Trusted Operation of Cyber-Physical Processes Based on Assessment of the System's State and Operating Mode.

We consider the trusted operation of cyber-physical processes based on an assessment of the system's state and operating mode and present a method for detecting anomalies in the behavior of a cyber-physical system (CPS) based on the analysis of the data transmitted by its sensory subsystem. Probability theory and mathematical statistics are used to process and normalize the data in order to determine whether or not the system is in the correct operating mode and control process state. To describe the mode-specific control processes of a CPS, the paradigm of using cyber-physical parameters is taken as a basis, as it is the feature that most clearly reflects the system's interaction with physical processes. In this study, two metrics were taken as a sign of an anomaly: the probability of falling into the sensor values' confidence interval and parameter change monitoring. These two metrics, as well as the current mode evaluation, produce a final probability function for our trust in the CPS's currently executing control process, which is, in turn, determined by the operating mode of the system. Based on the results of this trust assessment, it is possible to draw a conclusion about the processing state in which the system is operating. If the score is higher than 0.6, it means the system is in a trusted state. If the score is equal to 0.6, it means the system is in an uncertain state. If the trust score tends towards zero, then the system can be interpreted as unstable or under stress due to a system failure or deliberate attack. Through a case study using cyber-attack data for an unmanned aerial vehicle (UAV), it was found that the method works well. When we were evaluating the normal flight mode, there were no false positive anomaly estimates. When we were evaluating the UAV's state during an attack, a deviation and an untrusted state were detected. This method can be used to implement software solutions aimed at detecting system faults and cyber-attacks, and thus make decisions about the presence of malfunctions in the operation of a CPS, thereby minimizing the amount of knowledge and initial data about the system.

A Review of Emerging Technologies for IoT-Based Smart Cities.

Smart cities can be complemented by fusing various components and incorporating recent emerging technologies. IoT communications are crucial to smart city operations, which are designed to support the concept of a "Smart City" by utilising the most cutting-edge communication technologies to enhance city administration and resident services. Smart cities have been outfitted with numerous IoT-based gadgets; the Internet of Things is a modular method to integrate various sensors with all ICT technologies. This paper provides an overview of smart cities' concepts, characteristics, and applications. We thoroughly investigate smart city applications, challenges, and possibilities with solutions in recent technological trends and perspectives, such as machine learning and blockchain. We discuss cloud and fog IoT ecosystems in the in capacity of IoT devices, architectures, and machine learning approaches. In addition we integrate security and privacy aspects, including blockchain applications, towards more trustworthy and resilient smart cities. We also highlight the concepts, characteristics, and applications of smart cities and provide a conceptual model of the smart city mega-events framework. Finally, we outline the impact of recent emerging technologies' implications on challenges, applications, and solutions for futuristic smart cities.

An Adaptive Protection System for Sensor Networks Based on Analysis of Neighboring Nodes.

Creation and operation of sensor systems is a complex challenge not only for industrial and military purposes but also for consumer services ("smart city", "smart home") and other applications such as agriculture ("smart farm", "smart greenhouse"). The use of such systems gives a positive economic effect and provides additional benefits from various points of view. At the same time, due to a large number of threats and challenges to cyber security, it is necessary to detect attacks on sensor systems in a timely manner. Here we present an anomaly detection method in which sensor nodes observe their neighbors and detect obvious deviations in their behavior. In this way, the community of neighboring nodes works collectively to protect one another. The nodes record only those parameters and attributes that are inherent in any node. Regardless of the node's functionality, such parameters include the amount of traffic passing through the node, its Central Processing Unit (CPU) load, as well as the presence and number of packets dropped by the node. Our method's main goal is to implement protection against the active influence of an internal attacker on the whole sensor network. We present the anomaly detection method, a dataset collection strategy, and experimental results that show how different types of attacks can be distinguished in the data produced by the nodes.

Specific Resonant Properties of Non-Symmetrical Microwave Antennas.

The aircraft avionics modernization process often requires optimization of the aircraft itself. Scale models of aircraft and their antennas are frequently used to solve this problem. Here we present interesting properties of the resonant antennas, which were discovered serendipitously during the measurement process of some microwave antennas' models as part of an aircraft modernization project. Aircraft microwave antennas are often designed as non-symmetric flat microwave antennas. Due to their thin, low and longitudinally elongated outer profile, they are also called tail antennas. An analysis of the resonant properties of non-symmetric antennas was performed in the band from 1 GHz to 4 GHz. The length of the antenna models ranged from 2 cm to 7 cm. The width of the antennas, together with the thickness of the strip, was always a constant parameter for one measured set of six antennas. In the measurement and subsequent analysis, attention was focused on the first-series resonant frequency (λ/4) of each antenna. During the evaluation of the resonance parameters, the flat microwave antenna models showed specific resonant properties different from those of conventional cylindrical microwave antennas. This article aims to inform professionals about these unknown specific properties of non-symmetrical antennas. The results of experimental measurements are analyzed theoretically and then visually compared using graphs so that the reader can more easily understand the properties observed. These surprising observations open up some new possibilities for the design, implementation, and use of flat microwave antennas, as found in modern aircraft, automobiles, etc.

A Self-Diagnosis Method for Detecting UAV Cyber Attacks Based on Analysis of Parameter Changes.

We consider how to protect Unmanned Aerial Vehicles (UAVs) from Global Positioning System (GPS) spoofing attacks to provide safe navigation. The Global Navigation Satellite System (GNSS) is widely used for locating drones and is by far the most popular navigation solution. This is because of the simplicity and relatively low cost of this technology, as well as the accuracy of the transmitted coordinates. Nevertheless, there are many security threats to GPS navigation. These are primarily related to the nature of the GPS signal, as an intruder can jam and spoof the GPS signal. We discuss methods of protection against this type of attack and have developed an experimental stand and conducted scenarios of attacks on a drone's GPS system. Data from the UAV's flight log were collected and analyzed in order to see the attack's impact on sensor readings. From this we identify a new method for detecting UAV anomalies by analyzing changes in internal parameters of the UAV. This self-diagnosis method allows a UAV to independently assess the presence of changes in its own subsystems indicative of cyber attacks.

Multifaceted Modelling of Complex Business Enterprises.

We formalise and present a new generic multifaceted complex system approach for modelling complex business enterprises. Our method has a strong focus on integrating the various data types available in an enterprise which represent the diverse perspectives of various stakeholders. We explain the challenges faced and define a novel approach to converting diverse data types into usable Bayesian probability forms. The data types that can be integrated include historic data, survey data, and management planning data, expert knowledge and incomplete data. The structural complexities of the complex system modelling process, based on various decision contexts, are also explained along with a solution. This new application of complex system models as a management tool for decision making is demonstrated using a railway transport case study. The case study demonstrates how the new approach can be utilised to develop a customised decision support model for a specific enterprise. Various decision scenarios are also provided to illustrate the versatility of the decision model at different phases of enterprise operations such as planning and control.