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The permanent magnet synchronous motor (PMSM) has several advantages over the DC motor and is gradually replacing it in the industry. The dynamics of the PMSM are described by non-linear equations; it is sensitive to unknown external disturbances (load), and its characteristics vary over time. All of these restrictions complicate the control task. Non-linear controls are required to adjust for non-linearities and the drawbacks mentioned above. This paper investigates an interconnection and damping assignment (IDA) passivity-based control (PBC) combined with a non-linear observer approach for the PMSM using the model represented in the dq-frame. The IDA-PBC approach has the inherent benefit of not canceling non-linear features but compensating them in a damped manner. The suggested PBC is in charge of creating the intended dynamic of the system, while the non-linear observer is in charge of reconstructing the recorded signals in order to compel the PMSM to track speed. The primary objective of this study is to synthesize the controller while accounting for the whole dynamic of the PMSM and making the system passive. It is performed by restructuring the energy of the proposed strategy and introducing a damping component that addresses the non-linear elements in a damped instead of deleted way, so providing a duality concept between both the IDA-PBC and the observer There are three methods for computing IDA-PBC: parametric, nonparametric, and algebraic. The parameterized IDA-PBC method is used to control the speed of the PMSM. This method uses the energy function in parameterized closed-loop in terms of some functions depending on the system’s state vector, such that the energy formation step is satisfied. Then, the original port-controlled Hamiltonian (PCH) dynamics in open-loop (OL) are equalized with the desired one in closed-loop (CL). The equalization process allows obtaining a set of solutions of the partial differential equations. The latter must be solved in terms of the parameters of the energy function of the closed-loop. Finally, the stability properties are studied using the Lyapunov theory. Generally, the proposed candidate offers high robustness, fast speed convergence, and high efficiency over the conventional benchmark strategies. The effectiveness of the proposed strategy is performed under extensive numerical investigation with MATLAB/Simulink software.

Several advantages are provided by the permanent magnet synchronous motor (PMSM) over DC motors and is gradually replacing them in industry. The dynamics of the PMSM can be described by time-varying non-linear equations considering unknown external disturbances (load). However, the control task is complicated due to the associated constraints. Non-linear controls are required to correct for non-linearities, external disturbances, and parametric fluctuations. This paper investigates a new robust interconnection and damping assignment passivity-based control (IDA-PBC) paired with a non-linear observer technique and the dq-frame model of the PMSM. The IDA-PBC method has the advantage of not canceling non-linear features but compensating them in a damped manner. The proposed IDA-PBC is in charge of creating the system’s desired dynamic, while the nonlinear observer is in charge of reconstructing the measured signals in order to force the PMSM to track speed. The main contribution of this paper is to synthesize the controller while taking into account the PMSM’s entire dynamic and making the system passive. It is achieved by rearranging the energy of the proposed IDA-PBC and inserting a damping factor that compensates the non-linear terms in a damped rather than canceled manner, so establishing a duality concept between the observer and the IDA-PBC. IDA-PBC can be computed in three ways: parametrically, nonparametrically, and algebraically. To control the PMSM’s speed, the parameterized IDA-PBC approach is employed. In general, the presented candidate outperforms the conventional IDA-PBC in terms of resilience, speed of convergence, efficiency, and high robustness. The efficiency of the proposed technique is investigated numerically using MATLAB/Simulink software.

As microservices become more popular, more drawbacks become apparent to developers. One issue that many teams face today is the failure to visualize the entire system architecture holistically. Without a full view of the system, the architecture can become convoluted as teams add and subtract from their system without reconciling their changes. One established practice to determine a view on the entire system involves dynamic analysis of microservice interaction and dependencies. In this mapping study, we investigate dynamic analysis as a way to visualize system architecture. Capturing the architectural view with dynamic analysis has the ability to build the system and then show its behavior at run-time. We identify dynamic analysis techniques, the corresponding tools, and the models that these practices can generate. The findings of this study are relevant to developers of decentralized systems looking for a way to visualize their system architecture in a dynamic perspective.

Software code is present on multiple levels within current Internet of Things (IoT) systems. The quality of this code impacts system reliability, safety, maintainability, and other quality aspects. In this paper, we provide a comprehensive overview of code quality-related metrics, specifically revised for the context of IoT systems. These metrics are divided into main code quality categories: Size, redundancy, complexity, coupling, unit test coverage and effectiveness, cohesion, code readability, security, and code heterogeneity. The metrics are then linked to selected general quality characteristics from the ISO/IEC 25010:2011 standard by their possible impact on the quality and reliability of an IoT system, the principal layer of the system, the code levels and the main phases of the project to which they are relevant. This analysis is followed by a discussion of code smells and their relation to the presented metrics. The overview presented in the paper is the result of a thorough analysis and discussion of the author’s team with the involvement of external subject-matter experts in which a defined decision algorithm was followed. The primary result of the paper is an overview of the metrics accompanied by applicability notes related to the quality characteristics, the system layer, the level of the code, and the phase of the IoT project.

Code analysis brings excellent benefits to software development, maintenance, and quality assurance. Various tools can uncover code defects or even software bugs in a range of seconds. For many projects and developers, the code analysis tools became essential in their daily routines. However, how can code analysis help in an enterprise environment? Enterprise software solutions grow in scale and complexity. These solutions no longer involve only plain objects and basic language constructs but operate with various components and mechanisms simplifying the development of such systems. Enterprise software vendors have adopted various development and design standards; however, there is a gap between what constructs the enterprise frameworks use and what current code analysis tools recognize. This manuscript aims to challenge the mainstream research directions of code analysis and motivate for a transition towards code analysis of enterprise systems with interesting problems and opportunities. In particular, this manuscript addresses selected enterprise problems apparent for monolithic and distributed enterprise solutions. It also considers challenges related to the recent architectural push towards a microservice architecture. Along with open-source proof-of-concept prototypes to some of the challenges, this manuscript elaborates on code analysis directions and their categorization. Furthermore, it suggests one possible perspective of the problem area using aspect-oriented programming.

Internet of Things (IoT) is an emerging technology that has the promising power to change our future. Due to the market pressure, IoT systems may be released without sufficient testing. However, it is no longer acceptable to release IoT systems to the market without assuring the quality. As in the case of new technologies, the quality assurance process is a challenging task. This paper shows the results of the first comprehensive and systematic mapping study to structure and categories the research evidence in the literature starting in 2009 when the early publication of IoT papers for IoT quality assurance appeared. The conducted research is based on the most recent guidelines on how to perform systematic mapping studies. A set of research questions is defined carefully regarding the quality aspects of the IoT. Based on these questions, a large number of evidence and research papers is considered in the study (478 papers). We have extracted and analyzed different levels of information from those considered papers. Also, we have classified the topics addressed in those papers into categories based on the quality aspects. The study results carry out different areas that require more work and investigation in the context of IoT quality assurance. The results of the study can help in a further understanding of the research gaps. Moreover, the results show a roadmap for future research directions.

The integration of Information and Communication Technology (ICT) tools into mechanical devices in routine use within the aviation industry has heightened cyber-security concerns. The extent of the inherent vulnerabilities in the software tools that drive these systems escalates as the level of integration increases. Moreover, these concerns are becoming even more acute as the migration within the industry in the deployment of electronic-enabled aircraft and smart airports gathers pace. A review of cyber-security attacks and attack surfaces within the aviation sector over the last 20 years provides a mapping of the trends and insights that are of value in informing on future frameworks to protect the evolution of a key industry. The goal is to identify common threat actors, their motivations, attacks types and map the vulnerabilities within aviation infrastructures most commonly subject to persistent attack campaigns. The analyses will enable an improved understanding of both the current and potential future cyber-security protection provisions for the sector. Evidence is provided that the main threats to the industry arise from Advance Persistent Threat (APT) groups that operate, in collaboration with a particular state actor, to steal intellectual property and intelligence in order to advance their domestic aerospace capabilities as well as monitor, infiltrate and subvert other sovereign nations’ capabilities. A segment of the aviation industry commonly attacked is the Information Technology (IT) infrastructure, the most prominent type of attack being malicious hacking with intent to gain unauthorised access. The analysis of the range of attack surfaces and the existing threat dynamics has been used as a foundation to predict future cyber-attack trends. The insights arising from the review will support the future definition and implementation of proactive measures that protect critical infrastructures against cyber-incidents that damage the confidence of customers in a key service-oriented industry.

The paper presents a classification of cyber attacks within the context of the state of the art in the maritime industry. A systematic categorization of vessel components has been conducted, complemented by an analysis of key services delivered within ports. The vulnerabilities of the Global Navigation Satellite System (GNSS) have been given particular consideration since it is a critical subcategory of many maritime infrastructures and, consequently, a target for cyber attacks. Recent research confirms that the dramatic proliferation of cyber crimes is fueled by increased levels of integration of new enabling technologies, such as IoT and Big Data. The trend to greater systems integration is, however, compelling, yielding significant business value by facilitating the operation of autonomous vessels, greater exploitation of smart ports, a reduction in the level of manpower and a marked improvement in fuel consumption and efficiency of services. Finally, practical challenges and future research trends have been highlighted.

Microservice architecture has become the leading design for cloud-native systems. The highly decentralized approach to software development consists of relatively independent services, which provides benefits such as faster deployment cycles, better scalability, and good separation of concerns among services. With this new architecture, one can naturally expect a broad range of advancements and simplifications over legacy systems. However, microservice system design remains challenging, as it is still difficult for engineers to understand the system module boundaries. Thus, understanding and explaining the microservice systems might not be as easy as initially thought. This study aims to classify recently published approaches and techniques to analyze microservice systems. It also looks at the evolutionary perspective of such systems and their analysis. Furthermore, the identified approaches target various challenges and goals, which this study analyzed. Thus, it provides the reader with a roadmap to the discipline, tools, techniques, and open challenges for future work. It provides a guide towards choices when aiming for analyzing cloud-native systems. The results indicate five analytical approaches commonly used in the literature, possibly in combination, towards problems classified into seven categories.

Interaction testing can be used to effectively detect faults that are otherwise difficult to find by other testing techniques. However, in practice, the input configurations of software systems are subjected to constraints, especially in the case of highly configurable systems. Handling constraints effectively and efficiently in combinatorial interaction testing is a challenging problem. Nevertheless, researchers have attacked this challenge through different techniques, and much progress has been achieved in the past decade. Thus, it is useful to reflect on the current achievements and shortcomings and to identify potential areas of improvements. This paper presents the first comprehensive and systematic literature study to structure and categorize the research contributions for constrained interaction testing. Following the guidelines of conducting a literature study, the relevant data are extracted from a set of 103 research papers belonging to constrained interaction testing. The topics addressed in constrained interaction testing research are classified into four categories of constraint test generation, application, generation and application, and model validation studies. The papers within each of these categories are extensively reviewed. Apart from answering several other research questions, this paper also discusses the applications of constrained interaction testing in several domains, such as software product lines, fault detection and characterization, test selection, security, and graphical user interface testing. This paper ends with a discussion of limitations, challenges, and future work in the area.

Cyber-attacks continue to grow, both in terms of volume and sophistication. This is aided by an increase in available computational power, expanding attack surfaces, and advancements in the human understanding of how to make attacks undetectable. Unsurprisingly, machine learning is utilised to defend against these attacks. In many applications, the choice of features is more important than the choice of model. A range of studies have, with varying degrees of success, attempted to discriminate between benign traffic and well-known cyber-attacks. The features used in these studies are broadly similar and have demonstrated their effectiveness in situations where cyber-attacks do not imitate benign behaviour. To overcome this barrier, in this manuscript, we introduce new features based on a higher level of abstraction of network traffic. Specifically, we perform flow aggregation by grouping flows with similarities. This additional level of feature abstraction benefits from cumulative information, thus qualifying the models to classify cyber-attacks that mimic benign traffic. The performance of the new features is evaluated using the benchmark CICIDS2017 dataset, and the results demonstrate their validity and effectiveness. This novel proposal will improve the detection accuracy of cyber-attacks and also build towards a new direction of feature extraction for complex ones.

Cyber situational awareness has been proven to be of value in forming a comprehensive understanding of threats and vulnerabilities within organisations, as the degree of exposure is governed by the prevailing levels of cyber-hygiene and established processes. A more accurate assessment of the security provision informs on the most vulnerable environments that necessitate more diligent management. The rapid proliferation in the automation of cyber-attacks is reducing the gap between information and operational technologies and the need to review the current levels of robustness against new sophisticated cyber-attacks, trends, technologies and mitigation countermeasures has become pressing. A deeper characterisation is also the basis with which to predict future vulnerabilities in turn guiding the most appropriate deployment technologies. Thus, refreshing established practices and the scope of the training to support the decision making of users and operators. The foundation of the training provision is the use of Cyber-Ranges (CRs) and Test-Beds (TBs), platforms/tools that help inculcate a deeper understanding of the evolution of an attack and the methodology to deploy the most impactful countermeasures to arrest breaches. In this paper, an evaluation of documented CRs and TBs platforms is evaluated. CRs and TBs are segmented by type, technology, threat scenarios, applications and the scope of attainable training. To enrich the analysis of documented CRs and TBs research and cap the study, a taxonomy is developed to provide a broader comprehension of the future of CRs and TBs. The taxonomy elaborates on the CRs/TBs dimensions, as well as, highlighting a diminishing differentiation between application areas.

The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. In addition to exploiting sine and cosine functions to perform local and global searches (hence the name sine-cosine), the SCA introduces several random and adaptive parameters to facilitate the search process. Although it shows promising results, the search process of the SCA is vulnerable to local minima/maxima due to the adoption of a fixed switch probability and the bounded magnitude of the sine and cosine functions (from -1 to 1). In this paper, we propose a new hybrid Q-learning sine-cosine- based strategy, called the Q-learning sine-cosine algorithm (QLSCA). Within the QLSCA, we eliminate the switching probability. Instead, we rely on the Q-learning algorithm (based on the penalty and reward mechanism) to dynamically identify the best operation during runtime. Additionally, we integrate two new operations (Lévy flight motion and crossover) into the QLSCA to facilitate jumping out of local minima/maxima and enhance the solution diversity. To assess its performance, we adopt the QLSCA for the combinatorial test suite minimization problem. Experimental results reveal that the QLSCA is statistically superior with regard to test suite size reduction compared to recent state-of-the-art strategies, including the original SCA, the particle swarm test generator (PSTG), adaptive particle swarm optimization (APSO) and the cuckoo search strategy (CS) at the 95% confidence level. However, concerning the comparison with discrete particle swarm optimization (DPSO), there is no significant difference in performance at the 95% confidence level. On a positive note, the QLSCA statistically outperforms the DPSO in certain configurations at the 90% confidence level.

The article deals with detection of network anomalies. Network anomalies include everything that is quite different from the normal operation. For detection of anomalies were used machine learning systems. Machine learning can be considered as a support or a limited type of artificial intelligence. A machine learning system usually starts with some knowledge and a corresponding knowledge organization so that it can interpret, analyse, and test the knowledge acquired. There are several machine learning techniques available. We tested Decision tree learning and Bayesian networks. The open source data-mining framework WEKA was the tool we used for testing the classify, cluster, association algorithms and for visualization of our results. The WEKA is a collection of machine learning algorithms for data mining tasks.