In recent years, technology has advanced to the fourth industrial revolution (Industry 4.0), where the Internet of things (IoTs), fog computing, computer security, and cyberattacks have evolved exponentially on a large scale. The rapid development of IoT devices and networks in various forms generate enormous amounts of data which in turn demand careful authentication and security. Artificial intelligence (AI) is considered one of the most promising methods for addressing cybersecurity threats and providing security. In this study, we present a systematic literature review (SLR) that categorize, map and survey the existing literature on AI methods used to detect cybersecurity attacks in the IoT environment. The scope of this SLR includes an in-depth investigation on most AI trending techniques in cybersecurity and state-of-art solutions. A systematic search was performed on various electronic databases (SCOPUS, Science Direct, IEEE Xplore, Web of Science, ACM, and MDPI). Out of the identified records, 80 studies published between 2016 and 2021 were selected, surveyed and carefully assessed. This review has explored deep learning (DL) and machine learning (ML) techniques used in IoT security, and their effectiveness in detecting attacks. However, several studies have proposed smart intrusion detection systems (IDS) with intelligent architectural frameworks using AI to overcome the existing security and privacy challenges. It is found that support vector machines (SVM) and random forest (RF) are among the most used methods, due to high accuracy detection another reason may be efficient memory. In addition, other methods also provide better performance such as extreme gradient boosting (XGBoost), neural networks (NN) and recurrent neural networks (RNN). This analysis also provides an insight into the AI roadmap to detect threats based on attack categories. Finally, we present recommendations for potential future investigations.
Organisations use Enterprise Architecture (EA) to reduce organisational complexity, improve communication, align business and information technology (IT), and drive organisational change. Due to the dynamic nature of environmental and organisational factors, EA descriptions need to change over time to keep providing value for its stakeholders. Emerging business and IT trends, such as Service-Oriented Architecture (SOA), may impact EA frameworks, methodologies, governance and tools. However, the phenomenon of EA evolution is still poorly understood. Using Archer's morphogenetic theory as a foundation, this research conceptualises three analytical phases of EA evolution in organisations, namely conditioning, interaction and elaboration. Based on a case study with a government agency, this paper provides new empirically and theoretically grounded insights into EA evolution, in particular in relation to the introduction of SOA, and describes relevant generative mechanisms affecting EA evolution. By doing so, it builds a foundation to further examine the impact of other IT trends such as mobile or cloud-based solutions on EA evolution. At a practical level, the research delivers a model that can be used to guide professionals to manage EA and continually evolve it. KeywordsEnterprise Architecture, EA, Service-oriented Architecture, SOA, critical realism, morphogenetic theory A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT 2 Empirical Insights into the Development of a Serviceoriented Enterprise Architecture AbstractOrganisations use Enterprise Architecture (EA) to reduce organisational complexity, improve communication, align business and information technology (IT), and drive organisational change. Due to the dynamic nature of environmental and organisational factors, EA descriptions need to change over time to keep providing value for its stakeholders. Emerging business and IT trends, such as Service-Oriented Architecture (SOA), may impact EA frameworks, methodologies, governance and tools. However, the phenomenon of EA evolution is still poorly understood. Using Archer's morphogenetic theory as a foundation, this research conceptualises three analytical phases of EA evolution in organisations, namely conditioning, interaction and elaboration. Based on a case study with a government agency, this paper provides new empirically and theoretically grounded insights into EA evolution, in particular in relation to the introduction of SOA, and describes relevant generative mechanisms affecting EA evolution. By doing so, it builds a foundation to further examine the impact of other IT trends such as mobile or cloud-based solutions on EA evolution. At a practical level, the research delivers a model that can be used to guide professionals to manage EA and continually evolve it.
Industry 4.0 (I4.0) is a technological development in the manufacturing industry that has revolutionized Total Quality Management (TQM) practices. There has been scant empirical research on the multidimensional perspective of TQM. Thus, this study aims to empirically examine the effect of the multidimensional view of TQM (soft and hard) on I4.0 readiness in small and medium-sized (SMEs) manufacturing firms. Based on the sociotechnical systems (STS) theory, a framework has been developed and validated empirically through an online survey of 209 Malaysian SMEs manufacturing firms. Unlike the existing TQM studies that used structural equation modeling (SEM), a two-stage analysis was performed in this study. First, the SEM approach was used to determine which variable significantly affects I4.0 readiness. Second, the artificial neural network (ANN) technique was adopted to rank the relative influence of significant predictors obtained from SEM. The results show that the soft and hard TQM practices have supported the I4.0 readiness. Moreover, the results highlight that hard TQM practices have mediating role between soft TQM practices and I4.0 readiness. The ANN results affirmed that customer focus is considered an important TQM factor for I4.0 managerial readiness, advanced manufacturing technology for operational readiness and top management commitment for technology readiness. In a nutshell, the SEM-ANN approach uniquely contributes to the TQM and I4.0 literature. Finally, the findings can help managers to prioritize firms’ soft and hard quality practices that promote I4.0 implementation, especially in emerging economies.
This paper investigates how Enterprise Architecture (EA) evolves due to emerging trends. It specifically explores how EA integrates the Service-oriented Architecture (SOA). Archer's Morphogenetic theory is used as an analytical approach to distinguish the architectural conditions under which SOA is introduced, to study the relationships between these conditions and SOA introduction, and to reflect on EA evolution (elaborations) that then take place. The paper focuses on reasons for why EA evolution could take place, or not and what architectural changes could happen due to SOA integration. The research builds on sound theoretical foundations to discuss EA evolution in a field that often lacks a solid theoretical groundwork. Specifically, it proposes that critical realism, using the morphogenetic theory, can provide a useful theoretical foundation to study enterprise architecture (EA) evolution. The initial results of a literature review (a-priori model) were extended using explorative interviews. The findings of this study are threefold. First, there are five different levels of EA-SOA integration outcomes. Second, a mature EA, flexible and well-defined EA framework and comprehensive objectives of EA improve the integration outcomes. Third, the analytical separation using Archer's theory is helpful in order to understand how these different integration outcomes are generated.
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