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Currently, unmanned aerial vehicles (UAVs) are widely used due to their low cost and flexibility. In particular, they are used in remote sensing as airborne platforms for various instruments. Here we investigate the capability of a conical scanning radar operated as a scatterometer mounted on a high-altitude UAV to perform sea surface wind retrieval based on an appropriate geophysical model function (GMF). Increasing the maximum altitude of the wind retrieval method’s applicability is an important problem for UAV or manned aircraft scatterometers. For this purpose, we consider the possibility of increasing the method’s maximum altitude by applying a semicircular scheme for azimuth normalized radar cross section (NRCS) sampling instead of a whole 360° circular scheme. We developed wind retrieval algorithms for both semicircular and circular NRCS sampling schemes and evaluated them using Monte Carlo simulations. The simulations showed that the semicircular scheme for azimuth NRCS sampling enables twice the maximum altitude for wind retrieval compared to a 360° circular scheme. At the same time, however, the semicircular scheme requires approximately three times the number of integrated NRCS samples in each azimuth sector to provide equivalent wind retrieval accuracy. Nonetheless, our results confirm that the semicircular azimuth NRCS sampling scheme is well-suited for wind retrieval, and any wind retrieval errors are within the typical range for scatterometer wind recovery. The obtained results can be used for enhancing existing UAV and aircraft radars, and for the development of new remote sensing systems.

The data analysis subsystem of an Unmanned Aerial Vehicle (UAV) includes two main modules: a data acquisition module for data processing and a normalization module. One of the main features of an adaptive UAV protection system is the analysis of its cyber-physical parameters. An attack on a general-purpose computer system mainly affects the integrity, confidentiality and availability of important information. By contrast, an attack on a Cyber-Physical System (CPS), such as a UAV, affects the functionality of the system and may disrupt its operation, ultimately preventing it from fulfilling its tasks correctly. Cyber-physical parameters are the internal parameters of a system node, including the states of its computing resources, data storage, actuators and sensor system. Here, we develop a data normalization technique that additionally allows us to identify the signs of a cyber-attack. In addition, we define sets of parameters that can highlight an attack and define a new database format to support intrusion detection for UAVs. To achieve these goals, we performed an experimental study of the impact of attacks on UAV parameters and developed a software module for collecting data from UAVs, as well as a technique for normalizing and presenting data for detecting attacks on UAVs. Data analysis and the evaluation of the quality of a parameter (whether the parameter changes normally, or abrupt anomalous changes are observed) are facilitated by converting different types of data to the same format. The resulting formalized CPS model allows us to identify the nature of an attack and its potential impact on UAV subsystems. In the future, such a model could be the basis of a CPS digital twin in terms of security. The presented normalization technique supports processing raw data, as well as classifying data sets for their use in machine learning (ML) analyses in the future. The data normalization technique can also help to immediately determine the presence and signs of an

In recent decades, cyber security issues in IEC 61850-compliant substation automation systems (SASs) have become growing concerns. Many researchers have developed various strategies to detect malicious behaviours of SASs during the system operational stage, such as anomaly-based detection. However, most existing anomaly-based detection methods identify an abnormal behaviour by checking every single network packet without any association. These traditional methods cannot effectively detect 'stealthy' attacks which modify legitimate messages slightly while imitating patterns of benign behaviours. In this paper, we present feature selection and extraction methods to generalise and summarise critical features when detecting insider attacks triggering from untrusted control devices within SASs. By applying a sliding window-based sequential classification mechanism, our detection method can detect anomalies across multiple devices without the need to learn datasets collected from all devices. Firstly, to generalise critical features and summarise systems' behaviours so that it is unnecessary to collect all datasets, we selected and extracted six critical network features from generic object-oriented substation events (GOOSE) messages and seven summarised physical features based on the general architecture of the primary plant of distribution substations. After that, to improve detection accuracy and reduce computational costs, we applied sliding window algorithms to divide datasets into different overlapped window-based snippets. Then we applied a sequential classification model based on Bidirectional Long Short-Term Memory networks to train and test those datasets. As a result, our method can detect insider attacks across multiple devices accurately with a false-negative rate of less than 1%.

Here we developed a method for detecting cyber security attacks aimed at spoofing the Global Positioning System (GPS) signal of an Unmanned Aerial Vehicle (UAV). Most methods for detecting UAV anomalies indicative of an attack use machine learning or other such methods that compare normal behavior with abnormal behavior. Such approaches require large amounts of data and significant “training” time to prepare and implement the system. Instead, we consider a new approach based on other mathematical methods for detecting UAV anomalies without the need to first collect a large amount of data and describe normal behavior patterns. Doing so can simplify the process of creating an anomaly detection system, which can further facilitate easier implementation of intrusion detection systems in UAVs. This article presents issues related to ensuring the information security of UAVs. Development of the GPS spoofing detection method for UAVs is then described, based on a preliminary study that made it possible to form a mathematical apparatus for solving the problem. We then explain the necessary analysis of parameters and methods of data normalization, and the analysis of the Kullback-Leibler divergence measure needed to detect anomalies in UAV systems.

In a multimode industrial control system, mode switching decisions have to follow standard operating procedures which are set for the safety of the system based on the operating limitations of equipment. A rich literature can be found on monitoring multimode systems. However, that work is mainly focused on mode identification and monitoring anomalies in the process running under each mode. Instead, we present a data-driven method for monitoring the modes' switching constraints. This article is based on state-transition matrix and decision-tree methods to discover data-driven mode switching conditions. Moreover, our approach is not limited to only threshold based condition learning. To capture data trajectory-based conditions, we adopt a functional data descriptors method. In practical experiments, we showed that our approach can discover anomalous mode-switching decisions which cannot be discovered by previous multimode process-monitoring methods.

This paper addresses the challenge of decoupling “back-office” enterprise system functions in order to integrate them with the Industrial Internet-of-Things (IIoT). IIoT is a widely anticipated strategy, combining IoT technologies managing physical object movements, interactions and contexts, with business contexts. However, enterprise systems, supporting these contexts, are notoriously large and monolithic, and coordinate centralised business processes through software components dedicated to managing business objects (BOs). Such objects and their associated operations are difficult to manually decouple because of the asynchronous and user-driven nature of the business processes and complex BO dependencies, such as many-to-many and aggregation relationships. Here we present a software remodularisation technique for enterprise systems, to support the discovery of fine-grained microservices, which can be extracted and embedded to run on IIoT network nodes. It combines the semantic knowledge of enterprise systems, i.e., the BO structure, with syntactic knowledge of the code, i.e., various dependencies at the level of classes and methods. Using extracted feature sets based on both semantic and syntactic dependencies, K-Means clustering and optimisation is then used to recommend microservices, i.e., redistributions of BO operations through microservices from BO-centric components of enterprise systems. The approach is validated using the Dolibarr open source ERP system, in which we identify processes comprising both “edge” operations and request-response calls to the Cloud-based enterprise system. Through experimentation using Amazon GreenGrass deployments, simulating IIoT nodes, we show that the recommended microservices demonstrate key non-functional characteristics, of high execution efficiency, scalability and availability.

Microservices have been proposed as the software architecture style for fog-IoT network applications ecosystems. Recently, microservices have been extensively used in fog-IoT ecosystems. Here, master-worker fog-based frameworks have been widely adopted in the ecosystem to ensure resilience. However, the architecture’s reliability, including the possibility of the master fog node failure, and service unavailability, is not reflected adequately in the literature. Therefore, we present a resilient master-citizen fog-IoT framework to ensure efficient resource management and overall system reliability. In this work, we develop a master fog node and layered citizen nodes in the distributed ecosystem. Our fault-tolerant fog-IoT-based microservice execution framework can ensure efficient recovery from a single point of failure, unavailability, and unexpected events in the master fog node, which means the network can continue working after a system failure. We use different fault-tolerance strategies and algorithms for selecting the master fog node that synchronizes with other citizen fog nodes and the upper layer cloud for efficient microservice execution. Finally, we developed and implemented a resilient fog-IoT network for providing uninterrupted services in the event of master fog node failure.

With the rapid advancement of the electrical grid, substation automation systems (SASs) have been developing continuously. However, with the introduction of advanced features, such as remote control, potential cyber security threats in SASs are also increased. Additionally, crucial components in SASs, such as protection relays, usually come from third-party vendors and may not be fully trusted. Untrusted devices may stealthily perform harmful or unauthorised behaviours which could compromise or damage SASs, and therefore, bring adverse impacts to the primary plant. Thus, it is necessary to detect abnormal behaviours from an untrusted device before it brings about catastrophic impacts. Anomaly detection techniques are suitable to detect anomalies in SASs as they only bring minimal side-effects to normal system operations. Many researchers have developed various machine learning algorithms and mathematical models to improve the accuracy of anomaly detection. However, without prudent feature selection, it is difficult to achieve high accuracy when detecting attacks launched from internal trusted networks, especially for stealthy message modification attacks which only modify message payloads slightly and imitate patterns of benign behaviours. Therefore, this paper presents choices of features which improve the accuracy of anomaly detection within SASs, especially for detecting 'stealthy' attacks. By including two additional features, Boolean control data from message payloads and physical values from sensors, our method improved the accuracy of anomaly detection by decreasing the false-negative rate from 25% to 5% approximately.

Fog computing complemented cloud computing integration services in the Internet of Things (IoT) and the web of real-time interactivity. Fog offers faster computing and other services facilities sitting close to user applications. However, secure data transfer in the fog is still a challenging issue requiring attention and efficient deployment of a secure data security scheme. We present an Identity Based Encryption (IBE) scheme to secure data security and transmission in fog clouds and IoT ecosystems. We devise and develop a four-level Hierarchical Identity Based Architecture for Fog Computing (HIBAF) data security scheme to enhance data security. We also analyze the system’s performance regarding response time, CPU utilization, run-time encryption-decryption, and key generation time in the fog computing paradigm to an increasing number of users data-loads. Moreover, we evaluate our scheme and compare the outcomes with different cryptography structures to discern our scheme’s effectiveness. Furthermore, we also evaluate secret key updating time, re-encrypted key updating time, and file revoking time by launching DDoS attacks both in the cloud and fog computing environment to compare improvements of HIBAF in the fog computing paradigm. Finally, through this overall evaluation, we have found that the developed HIBAF scheme provides a 33% performance enhancement in a fog environment in terms of data processing, provision, and management compared to the cloud environment.

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.

Fog computing complements cloud computing by removing several limitations, such as delays and network bandwidth. It emerged to support Internet of Things (IoT) applications wherein its computations and tasks are carried out at the network’s edge. Heterogeneous IoT devices interact with different users throughout a network. However, data security is a crucial concern for IoT, fog and cloud network ecosystems. Since the number of anonymous users increases and new identity disclosures occur within the IoTs, it is becoming challenging to grow mesh networks to deliver end to end communications, as the extended IoT networks resemble a mesh architecture. To reinforce data security over IoTs, we deploy a microservice-based blockchain mechanism for fogs, which works as a decentralized client-server network medium (i.e., secured end device-based communication). We implement a blockchain equipped security scheme to be used with a fog-IoT hierarchical tree-based overlay mesh architecture to address and develop the network performance issues. In this study, we consider encryption and decryption delays from IoT and fog-integrated parts to monitor data records and compare them through the developed security scheme. The blocks of a blockchain offer the desired execution results mainly in terms of the algorithmic efficiency, which correlates with the existing algorithms, namely the Advanced Encryption Standard (AES), the Rivest Shamir Adleman (RSA), and the Data Encryption Standard (DES). Our ‘BFIM’ scheme has an enhanced task scheduling capacity and a more efficient throughput than the AES, DES, RSA resource deliverables (i.e., tasks). Our comprehensive performance evaluation implies that the Blockchain-based Fog IoT Microservice (i.e., BFIM) architecture provides a task delivery efficiency of 78.79% (i.e., task deliverable) and a service delivery efficiency of 83.24% (i.e., task scheduling). The ‘BFIM’ also has an overall process delivery efficiency of 75% (i.e., time delay, throu

Fog computing is one of the most important emerging paradigms in recent technological development. It alleviates several limitations of cloud computing by bringing computation, communication, storage, and real-time services near to the end-users. However, with the rapid development of automation in smart cities, the number of task executions by fog nodes are increasing, requiring additional fog nodes. In this paper, we present a Scheduling-based Dynamic Fog Computing (SDFC) Framework to augment the utilization of existing resources rather than adding further fog resources. It includes an additional layer, Master Fog (MF), between the cloud and general-purpose fogs, which are addressed here as Citizen Fog (CF). The MF is responsible for deciding task execution in CFs and the cloud. We use the Comparative Attributes Algorithm (CAA) to schedule tasks based on their priority and a Linear Attribute Summarized Algorithm (LASA) to select the most available CF with the highest computational ability. Our empirical results validate our SDFC framework and show the dependency on the cloud reduces by 15%–20% and overall execution time decreases by 45%–50%.

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.

Personal Health Record (PHR) systems in occupational health are an emerging technology for employee health and well-being promotions in organizations. Since PHRs increasingly experience low rates of adoption, practitioners and developers are searching for effective design guidelines to match current occupational health and well-being trends and user needs. We examined existing literature and conducted focus group discussions with a group of employees (n=26) to identify typically expected PHR features. We evaluated those PHR features through a questionnaire survey with employees (n=360) to identify features that make PHRs more user-friendly and motivate their use in an occupational environment. We found that the ability of easy data entry to PHR systems is the most important feature to motivate the use of PHRs in a work environment. Additionally, clear guidance to overcome health risks, displaying the current status of overall health and well-being information, and displaying the most current information in the main interface were identified as relatively important features to make PHRs more usable in occupational environments. Based on those features prioritization, we designed and implemented an occupation-based PHR prototype. Finally the PHR prototype was evaluated for usability through practical trials with real-world employees (n=42) to identify design guidelines for occupation-based PHRs in the light of standard heuristics. As findings, we discovered that designers of occupation-based PHRs should pay more attention to pleasurable and respectful interaction between users and the system, enhancing user skills and knowledge, user control and freedom, visibility of system status, and privacy.

This paper addresses the challenge of automated remodularization of large systems as microservices. It focuses on the analysis of enterprise systems, which are widely used in corporate sectors and are notoriously large, monolithic and challenging to manually decouple because they manage asynchronous, user-driven business processes and business objects (BOs) having complex structural relationships. The technique presented leverages semantic knowledge of enterprise systems, i.e., BO structure, together with syntactic knowledge of the code, i.e., classes and interactions as part of static profiling and clustering. On a semantic level, BOs derived from databases form the basis for prospective clustering of classes as modules, while on a syntactic level, structural and interaction details of classes provide further insights for module dependencies and clustering, based on K-Means clustering and optimization. Our integrated techniques are validated using two open source enterprise customer relationship management systems, SugarCRM and ChurchCRM. The results demonstrate improved feasibility of remodularizing enterprise systems (inclusive of coded BOs and classes) as microservices. Furthermore, the recommended microservices, integrated with ‘backend’ enterprise systems, demonstrate improvements in key non-functional characteristics, namely high execution efficiency, scalability and availability.

This article presents a novel concept for a highly accurate and simple two-port vector signal analyzer. A current phase ratio measurement method is defined for one independent external signal with a restriction of the measuring range ±90 or ±180°. The article presents a new way of phase ratio evaluation of two independent external signals with precise phase determination in a wide range from 0° to 360°. The new functional blocks of the analyzer circuit element act as a four-position radio frequency (RF) switcher and an algorithm for phase determination is presented. Practical application and verification of the results achieved are demonstrated by measuring the phase directional radiation pattern of a complex object such as an aircraft fuselage.

We have considered the problem of secure communication by means of chaotic carrier communication channels. Data protection is provided if the signal in the communication channel looks like a stochastic or noisy one, i.e., by the steganography property, and also by using advanced and secured approaches of mixing a useful signal into a chaotic carrier. The problem is to find effective and efficient techniques for useful signal reconstruction at the receiver side. We firstly use a synergetic nonlinear mathematical observer to recover the two useful signals transmitted simultaneously over a single communication channel. Compared to a known observer, the synergetic one operates with initial nonlinear models, i.e., it may be applied directly to chaotic systems. In that system structure, we consider the useful signals as unobservable variables and estimate them by the given model of a chaotic generator. A chaotic data transmission system with two channels is demonstrated; the presented mathematical procedure explains the steps of synergetic two-channel observer design. Computer simulation results prove the viability of our approach. The proposed data transmission scheme and the observer design procedure provide effective transmission and reconstruction of informational signals. Reconstruction error is up to approximately 25% but has a very short pulses shape and could be eliminated with further analog or digital filtering algorithms. As an example application, simulation of image transmission and recovery is demonstrated. The study’s results may be used as a basis for further research into secure data transmission system design.