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Context: Design smell detection has proven to be a significant activity that has an aim of not only enhancing the software quality but also increasing its life cycle. Objective: This work investigates whether machine learning approaches can effectively be leveraged for software design smell detection. Additionally, this paper provides a comparatively study, focused on using balanced datasets, where it checks if avoiding dataset balancing can be of any influence on the accuracy and behavior during design smell detection. Method: A set of experiments have been conducted-using 28 Machine Learning classifiers aimed at detecting God classes. This experiment was conducted using a dataset formed from 12,587 classes of 24 software systems, in which 1,958 classes were manually validated. Results: Ultimately, most classifiers obtained high performances,-with Cat Boost showing a higher performance. Also, it is evident from the experiments conducted that data balancing does not have any significant influence on the accuracy of detection. This reinforces the application of machine learning in real scenarios where the data is usually imbalanced by the inherent nature of design smells. Conclusions: Machine learning approaches can effectively be used as a leverage for God class detection. While in this paper we have employed SMOTE technique for data balancing, it is worth noting that there exist other methods of data balancing and with other design smells. Furthermore, it is also important to note that application of those other methods may improve the results, in our experiments SMOTE did not improve God class detection. The results are not fully generalizable because only one design smell is studied with projects developed in a single programming language, and only one balancing technique is used to compare with the imbalanced case. But these results are promising for the application in real design smells detection scenarios as mentioned above and the focus on other measures, suc, open access

Context: Design smell detection has proven to be a significant activity that has an aim of not only enhancing the software quality but also increasing its life cycle. Objective: This work investigates whether machine learning approaches can effectively be leveraged for software design smell detection. Additionally, this paper provides a comparatively study, focused on using balanced datasets, where it checks if avoiding dataset balancing can be of any influence on the accuracy and behavior during design smell detection. Method: A set of experiments have been conducted-using 28 Machine Learning classifiers aimed at detecting God classes. This experiment was conducted using a dataset formed from 12,587 classes of 24 software systems, in which 1,958 classes were manually validated. Results: Ultimately, most classifiers obtained high performances,-with Cat Boost showing a higher performance. Also, it is evident from the experiments conducted that data balancing does not have any significant influence on the accuracy of detection. This reinforces the application of machine learning in real scenarios where the data is usually imbalanced by the inherent nature of design smells. Conclusions: Machine learning approaches can effectively be used as a leverage for God class detection. While in this paper we have employed SMOTE technique for data balancing, it is worth noting that there exist other methods of data balancing and with other design smells. Furthermore, it is also important to note that application of those other methods may improve the results, in our experiments SMOTE did not improve God class detection. The results are not fully generalizable because only one design smell is studied with projects developed in a single programming language, and only one balancing technique is used to compare with the imbalanced case. But these results are promising for the application in real design smells detection scenarios as mentioned above and the focus on other measures, suc, open access

Context: Design smell detection has proven to be a significant activity that has an aim of not only enhancing the software quality but also increasing its life cycle. Objective: This work investigates whether machine learning approaches can effectively be leveraged for software design smell detection. Additionally, this paper provides a comparatively study, focused on using balanced datasets, where it checks if avoiding dataset balancing can be of any influence on the accuracy and behavior during design smell detection. Method: A set of experiments have been conducted-using 28 Machine Learning classifiers aimed at detecting God classes. This experiment was conducted using a dataset formed from 12,587 classes of 24 software systems, in which 1,958 classes were manually validated. Results: Ultimately, most classifiers obtained high performances,-with Cat Boost showing a higher performance. Also, it is evident from the experiments conducted that data balancing does not have any significant influence on the accuracy of detection. This reinforces the application of machine learning in real scenarios where the data is usually imbalanced by the inherent nature of design smells. Conclusions: Machine learning approaches can effectively be used as a leverage for God class detection. While in this paper we have employed SMOTE technique for data balancing, it is worth noting that there exist other methods of data balancing and with other design smells. Furthermore, it is also important to note that application of those other methods may improve the results, in our experiments SMOTE did not improve God class detection. The results are not fully generalizable because only one design smell is studied with projects developed in a single programming language, and only one balancing technique is used to compare with the imbalanced case. But these results are promising for the application in real design smells detection scenarios as mentioned above and the focus on other measures, suc, open access

Advances in Industrial Internet of Things (IIoT), inter-connectivity, continuous rise of smart ecosystems, and the quintessential need of process automation has not only opened formidable opportunities, but it has also extended the cyber-threat and attack landscape. Predominantly, this has been witnessed even as industries race towards aligning themselves with the on-demand industry 4.0 goals. Notably, it has become apparent that the convergence and amalgamation of Industrial Operational Technology (OT) with Information Technology (IT) brings about sophiscation, IIoT ecosystem complexities, paradigm shift and overall changes in security and digital forensic investigation architectures. Consequently, as the cybersecurity threat landscape also becomes more complicated in this context, with a widened attack surface, emergent and diverse system behaviors, the digital forensic perspective of IIoT is hardly integrated or addressed as many industries race to realize industry 4.0 objectives at the time of writing this paper. It is based on this premise, that this paper puts an argument that, at the time of writing this paper, there still lacks methodologies, standards, processes or maturity models that have a focus on IIoT forensics as the rat-race towards achieving Industry 4.0 persists. Based on these dimensions, the author explores the scope of IIoT forensics and posits the need of exploring and incorporating forensic standards and methods in IIoT. It is the authors’ opinion that the suppositions fronted herewith may provide fundamental building blocks for future IIoT-centered investigative frameworks. © 2022 The Authors, open access

Context: Design smell detection has proven to be a significant activity that has an aim of not only enhancing the software quality but also increasing its life cycle. Objective: This work investigates whether machine learning approaches can effectively be leveraged for software design smell detection. Additionally, this paper provides a comparatively study, focused on using balanced datasets, where it checks if avoiding dataset balancing can be of any influence on the accuracy and behavior during design smell detection. Method: A set of experiments have been conducted-using 28 Machine Learning classifiers aimed at detecting God classes. This experiment was conducted using a dataset formed from 12,587 classes of 24 software systems, in which 1,958 classes were manually validated. Results: Ultimately, most classifiers obtained high performances,-with Cat Boost showing a higher performance. Also, it is evident from the experiments conducted that data balancing does not have any significant influence on the accuracy of detection. This reinforces the application of machine learning in real scenarios where the data is usually imbalanced by the inherent nature of design smells. Conclusions: Machine learning approaches can effectively be used as a leverage for God class detection. While in this paper we have employed SMOTE technique for data balancing, it is worth noting that there exist other methods of data balancing and with other design smells. Furthermore, it is also important to note that application of those other methods may improve the results, in our experiments SMOTE did not improve God class detection. The results are not fully generalizable because only one design smell is studied with projects developed in a single programming language, and only one balancing technique is used to compare with the imbalanced case. But these results are promising for the application in real design smells detection scenarios as mentioned above and the focus on other measures, suc, open access

Advances in Industrial Internet of Things (IIoT), inter-connectivity, continuous rise of smart ecosystems, and the quintessential need of process automation has not only opened formidable opportunities, but it has also extended the cyber-threat and attack landscape. Predominantly, this has been witnessed even as industries race towards aligning themselves with the on-demand industry 4.0 goals. Notably, it has become apparent that the convergence and amalgamation of Industrial Operational Technology (OT) with Information Technology (IT) brings about sophiscation, IIoT ecosystem complexities, paradigm shift and overall changes in security and digital forensic investigation architectures. Consequently, as the cybersecurity threat landscape also becomes more complicated in this context, with a widened attack surface, emergent and diverse system behaviors, the digital forensic perspective of IIoT is hardly integrated or addressed as many industries race to realize industry 4.0 objectives at the time of writing this paper. It is based on this premise, that this paper puts an argument that, at the time of writing this paper, there still lacks methodologies, standards, processes or maturity models that have a focus on IIoT forensics as the rat-race towards achieving Industry 4.0 persists. Based on these dimensions, the author explores the scope of IIoT forensics and posits the need of exploring and incorporating forensic standards and methods in IIoT. It is the authors’ opinion that the suppositions fronted herewith may provide fundamental building blocks for future IIoT-centered investigative frameworks. © 2022 The Authors, open access

Advances in Industrial Internet of Things (IIoT), inter-connectivity, continuous rise of smart ecosystems, and the quintessential need of process automation has not only opened formidable opportunities, but it has also extended the cyber-threat and attack landscape. Predominantly, this has been witnessed even as industries race towards aligning themselves with the on-demand industry 4.0 goals. Notably, it has become apparent that the convergence and amalgamation of Industrial Operational Technology (OT) with Information Technology (IT) brings about sophiscation, IIoT ecosystem complexities, paradigm shift and overall changes in security and digital forensic investigation architectures. Consequently, as the cybersecurity threat landscape also becomes more complicated in this context, with a widened attack surface, emergent and diverse system behaviors, the digital forensic perspective of IIoT is hardly integrated or addressed as many industries race to realize industry 4.0 objectives at the time of writing this paper. It is based on this premise, that this paper puts an argument that, at the time of writing this paper, there still lacks methodologies, standards, processes or maturity models that have a focus on IIoT forensics as the rat-race towards achieving Industry 4.0 persists. Based on these dimensions, the author explores the scope of IIoT forensics and posits the need of exploring and incorporating forensic standards and methods in IIoT. It is the authors’ opinion that the suppositions fronted herewith may provide fundamental building blocks for future IIoT-centered investigative frameworks. © 2022 The Authors, open access

Advances in Industrial Internet of Things (IIoT), inter-connectivity, continuous rise of smart ecosystems, and the quintessential need of process automation has not only opened formidable opportunities, but it has also extended the cyber-threat and attack landscape. Predominantly, this has been witnessed even as industries race towards aligning themselves with the on-demand industry 4.0 goals. Notably, it has become apparent that the convergence and amalgamation of Industrial Operational Technology (OT) with Information Technology (IT) brings about sophiscation, IIoT ecosystem complexities, paradigm shift and overall changes in security and digital forensic investigation architectures. Consequently, as the cybersecurity threat landscape also becomes more complicated in this context, with a widened attack surface, emergent and diverse system behaviors, the digital forensic perspective of IIoT is hardly integrated or addressed as many industries race to realize industry 4.0 objectives at the time of writing this paper. It is based on this premise, that this paper puts an argument that, at the time of writing this paper, there still lacks methodologies, standards, processes or maturity models that have a focus on IIoT forensics as the rat-race towards achieving Industry 4.0 persists. Based on these dimensions, the author explores the scope of IIoT forensics and posits the need of exploring and incorporating forensic standards and methods in IIoT. It is the authors’ opinion that the suppositions fronted herewith may provide fundamental building blocks for future IIoT-centered investigative frameworks. © 2022 The Authors, open access

Advances in Industrial Internet of Things (IIoT), inter-connectivity, continuous rise of smart ecosystems, and the quintessential need of process automation has not only opened formidable opportunities, but it has also extended the cyber-threat and attack landscape. Predominantly, this has been witnessed even as industries race towards aligning themselves with the on-demand industry 4.0 goals. Notably, it has become apparent that the convergence and amalgamation of Industrial Operational Technology (OT) with Information Technology (IT) brings about sophiscation, IIoT ecosystem complexities, paradigm shift and overall changes in security and digital forensic investigation architectures. Consequently, as the cybersecurity threat landscape also becomes more complicated in this context, with a widened attack surface, emergent and diverse system behaviors, the digital forensic perspective of IIoT is hardly integrated or addressed as many industries race to realize industry 4.0 objectives at the time of writing this paper. It is based on this premise, that this paper puts an argument that, at the time of writing this paper, there still lacks methodologies, standards, processes or maturity models that have a focus on IIoT forensics as the rat-race towards achieving Industry 4.0 persists. Based on these dimensions, the author explores the scope of IIoT forensics and posits the need of exploring and incorporating forensic standards and methods in IIoT. It is the authors’ opinion that the suppositions fronted herewith may provide fundamental building blocks for future IIoT-centered investigative frameworks. © 2022 The Authors, open access

Context: Design smell detection has proven to be a significant activity that has an aim of not only enhancing the software quality but also increasing its life cycle. Objective: This work investigates whether machine learning approaches can effectively be leveraged for software design smell detection. Additionally, this paper provides a comparatively study, focused on using balanced datasets, where it checks if avoiding dataset balancing can be of any influence on the accuracy and behavior during design smell detection. Method: A set of experiments have been conducted-using 28 Machine Learning classifiers aimed at detecting God classes. This experiment was conducted using a dataset formed from 12,587 classes of 24 software systems, in which 1,958 classes were manually validated. Results: Ultimately, most classifiers obtained high performances,-with Cat Boost showing a higher performance. Also, it is evident from the experiments conducted that data balancing does not have any significant influence on the accuracy of detection. This reinforces the application of machine learning in real scenarios where the data is usually imbalanced by the inherent nature of design smells. Conclusions: Machine learning approaches can effectively be used as a leverage for God class detection. While in this paper we have employed SMOTE technique for data balancing, it is worth noting that there exist other methods of data balancing and with other design smells. Furthermore, it is also important to note that application of those other methods may improve the results, in our experiments SMOTE did not improve God class detection. The results are not fully generalizable because only one design smell is studied with projects developed in a single programming language, and only one balancing technique is used to compare with the imbalanced case. But these results are promising for the application in real design smells detection scenarios as mentioned above and the focus on other measures, suc, open access

Advances in Industrial Internet of Things (IIoT), inter-connectivity, continuous rise of smart ecosystems, and the quintessential need of process automation has not only opened formidable opportunities, but it has also extended the cyber-threat and attack landscape. Predominantly, this has been witnessed even as industries race towards aligning themselves with the on-demand industry 4.0 goals. Notably, it has become apparent that the convergence and amalgamation of Industrial Operational Technology (OT) with Information Technology (IT) brings about sophiscation, IIoT ecosystem complexities, paradigm shift and overall changes in security and digital forensic investigation architectures. Consequently, as the cybersecurity threat landscape also becomes more complicated in this context, with a widened attack surface, emergent and diverse system behaviors, the digital forensic perspective of IIoT is hardly integrated or addressed as many industries race to realize industry 4.0 objectives at the time of writing this paper. It is based on this premise, that this paper puts an argument that, at the time of writing this paper, there still lacks methodologies, standards, processes or maturity models that have a focus on IIoT forensics as the rat-race towards achieving Industry 4.0 persists. Based on these dimensions, the author explores the scope of IIoT forensics and posits the need of exploring and incorporating forensic standards and methods in IIoT. It is the authors’ opinion that the suppositions fronted herewith may provide fundamental building blocks for future IIoT-centered investigative frameworks. © 2022 The Authors, open access

Context: Design smell detection has proven to be a significant activity that has an aim of not only enhancing the software quality but also increasing its life cycle. Objective: This work investigates whether machine learning approaches can effectively be leveraged for software design smell detection. Additionally, this paper provides a comparatively study, focused on using balanced datasets, where it checks if avoiding dataset balancing can be of any influence on the accuracy and behavior during design smell detection. Method: A set of experiments have been conducted-using 28 Machine Learning classifiers aimed at detecting God classes. This experiment was conducted using a dataset formed from 12,587 classes of 24 software systems, in which 1,958 classes were manually validated. Results: Ultimately, most classifiers obtained high performances,-with Cat Boost showing a higher performance. Also, it is evident from the experiments conducted that data balancing does not have any significant influence on the accuracy of detection. This reinforces the application of machine learning in real scenarios where the data is usually imbalanced by the inherent nature of design smells. Conclusions: Machine learning approaches can effectively be used as a leverage for God class detection. While in this paper we have employed SMOTE technique for data balancing, it is worth noting that there exist other methods of data balancing and with other design smells. Furthermore, it is also important to note that application of those other methods may improve the results, in our experiments SMOTE did not improve God class detection. The results are not fully generalizable because only one design smell is studied with projects developed in a single programming language, and only one balancing technique is used to compare with the imbalanced case. But these results are promising for the application in real design smells detection scenarios as mentioned above and the focus on other measures, suc, open access

Context: Systematic Literature Reviews (SLRs) have been adopted within Software Engineering (SE) for more than a decade to provide meaningful summaries of evidence on several topics. Many of these SLRs are now potentially not fully up-to-date, and there are no standard proposals on how to update SLRs in SE. Objective: The objective of this paper is to propose guidelines on how to best search for evidence when updating SLRs in SE, and to evaluate these guidelines using an SLR that was not employed during the formulation of the guidelines. Method: To propose our guidelines, we compare and discuss outcomes from applying different search strategies to identify primary studies in a published SLR, an SLR update, and two replications in the area of effort estimation. These guidelines are then evaluated using an SLR in the area of software ecosystems, its update and a replication. Results: The use of a single iteration forward snowballing with Google Scholar, and employing as a seed set the original SLR and its primary studies is the most cost-effective way to search for new evidence when updating SLRs. Furthermore, the importance of having more than one researcher involved in the selection of papers when applying the inclusion and exclusion criteria is highlighted through the results. Conclusions: Our proposed guidelines formulated based upon an effort estimation SLR, its update and two replications, were supported when using an SLR in the area of software ecosystems, its update and a replication. Therefore, we put forward that our guidelines ought to be adopted for updating SLRs in SE. © 2020, Open access

Context: Systematic Literature Reviews (SLRs) have been adopted within Software Engineering (SE) for more than a decade to provide meaningful summaries of evidence on several topics. Many of these SLRs are now potentially not fully up-to-date, and there are no standard proposals on how to update SLRs in SE. Objective: The objective of this paper is to propose guidelines on how to best search for evidence when updating SLRs in SE, and to evaluate these guidelines using an SLR that was not employed during the formulation of the guidelines. Method: To propose our guidelines, we compare and discuss outcomes from applying different search strategies to identify primary studies in a published SLR, an SLR update, and two replications in the area of effort estimation. These guidelines are then evaluated using an SLR in the area of software ecosystems, its update and a replication. Results: The use of a single iteration forward snowballing with Google Scholar, and employing as a seed set the original SLR and its primary studies is the most cost-effective way to search for new evidence when updating SLRs. Furthermore, the importance of having more than one researcher involved in the selection of papers when applying the inclusion and exclusion criteria is highlighted through the results. Conclusions: Our proposed guidelines formulated based upon an effort estimation SLR, its update and two replications, were supported when using an SLR in the area of software ecosystems, its update and a replication. Therefore, we put forward that our guidelines ought to be adopted for updating SLRs in SE. © 2020, Open access

Context: Systematic Literature Reviews (SLRs) have been adopted within Software Engineering (SE) for more than a decade to provide meaningful summaries of evidence on several topics. Many of these SLRs are now potentially not fully up-to-date, and there are no standard proposals on how to update SLRs in SE. Objective: The objective of this paper is to propose guidelines on how to best search for evidence when updating SLRs in SE, and to evaluate these guidelines using an SLR that was not employed during the formulation of the guidelines. Method: To propose our guidelines, we compare and discuss outcomes from applying different search strategies to identify primary studies in a published SLR, an SLR update, and two replications in the area of effort estimation. These guidelines are then evaluated using an SLR in the area of software ecosystems, its update and a replication. Results: The use of a single iteration forward snowballing with Google Scholar, and employing as a seed set the original SLR and its primary studies is the most cost-effective way to search for new evidence when updating SLRs. Furthermore, the importance of having more than one researcher involved in the selection of papers when applying the inclusion and exclusion criteria is highlighted through the results. Conclusions: Our proposed guidelines formulated based upon an effort estimation SLR, its update and two replications, were supported when using an SLR in the area of software ecosystems, its update and a replication. Therefore, we put forward that our guidelines ought to be adopted for updating SLRs in SE. © 2020, Open access

Context: Systematic Literature Reviews (SLRs) have been adopted within Software Engineering (SE) for more than a decade to provide meaningful summaries of evidence on several topics. Many of these SLRs are now potentially not fully up-to-date, and there are no standard proposals on how to update SLRs in SE. Objective: The objective of this paper is to propose guidelines on how to best search for evidence when updating SLRs in SE, and to evaluate these guidelines using an SLR that was not employed during the formulation of the guidelines. Method: To propose our guidelines, we compare and discuss outcomes from applying different search strategies to identify primary studies in a published SLR, an SLR update, and two replications in the area of effort estimation. These guidelines are then evaluated using an SLR in the area of software ecosystems, its update and a replication. Results: The use of a single iteration forward snowballing with Google Scholar, and employing as a seed set the original SLR and its primary studies is the most cost-effective way to search for new evidence when updating SLRs. Furthermore, the importance of having more than one researcher involved in the selection of papers when applying the inclusion and exclusion criteria is highlighted through the results. Conclusions: Our proposed guidelines formulated based upon an effort estimation SLR, its update and two replications, were supported when using an SLR in the area of software ecosystems, its update and a replication. Therefore, we put forward that our guidelines ought to be adopted for updating SLRs in SE. © 2020, Open access

Context: Systematic Literature Reviews (SLRs) have been adopted within Software Engineering (SE) for more than a decade to provide meaningful summaries of evidence on several topics. Many of these SLRs are now potentially not fully up-to-date, and there are no standard proposals on how to update SLRs in SE. Objective: The objective of this paper is to propose guidelines on how to best search for evidence when updating SLRs in SE, and to evaluate these guidelines using an SLR that was not employed during the formulation of the guidelines. Method: To propose our guidelines, we compare and discuss outcomes from applying different search strategies to identify primary studies in a published SLR, an SLR update, and two replications in the area of effort estimation. These guidelines are then evaluated using an SLR in the area of software ecosystems, its update and a replication. Results: The use of a single iteration forward snowballing with Google Scholar, and employing as a seed set the original SLR and its primary studies is the most cost-effective way to search for new evidence when updating SLRs. Furthermore, the importance of having more than one researcher involved in the selection of papers when applying the inclusion and exclusion criteria is highlighted through the results. Conclusions: Our proposed guidelines formulated based upon an effort estimation SLR, its update and two replications, were supported when using an SLR in the area of software ecosystems, its update and a replication. Therefore, we put forward that our guidelines ought to be adopted for updating SLRs in SE. © 2020, Open access

Context: Systematic Literature Reviews (SLRs) have been adopted within Software Engineering (SE) for more than a decade to provide meaningful summaries of evidence on several topics. Many of these SLRs are now potentially not fully up-to-date, and there are no standard proposals on how to update SLRs in SE. Objective: The objective of this paper is to propose guidelines on how to best search for evidence when updating SLRs in SE, and to evaluate these guidelines using an SLR that was not employed during the formulation of the guidelines. Method: To propose our guidelines, we compare and discuss outcomes from applying different search strategies to identify primary studies in a published SLR, an SLR update, and two replications in the area of effort estimation. These guidelines are then evaluated using an SLR in the area of software ecosystems, its update and a replication. Results: The use of a single iteration forward snowballing with Google Scholar, and employing as a seed set the original SLR and its primary studies is the most cost-effective way to search for new evidence when updating SLRs. Furthermore, the importance of having more than one researcher involved in the selection of papers when applying the inclusion and exclusion criteria is highlighted through the results. Conclusions: Our proposed guidelines formulated based upon an effort estimation SLR, its update and two replications, were supported when using an SLR in the area of software ecosystems, its update and a replication. Therefore, we put forward that our guidelines ought to be adopted for updating SLRs in SE. © 2020, Open access

Context: Software development involves many activities, and decision making is an essential one. Various factors can impact a decision-making process, and by understanding such factors, one can improve the process. Since people are the ones making decisions, some human-related aspects are amongst those influencing factors. One such aspect is the decision maker's personality. Objective: This research investigates the relationship between decision-making style and personality within the context of software project development. Method: We conducted a survey in a population of Brazilian software engineers to gather data on their personality and decision-making style. Results: Data from 63 participants was gathered and resulted in the identification of seven statistically significant correlations between decision-making style and personality (personality factor and personality facets). Furthermore, we built a regression model in which decision-making style (DMS) was the response variable and personality factors the independent variables. The backward elimination procedure selected only agreeableness to explain 4.2% of DMS variation. The model accuracy was evaluated and deemed good enough. Regarding the moderation effect of demographic variables (age, educational level, experience, and role) on the relationship between DMS and Agreeableness, the analysis showed that only software engineers’ role has such effect. Conclusion: This paper contributes toward understanding the relationship between DMS and personality. Results show that the personality variable agreeableness can explain the variation in decision-making style. Furthermore, someone's role in a software development project can impact the strength of the relationship between DMS and agreeableness. © 2021 Elsevier B.V.

The evolution of manufacturing systems into a smart factory brings advantages but also increased cyber-risks. This paper investigates the problem of intrusion detection and autonomous response to cyber-attacks targeting the control logic of industrial control applications for the smart factory. Specifically, we propose ASiMOV (Asynchronous Modular Verification), a self-protecting architecture for cyber–physical systems realizing a verifiable control application. ASiMOV is inspired by modular redundancy and leverages virtualization technologies to respond and to prevent cyber-attacks to the control logic. Using simulation experiments, we evaluate: the effects of an attack on an industrial control application enhanced by ASiMOV; the delay introduced by ASiMOV within a control loop; and the cyber-attack detection delay. Results show that, in the simulated scenario, the controller can work with a sampling rate of up to 200 Hertz. Any tampering with the control logic is detected without false positives/negatives in a time equal to the latency between the proposed control application and the proposed IDS (e.g., tens to hundreds of milliseconds). © 2020