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.

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The rapid development of smart technologies and data analytics empowers most industries to evolve their systems and introduce innovative applications. Consequently, smart metering technology, an internet of things-based application service, is diffusing rapidly in the energy sector. Regardless of its associated benefits, smart meters continue to struggle from consumers’ acceptance. To promote smart meters’ successful deployment, research is needed to better understand consumers’ acceptance of smart metering. Motivated by these concerns, a smart meter acceptance model is developed to evaluate the moderation role of experience and personal innovativeness factors among residential consumers. A cross-sectional research design was used in this study. Data were collected using a self-administrated questionnaire from 318 smart meters consumers who have had experience in using it. Hypothetical relationships were assessed and validated using partial least squares structural equation modelling. The empirical findings exert the moderating role of experience and personal innovativeness of smart meter acceptance that achieved an acceptable fit with the data, and specifically, five out of nine hypotheses were supported.

Today's increasing demand for electricity requires solutions that better align with energy demand and supply. Innovative technological solutions such as smart metering applications are gaining popularity among electricity providers. Despite numerous benefits, smart meters, a part of the technology on the Internet of Things (IoT), continue to struggle for widespread consumer acceptance due to limited knowledge on electricity savings and environmental awareness. These factors were examined in isolation and have not been theoretically incorporated or examined. Hence, this study investigates the factors that influence residential consumers' acceptance of smart meters by integrating electricity-saving knowledge and environmental awareness with the second generation of ''unified theory of acceptance and use of technology'' (UTAUT2). The literature revealed an important link between users' behavioural intention and users' use behaviour. Well-established theories of acceptance like ''technology acceptance model'' (TAM) and UTAUT, incorporate the behavioural intention variable in the nomological network of technology adoption determinants. This study highlighted the impact of users' behavioural intention on users' use behaviour, which was not examined previously by any of the smart meter acceptance models. The data were collected from 318 consumers of residential smart meters in Putrajaya and Malacca, the cities in Malaysia, and were statistically tested using SME-PLS The study confirms that adding electricity-saving knowledge and environmental awareness to the UTAUT2 leads to a significant increase in the explained variance in consumer acceptance of smart meter.