growth Codes (GC). These results are achieved independently of the systems' dynamics and despite of high fault probabilities.

Rumez, Marcel, Dürrwang, Jürgen, Brecht, Tim, Steinshorn, Timo, Neugebauer, Peter, Kriesten, Reiner, Sax, Eric.  2019.  CAN Radar: Sensing Physical Devices in CAN Networks based on Time Domain Reflectometry. 2019 IEEE Vehicular Networking Conference (VNC). :1–8.
The presence of security vulnerabilities in automotive networks has already been shown by various publications in recent years. Due to the specification of the Controller Area Network (CAN) as a broadcast medium without security mechanisms, attackers are able to read transmitted messages without being noticed and to inject malicious messages. In order to detect potential attackers within a network or software system as early as possible, Intrusion Detection Systems (IDSs) are prevalent. Many approaches for vehicles are based on techniques which are able to detect deviations from specified CAN network behaviour regarding protocol or payload properties. However, it is challenging to detect attackers who secretly connect to CAN networks and do not actively participate in bus traffic. In this paper, we present an approach that is capable of successfully detecting unknown CAN devices and determining the distance (cable length) between the attacker device and our sensing unit based on Time Domain Reflectometry (TDR) technique. We evaluated our approach on a real vehicle network.
Rullo, Antonino, Midi, Daniele, Serra, Edoardo, Bertino, Elisa.  2017.  A Game of Things: Strategic Allocation of Security Resources for IoT. Proceedings of the Second International Conference on Internet-of-Things Design and Implementation. :185–190.
In many Internet of Thing (IoT) application domains security is a critical requirement, because malicious parties can undermine the effectiveness of IoT-based systems by compromising single components and/or communication channels. Thus, a security infrastructure is needed to ensure the proper functioning of such systems even under attack. However, it is also critical that security be at a reasonable resource and energy cost, as many IoT devices may not have sufficient resources to host expensive security tools. In this paper, we focus on the problem of efficiently and effectively securing IoT networks by carefully allocating security tools. We model our problem according to game theory, and provide a Pareto-optimal solution, in which the cost of the security infrastructure, its energy consumption, and the probability of a successful attack, are minimized. Our experimental evaluation shows that our technique improves the system robustness in terms of packet delivery rate for different network topologies.
Rullo, A., Serra, E., Bertino, E., Lobo, J..  2017.  Shortfall-Based Optimal Security Provisioning for Internet of Things. 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). :2585–2586.

We present a formal method for computing the best security provisioning for Internet of Things (IoT) scenarios characterized by a high degree of mobility. The security infrastructure is intended as a security resource allocation plan, computed as the solution of an optimization problem that minimizes the risk of having IoT devices not monitored by any resource. We employ the shortfall as a risk measure, a concept mostly used in the economics, and adapt it to our scenario. We show how to compute and evaluate an allocation plan, and how such security solutions address the continuous topology changes that affect an IoT environment.

Rukavitsyn, A., Borisenko, K., Shorov, A..  2017.  Self-learning method for DDoS detection model in cloud computing. 2017 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). :544–547.

Cloud Computing has many significant benefits like the provision of computing resources and virtual networks on demand. However, there is the problem to assure the security of these networks against Distributed Denial-of-Service (DDoS) attack. Over the past few decades, the development of protection method based on data mining has attracted many researchers because of its effectiveness and practical significance. Most commonly these detection methods use prelearned models or models based on rules. Because of this the proposed DDoS detection methods often failure in dynamically changing cloud virtual networks. In this paper, we purposed self-learning method allows to adapt a detection model to network changes. This is minimized the false detection and reduce the possibility to mark legitimate users as malicious and vice versa. The developed method consists of two steps: collecting data about the network traffic by Netflow protocol and relearning the detection model with the new data. During the data collection we separate the traffic on legitimate and malicious. The separated traffic is labeled and sent to the relearning pool. The detection model is relearned by a data from the pool of current traffic. The experiment results show that proposed method could increase efficiency of DDoS detection systems is using data mining.

Ruiz-Martin, Cristina, Wainer, Gabriel, Lopez-Paredes, Adolfo.  2020.  Studying Communications Resiliency in Emergency Plans. 2020 Spring Simulation Conference (SpringSim). :1–12.
Recent disasters have shown that hazards can be unpredictable and can have catastrophic consequences. Emergency plans are key to dealing with these situations and communications play a key role in emergency management. In this paper, we provide a formalism to design resilient emergency plans in terms of communications. We exemplify how to use the formalism using a case study of a Nuclear Emergency Plan.
Rui Zhou, Rong Min, Qi Yu, Chanjuan Li, Yong Sheng, Qingguo Zhou, Xuan Wang, Kuan-Ching Li.  2014.  Formal Verification of Fault-Tolerant and Recovery Mechanisms for Safe Node Sequence Protocol. Advanced Information Networking and Applications (AINA), 2014 IEEE 28th International Conference on. :813-820.

Fault-tolerance has huge impact on embedded safety-critical systems. As technology that assists to the development of such improvement, Safe Node Sequence Protocol (SNSP) is designed to make part of such impact. In this paper, we present a mechanism for fault-tolerance and recovery based on the Safe Node Sequence Protocol (SNSP) to strengthen the system robustness, from which the correctness of a fault-tolerant prototype system is analyzed and verified. In order to verify the correctness of more than thirty failure modes, we have partitioned the complete protocol state machine into several subsystems, followed to the injection of corresponding fault classes into dedicated independent models. Experiments demonstrate that this method effectively reduces the size of overall state space, and verification results indicate that the protocol is able to recover from the fault model in a fault-tolerant system and continue to operate as errors occur.
 

Rui Shu, Peipei Wang, Sigmund A. Gorski III, Benjamin Andow, Adwait Nadkarni, Luke Deshotels, Jason Gionta, William Enck, Xiaohui Gu.  2016.  A Study of Security Isolation Techniques. ACM Computing Surveys (CSUR).

Security isolation is a foundation of computing systems that enables resilience to different forms of attacks. This article seeks to understand existing security isolation techniques by systematically classifying different approaches and analyzing their properties. We provide a hierarchical classification structure for grouping different security  isolation techniques.  At the top level, we consider two principal aspects: mechanism and policy. Each aspect is broken down into salient dimensions that describe key properties. We break the mechanism into two dimensions: enforcement location and isolation granularity, and break the policy aspect  down into three dimensions: policy generation, policy configurability, and policy lifetime. We apply our classification to a set of representative papers that cover a breadth of security isolation techniques and discuss trade-offs among different design choices and limitations of existing  approaches.

 

Ruggeri, Armando, Celesti, Antonio, Fazio, Maria, Galletta, Antonino, Villari, Massimo.  2020.  BCB-X3DH: A Blockchain Based Improved Version of the Extended Triple Diffie-Hellman Protocol. 2020 Second IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA). :73–78.
The Extended Triple Diffie-Hellman (X3DH) protocol has been used for years as the basis of secure communication establishment among parties (i.e, humans and devices) over the Internet. However, such a protocol has several limits. It is typically based on a single trust third-party server that represents a single point of failure (SPoF) being consequently exposed to well- known Distributed Denial of Service (DDOS) attacks. In order to address such a limit, several solutions have been proposed so far that are often cost expensive and difficult to be maintained. The objective of this paper is to propose a BlockChain-Based X3DH (BCB-X3DH) protocol that allows eliminating such a SPoF, also simplifying its maintenance. Specifically, it combines the well- known X3DH security mechanisms with the intrinsic features of data non-repudiation and immutability that are typical of Smart Contracts. Furthermore, different implementation approaches are discussed to suits both human-to-human and device-to-device scenarios. Experiments compared the performance of both X3DH and BCB-X3DH.
Ruehrup, Stefan, Krenn, Stephan.  2019.  Towards Privacy in Geographic Message Dissemination for Connected Vehicles. 2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE). :1–6.
With geographic message dissemination, connected vehicles can be served with traffic information in their proximity, thereby positively impacting road safety, traffic management, or routing. Since such messages are typically relevant in a small geographic area, servers only distribute messages to affected vehicles for efficiency reasons. One main challenge is to maintain scalability of the server infrastructure when collecting location updates from vehicles and determining the relevant group of vehicles when messages are distributed to a geographic relevance area, while at the same time respecting the individual user's privacy in accordance with legal regulations. In this paper, we present a framework for geographic message dissemination following the privacy-by-design and privacy-by-default principles, without having to accept efficiency drawbacks compared to conventional server-client based approaches.
Rudolph, M., Moucha, C., Feth, D..  2016.  A Framework for Generating User-and Domain-Tailored Security Policy Editors. 2016 IEEE 24th International Requirements Engineering Conference Workshops (REW). :56–61.

In modern enterprises, incorrect or inconsistent security policies can lead to massive damage, e.g., through unintended data leakage. As policy authors have different skills and background knowledge, usable policy editors have to be tailored to the author's individual needs and to the corresponding application domain. However, the development of individual policy editors and the customization of existing ones is an effort consuming task. In this paper, we present a framework for generating tailored policy editors. In order to empower user-friendly and less error-prone specification of security policies, the framework supports multiple platforms, policy languages, and specification paradigms.

Rudolph, Hendryk, Lan, Tian, Strehl, Konrad, He, Qinwei, Lan, Yuanliang.  2019.  Simulating the Efficiency of Thermoelectrical Generators for Sensor Nodes. 2019 4th IEEE Workshop on the Electronic Grid (eGRID). :1—6.

In order to be more environmentally friendly, a lot of parts and aspects of life become electrified to reduce the usage of fossil fuels. This can be seen in the increased number of electrical vehicles in everyday life. This of course only makes a positive impact on the environment, if the electricity is produced environmentally friendly and comes from renewable sources. But when the green electrical power is produced, it still needs to be transported to where it's needed, which is not necessarily near the production site. In China, one of the ways to do this transport is to use High Voltage Direct Current (HVDC) technology. This of course means, that the current has to be converted to DC before being transported to the end user. That implies that the converter stations are of great importance for the grid security. Therefore, a precise monitoring of the stations is necessary. Ideally, this could be accomplished with wireless sensor nodes with an autarkic energy supply. A role in this energy supply could be played by a thermoelectrical generator (TEG). But to assess the power generated in the specific environment, a simulation would be highly desirable, to evaluate the power gained from the temperature difference in the converter station. This paper proposes a method to simulate the generated power by combining a model for the generator with a Computational Fluid Dynamics (CFD) model converter.