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Leff, D., Maskay, A., Cunha, M. P. da.  2020.  Wireless Interrogation of High Temperature Surface Acoustic Wave Dynamic Strain Sensor. 2020 IEEE International Ultrasonics Symposium (IUS). :1–4.
Dynamic strain sensing is necessary for high-temperature harsh-environment applications, including powerplants, oil wells, aerospace, and metal manufacturing. Monitoring dynamic strain is important for structural health monitoring and condition-based maintenance in order to guarantee safety, increase process efficiency, and reduce operation and maintenance costs. Sensing in high-temperature (HT), harsh-environments (HE) comes with challenges including mounting and packaging, sensor stability, and data acquisition and processing. Wireless sensor operation at HT is desirable because it reduces the complexity of the sensor connection, increases reliability, and reduces costs. Surface acoustic wave resonators (SAWRs) are compact, can operate wirelessly and battery-free, and have been shown to operate above 1000°C, making them a potential option for HT HE dynamic strain sensing. This paper presents wirelessly interrogated SAWR dynamic strain sensors operating around 288.8MHz at room temperature and tested up to 400°C. The SAWRs were calibrated with a high-temperature wired commercial strain gauge. The sensors were mounted onto a tapered-type Inconel constant stress beam and the assembly was tested inside a box furnace. The SAWR sensitivity to dynamic strain excitation at 25°C, 100°C, and 400°C was .439 μV/με, 0.363μV/με, and .136 μV/με, respectively. The experimental outcomes verified that inductive coupled wirelessly interrogated SAWRs can be successfully used for dynamic strain sensing up to 400°C.
Kumar, R., Sarupria, G., Panwala, V., Shah, S., Shah, N..  2020.  Power Efficient Smart Home with Voice Assistant. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1—5.

The popularity and demand of home automation has increased exponentially in recent years because of the ease it provides. Recently, development has been done in this domain and few systems have been proposed that either use voice assistants or application for controlling the electrical appliances. However; less emphasis is laid on power efficiency and this system cannot be integrated with the existing appliances and hence, the entire system needs to be upgraded adding to a lot of additional cost in purchasing new appliances. In this research, the objective is to design such a system that emphasises on power efficiency as well as can be integrated with the already existing appliances. NodeMCU, along with Raspberry Pi, Firebase realtime database, is used to create a system that accomplishes such endeavours and can control relays, which can control these appliances without the need of replacing them. The experiments in this paper demonstrate triggering of electrical appliances using voice assistant, fire alarm on the basis of flame sensor and temperature sensor. Moreover; use of android application was presented for operating electrical appliances from a remote location. Lastly, the system can be modified by adding security cameras, smart blinds, robot vacuums etc.

Islam, M. M., Karmakar, G., Kamruzzaman, J., Murshed, M..  2019.  Measuring Trustworthiness of IoT Image Sensor Data Using Other Sensors’ Complementary Multimodal Data. 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :775–780.
Trust of image sensor data is becoming increasingly important as the Internet of Things (IoT) applications grow from home appliances to surveillance. Up to our knowledge, there exists only one work in literature that estimates trustworthiness of digital images applied to forensic applications, based on a machine learning technique. The efficacy of this technique is heavily dependent on availability of an appropriate training set and adequate variation of IoT sensor data with noise, interference and environmental condition, but availability of such data cannot be assured always. Therefore, to overcome this limitation, a robust method capable of estimating trustworthy measure with high accuracy is needed. Lowering cost of sensors allow many IoT applications to use multiple types of sensors to observe the same event. In such cases, complementary multimodal data of one sensor can be exploited to measure trust level of another sensor data. In this paper, for the first time, we introduce a completely new approach to estimate the trustworthiness of an image sensor data using another sensor's numerical data. We develop a theoretical model using the Dempster-Shafer theory (DST) framework. The efficacy of the proposed model in estimating trust level of an image sensor data is analyzed by observing a fire event using IoT image and temperature sensor data in a residential setup under different scenarios. The proposed model produces highly accurate trust level in all scenarios with authentic and forged image data.
Beran, P., Klöhn, M., Hohe, H..  2019.  Measurement Characteristics of Different Integrated Three-Dimensional Magnetic Field Sensors. IEEE Magnetics Letters. 10:1–5.
Datasheets of different commercially available integrated sensors for vector measurements of magnetic fields provide typical specifications, such as measurement range, sampling rate, resolution, and noise. Other characteristics of interest, such as linearity, cross-sensitivity, remanent magnetization, and drifts over temperature, are mostly missing. This letter presents testing results of those characteristics of integrated three-dimensional (3-D) sensors working with different sensor principles and technologies in a reproducible measuring process. The sensors are exposed to temperatures from -20 °C to 80 °C and are cycled in hysteresis loops in fields up to 2.5 mT. For applying high-accuracy magnetic fields, a calibrated 3-D Helmholtz coil setup is used. Commercially available integrated 3-D magnetic field sensors are put in operation on a printed circuit board using nonmagnetic passive components. All sensors are configured for best measurement accuracy according to their data-sheets. The results show that sensors based on anisotropic magnetoresistance have high accuracy and low offsets yet also a high degree of nonlinearity. Hall-based sensors show good linearity but also high cross-sensitivity. A magnetic remanence appears for Hall-based sensors with integrated magnetic concentrators as well as for sensors using anisotropic magnetoresistance. Nearly all sensors show remaining drifts over temperature regarding offset and sensitivity up to several percentages.
Ponomarev, Kirill Yu..  2019.  Attribute-Based Access Control in Service Mesh. 2019 Dynamics of Systems, Mechanisms and Machines (Dynamics). :1–4.
Modern cloud applications can consist of hundreds of services with thousands of instances. In order to solve the problems of interservice interaction in this highly dynamic environment, an additional software infrastructure layer called service mesh is introduced. This layer provides a single point of interaction with the network for each service. Service mesh mechanisms are responsible for: load balancing, processing of network requests, service discovery, authentication, authorization, etc. However, the following questions arise: complex key management, fine-grained access control at the application level, confidentiality of data and many-to-many communications. It is possible to solve these problems with Attribute-based encryption (ABE) methods. This paper presents an abstract model of a service mesh and a protocol for interservice communications, which uses ABE for authorization and confidentiality of the messages.
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.

Gries, Stefan, Ollesch, Julius, Gruhn, Volker.  2019.  Modeling Semantic Dependencies to Allow Flow Monitoring in Networks with Black-Box Nodes. 2019 IEEE/ACM 5th International Workshop on Software Engineering for Smart Cyber-Physical Systems (SEsCPS). :14–17.
Cyber-Physical Systems are distributed, heterogeneous systems that communicate and exchange data over networks. This creates semantic dependencies between the individual components. In the event of an error, it is difficult to identify the source of an occurring error that is spread due to those underlying dependencies. Tools such as the Information Flow Monitor solve this problem, but require compliance with a protocol. Nodes that do not adhere to this protocol prevent errors from being tracked. In this paper, we present a way to bridge these black-box nodes with a dependency model and to still be able to use them in monitoring tools.
Ahsan, Ramoza, Bashir, Muzammil, Neamtu, Rodica, Rundensteiner, Elke A., Sarkozy, Gabor.  2019.  Nearest Neighbor Subsequence Search in Time Series Data. 2019 IEEE International Conference on Big Data (Big Data). :2057—2066.
Continuous growth in sensor data and other temporal sequence data necessitates efficient retrieval and similarity search support on these big time series datasets. However, finding exact similarity results, especially at the granularity of subsequences, is known to be prohibitively costly for large data sets. In this paper, we thus propose an efficient framework for solving this exact subsequence similarity match problem, called TINN (TIme series Nearest Neighbor search). Exploiting the range interval diversity properties of time series datasets, TINN captures similarity at two levels of abstraction, namely, relationships among subsequences within each long time series and relationships across distinct time series in the data set. These relationships are compactly organized in an augmented relationship graph model, with the former relationships encoded in similarity vectors at TINN nodes and the later captured by augmented edge types in the TINN Graph. Query processing strategy deploy novel pruning techniques on the TINN Graph, including node skipping, vertical and horizontal pruning, to significantly reduce the number of time series as well as subsequences to be explored. Comprehensive experiments on synthetic and real world time series data demonstrate that our TINN model consistently outperforms state-of-the-art approaches while still guaranteeing to retrieve exact matches.
Srivastava, Ankush, Ghosh, Prokash.  2019.  An Efficient Memory Zeroization Technique Under Side-Channel Attacks. 2019 32nd International Conference on VLSI Design and 2019 18th International Conference on Embedded Systems (VLSID). :76–81.
Protection of secured data content in volatile memories (processor caches, embedded RAMs etc) is essential in networking, wireless, automotive and other embedded secure applications. It is utmost important to protect secret data, like authentication credentials, cryptographic keys etc., stored over volatile memories which can be hacked during normal device operations. Several security attacks like cold boot, disclosure attack, data remanence, physical attack, cache attack etc. can extract the cryptographic keys or secure data from volatile memories of the system. The content protection of memory is typically done by assuring data deletion in minimum possible time to minimize data remanence effects. In today's state-of-the-art SoCs, dedicated hardwares are used to functionally erase the private memory contents in case of security violations. This paper, in general, proposes a novel approach of using existing memory built-in-self-test (MBIST) hardware to zeroize (initialize memory to all zeros) on-chip memory contents before it is being hacked either through different side channels or secuirty attacks. Our results show that the proposed MBIST based content zeroization approach is substantially faster than conventional techniques. By adopting the proposed approach, functional hardware requirement for memory zeroization can be waived.
Facon, Adrien, Guilley, Sylvain, Ngo, Xuan-Thuy, Perianin, Thomas.  2019.  Hardware-enabled AI for Embedded Security: A New Paradigm. 2019 3rd International Conference on Recent Advances in Signal Processing, Telecommunications Computing (SigTelCom). :80–84.

As chips become more and more connected, they are more exposed (both to network and to physical attacks). Therefore one shall ensure they enjoy a sufficient protection level. Security within chips is accordingly becoming a hot topic. Incident detection and reporting is one novel function expected from chips. In this talk, we explain why it is worthwhile to resort to Artificial Intelligence (AI) for security event handling. Drivers are the need to aggregate multiple and heterogeneous security sensors, the need to digest this information quickly to produce exploitable information, and so while maintaining a low false positive detection rate. Key features are adequate learning procedures and fast and secure classification accelerated by hardware. A challenge is to embed such security-oriented AI logic, while not compromising chip power budget and silicon area. This talk accounts for the opportunities permitted by the symbiotic encounter between chip security and AI.

Nikolov, Neven, Nakov, Ognyan.  2019.  Research of Secure Communication of Esp32 IoT Embedded System to.NET Core Cloud Structure Using MQTTS SSL/TLS. 2019 IEEE XXVIII International Scientific Conference Electronics (ET). :1–4.

This paper studies and describes encrypted communication between IoT cloud and IoT embedded systems. It uses encrypted MQTTS protocol with SSL/TLS certificate. A JSON type data format is used between the cloud structure and the IoT device. The embedded system used in this experiment is Esp32 Wrover. The IoT embedded system measures temperature and humidity from a sensor DHT22. The architecture and software implementation of the experimental stage are also presented.

Almehmadi, Tahani, Alshehri, Suhair, Tahir, Sabeen.  2019.  A Secure Fog-Cloud Based Architecture for MIoT. 2019 2nd International Conference on Computer Applications Information Security (ICCAIS). :1–6.

Medical Internet of Things (MIoT) offers innovative solutions to a healthier life, making radical changes in people's lives. Healthcare providers are enabled to continuously and remotely monitor their patients for many medial issues outside hospitals and healthcare providers' offices. MIoT systems and applications lead to increase availability, accessibility, quality and cost-effectiveness of healthcare services. On the other hand, MIoT devices generate a large amount of diverse real-time data, which is highly sensitive. Thus, securing medical data is an essential requirement when developing MIoT architectures. However, the MIoT architectures being developed in the literature have many security issues. To address the challenge of data security in MIoT, the integration of fog computing and MIoT is studied as an emerging and appropriate solution. By data security, it means that medial data is stored in fog nodes and transferred to the cloud in a secure manner to prevent any unauthorized access. In this paper, we propose a design for a secure fog-cloud based architecture for MIoT.

Warabino, Takayuki, Suzuki, Yusuke, Miyazawa, Masanori.  2019.  ROS-based Robot Development Toward Fully Automated Network Management. 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS). :1–4.

While the introduction of the softwarelization technologies such as SDN and NFV transfers main focus of network management from hardware to software, the network operators still have to care for a lot of network and computing equipment located in the network center. Toward fully automated network management, we believe that robotic approach will be significant, meaning that robot will care for the physical equipment on behalf of human. This paper explains our experience and insight gained throughout development of a network management robot. We utilize ROS(Robot Operating System) which is a powerful platform for robot development and secures the ease of development and expandability. Our roadmap of the network management robot is also shown as well as three use cases such as environmental monitoring, operator assistance and autonomous maintenance of the equipment. Finally, the paper briefly explains experimental results conducted in a commercial network center.

Gautier, Adam M., Andel, Todd R., Benton, Ryan.  2018.  On-Device Detection via Anomalous Environmental Factors. Proceedings of the 8th Software Security, Protection, and Reverse Engineering Workshop. :5:1–5:8.
Embedded Systems (ES) underlie society's critical cyberinfrastructure and comprise the vast majority of consumer electronics, making them a prized target for dangerous malware and hardware Trojans. Malicious intrusion into these systems present a threat to national security and economic stability as globalized supply chains and tight network integration make ES more susceptible to attack than ever. High-end ES like the Xilinx Zynq-7020 system on a chip are widely used in the field and provide a representative platform for investigating the methods of cybercriminals. This research suggests a novel anomaly detection framework that could be used to detect potential zero-day exploits, undiscovered rootkits, or even maliciously implanted hardware by leveraging the Zynq architecture and real-time device-level measurements of thermal side-channels. The results of an initial investigation showed different processor workloads produce distinct thermal fingerprints that are detectable by out-of-band, digital logic-based thermal sensors.
Berjab, N., Le, H. H., Yu, C., Kuo, S., Yokota, H..  2018.  Hierarchical Abnormal-Node Detection Using Fuzzy Logic for ECA Rule-Based Wireless Sensor Networks. 2018 IEEE 23rd Pacific Rim International Symposium on Dependable Computing (PRDC). :289-298.

The Internet of things (IoT) is a distributed, networked system composed of many embedded sensor devices. Unfortunately, these devices are resource constrained and susceptible to malicious data-integrity attacks and failures, leading to unreliability and sometimes to major failure of parts of the entire system. Intrusion detection and failure handling are essential requirements for IoT security. Nevertheless, as far as we know, the area of data-integrity detection for IoT has yet to receive much attention. Most previous intrusion-detection methods proposed for IoT, particularly for wireless sensor networks (WSNs), focus only on specific types of network attacks. Moreover, these approaches usually rely on using precise values to specify abnormality thresholds. However, sensor readings are often imprecise and crisp threshold values are inappropriate. To guarantee a lightweight, dependable monitoring system, we propose a novel hierarchical framework for detecting abnormal nodes in WSNs. The proposed approach uses fuzzy logic in event-condition-action (ECA) rule-based WSNs to detect malicious nodes, while also considering failed nodes. The spatiotemporal semantics of heterogeneous sensor readings are considered in the decision process to distinguish malicious data from other anomalies. Following our experiments with the proposed framework, we stress the significance of considering the sensor correlations to achieve detection accuracy, which has been neglected in previous studies. Our experiments using real-world sensor data demonstrate that our approach can provide high detection accuracy with low false-alarm rates. We also show that our approach performs well when compared to two well-known classification algorithms.

Pawlenka, T., Škuta, J..  2018.  Security system based on microcontrollers. 2018 19th International Carpathian Control Conference (ICCC). :344–347.
The article describes design and realization of security system based on single-chip microcontrollers. System includes sensor modules for unauthorized entrance detection based on magnetic contact, measuring carbon monoxide level, movement detection and measuring temperature and humidity. System also includes control unit, control panel and development board Arduino with ethernet interface connected for web server implementation.
Nichols, W., Hawrylak, P. J., Hale, J., Papa, M..  2018.  Methodology to Estimate Attack Graph System State from a Simulation of a Nuclear Research Reactor. 2018 Resilience Week (RWS). :84-87.
Hybrid attack graphs are a powerful tool when analyzing the cybersecurity of a cyber-physical system. However, it is important to ensure that this tool correctly models reality, particularly when modelling safety-critical applications, such as a nuclear reactor. By automatically verifying that a simulation reaches the state predicted by an attack graph by analyzing the final state of the simulation, this verification procedure can be accomplished. As such, a mechanism to estimate if a simulation reaches the expected state in a hybrid attack graph is proposed here for the nuclear reactor domain.
Molka-Danielsen, J., Engelseth, P., Olešnaníková, V., Šarafín, P., Žalman, R..  2017.  Big Data Analytics for Air Quality Monitoring at a Logistics Shipping Base via Autonomous Wireless Sensor Network Technologies. 2017 5th International Conference on Enterprise Systems (ES). :38–45.
The indoor air quality in industrial workplace buildings, e.g. air temperature, humidity and levels of carbon dioxide (CO2), play a critical role in the perceived levels of workers' comfort and in reported medical health. CO2 can act as an oxygen displacer, and in confined spaces humans can have, for example, reactions of dizziness, increased heart rate and blood pressure, headaches, and in more serious cases loss of consciousness. Specialized organizations can be brought in to monitor the work environment for limited periods. However, new low cost wireless sensor network (WSN) technologies offer potential for more continuous and autonomous assessment of industrial workplace air quality. Central to effective decision making is the data analytics approach and visualization of what is potentially, big data (BD) in monitoring the air quality in industrial workplaces. This paper presents a case study that monitors air quality that is collected with WSN technologies. We discuss the potential BD problems. The case trials are from two workshops that are part of a large on-shore logistics base a regional shipping industry in Norway. This small case study demonstrates a monitoring and visualization approach for facilitating BD in decision making for health and safety in the shipping industry. We also identify other potential applications of WSN technologies and visualization of BD in the workplace environments; for example, for monitoring of other substances for worker safety in high risk industries and for quality of goods in supply chain management.
Ciovati, G., Cheng, G., Drury, M., Fischer, J., Geng, R..  2017.  Impact of Remanent Magnetic Field on the Heat Load of Original CEBAF Cryomodule. IEEE Transactions on Applied Superconductivity. 27:1–6.

The heat load of the original cryomodules for the continuous electron beam accelerator facility is 50% higher than the target value of 100 W at 2.07 K for refurbished cavities operating at an accelerating gradient of 12.5 MV/m. This issue is due to the quality factor of the cavities being 50% lower in the cryomodule than when tested in a vertical cryostat, even at low RF field. Previous studies were not conclusive about the origin of the additional losses. We present the results of a systematic study of the additional losses in a five-cell cavity from a decommissioned cryomodule after attaching components, which are part of the cryomodule, such as the cold tuner, the He tank, and the cold magnetic shield, prior to cryogenic testing in a vertical cryostat. Flux-gate magnetometers and temperature sensors are used as diagnostic elements. Different cool-down procedures and tests in different residual magnetic fields were investigated during the study. Three flux-gate magnetometers attached to one of the cavities installed in the refurbished cryomodule C50-12 confirmed the hypothesis of high residual magnetic field as a major cause for the increased RF losses.

Patra, M. K..  2017.  An architecture model for smart city using Cognitive Internet of Things (CIoT). 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT). :1–6.

In this paper, a distributed architecture for the implementation of smart city has been proposed to facilitate various smart features like solid waste management, efficient urban mobility and public transport, smart parking, robust IT connectivity, safety and security of citizens and a roadmap for achieving it. How massive volume of IoT data can be analyzed and a layered architecture of IoT is explained. Why data integration is important for analyzing and processing of data collected by the different smart devices like sensors, actuators and RFIDs is discussed. The wireless sensor network can be used to sense the data from various locations but there has to be more to it than stuffing sensors everywhere for everything. Why only the sensor is not sufficient for data collection and how human beings can be used to collect data is explained. There is some communication protocols between the volunteers engaged in collecting data to restrict the sharing of data and ensure that the target area is covered with minimum numbers of volunteers. Every volunteer should cover some predefined area to collect data. Then the proposed architecture model is having one central server to store all data in a centralized server. The data processing and the processing of query being made by the user is taking place in centralized server.

Khalid, F., Hasan, S. R., Hasan, O., Awwadl, F..  2017.  Behavior Profiling of Power Distribution Networks for Runtime Hardware Trojan Detection. 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS). :1316–1319.

Runtime hardware Trojan detection techniques are required in third party IP based SoCs as a last line of defense. Traditional techniques rely on golden data model or exotic signal processing techniques such as utilizing Choas theory or machine learning. Due to cumbersome implementation of such techniques, it is highly impractical to embed them on the hardware, which is a requirement in some mission critical applications. In this paper, we propose a methodology that generates a digital power profile during the manufacturing test phase of the circuit under test. A simple processing mechanism, which requires minimal computation of measured power signals, is proposed. For the proof of concept, we have applied the proposed methodology on a classical Advanced Encryption Standard circuit with 21 available Trojans. The experimental results show that the proposed methodology is able to detect 75% of the intrusions with the potential of implementing the detection mechanism on-chip with minimal overhead compared to the state-of-the-art techniques.

Hill, Z., Nichols, W. M., Papa, M., Hale, J. C., Hawrylak, P. J..  2017.  Verifying Attack Graphs through Simulation. 2017 Resilience Week (RWS). :64–67.

Verifying attacks against cyber physical systems can be a costly and time-consuming process. By using a simulated environment, attacks can be verified quickly and accurately. By combining the simulation of a cyber physical system with a hybrid attack graph, the effects of a series of exploits can be accurately analysed. Furthermore, the use of a simulated environment to verify attacks may uncover new information about the nature of the attacks.

Pundir, N., Hazari, N. A., Amsaad, F., Niamat, M..  2017.  A Novel Hybrid Delay Based Physical Unclonable Function Immune to Machine Learning Attacks. 2017 IEEE National Aerospace and Electronics Conference (NAECON). :84–87.

In this paper, machine learning attacks are performed on a novel hybrid delay based Arbiter Ring Oscillator PUF (AROPUF). The AROPUF exhibits improved results when compared to traditional Arbiter Physical Unclonable Function (APUF). The challenge-response pairs (CRPs) from both PUFs are fed to the multilayered perceptron model (MLP) with one hidden layer. The results show that the CRPs generated from the proposed AROPUF has more training and prediction errors when compared to the APUF, thus making it more difficult for the adversary to predict the CRPs.

Paul-Pena, D., Krishnamurthy, P., Karri, R., Khorrami, F..  2017.  Process-aware side channel monitoring for embedded control system security. 2017 IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC). :1–6.

Cyber-physical systems (CPS) are interconnections of heterogeneous hardware and software components (e.g., sensors, actuators, physical systems/processes, computational nodes and controllers, and communication subsystems). Increasing network connectivity of CPS computational nodes facilitates maintenance and on-demand reprogrammability and reduces operator workload. However, such increasing connectivity also raises the potential for cyber-attacks that attempt unauthorized modifications of run-time parameters or control logic in the computational nodes to hamper process stability or performance. In this paper, we analyze the effectiveness of real-time monitoring using digital and analog side channels. While analog side channels might not typically provide sufficient granularity to observe each iteration of a periodic loop in the code in the CPS device, the temporal averaging inherent to side channel sensory modalities enables observation of persistent changes to the contents of a computational loop through their resulting effect on the level of activity of the device. Changes to code can be detected by observing readings from side channel sensors over a period of time. Experimental studies are performed on an ARM-based single board computer.

Sándor, H., Genge, B., Szántó, Z..  2017.  Sensor data validation and abnormal behavior detection in the Internet of Things. 2017 16th RoEduNet Conference: Networking in Education and Research (RoEduNet). :1–5.
Internet of Things (IoT) and its various application domains are radically changing the lives of people, providing smart services which will ultimately constitute integral components of the living environment. The services of IoT operate based on the data flows collected from the different sensors and actuators. In this respect, the correctness and security of the sensor data transported over the IoT system is a crucial factor in ensuring the correct functioning of the IoT services. In this work, we present a method that can detect abnormal sensor events based on “apriori” knowledge of the behavior of the monitored process. The main advantage of the proposed methodology is that it builds on well-established theoretical works, while delivering a practical technique with low computational requirements. As a result, the developed technique can be hosted on various components of an IoT system. The developed approach is evaluated through real-world use-cases.