In the last few decades, the concrete industry has been massively expanded with the adoption of various kinds of binding materials. As a substitute to cement and in an effort to relieve ecofriendly difficulties linked with cement creation, the utilization of industrial waste as cementitious material can sharply reduce the amount of trash disposed of in lakes and landfills. With respect to the mechanical properties, durability and thermal behavior, ground-granulated blast-furnace slag (GGBS) delineates a rational way to develop sustainable cement and concrete. Apart from environmental benefits, the replacement of cement by GGBS illustrates an adequate way to mitigate the economic impact. Although many researchers concentrate on utilizing GGBS in concrete production, knowledge is scattered, and additional research is needed to better understand relationships among a wide spectrum of key questions and to more accurately determine these preliminary findings. This work aims to shed some light on the scientific literature focusing on the use and effectiveness of GGBS as an alternative to cement. First and foremost, basic information on GGBS manufacturing and its physical, chemical and hydraulic activity and heat of hydration are thoroughly discussed. In a following step, fresh concrete properties, such as flowability and mechanical strength, are examined. Furthermore, the durability of concrete, such as density, permeability, acid resistance, carbonation depth and dry shrinkage, are also reviewed and interpreted. It can be deduced that the chemical structure of GGBS is parallel to that of cement, as it shows the creditability of being partially integrated and overall suggests an alternative to Ordinary Portland Cement (OPC). On the basis of such adjustments, the mechanical strength of concrete with GGBS has shown an increase, to a certain degree; however, the flowability of concrete has been reduced. In addition, the durability of concrete containing GGBS cement is shown to be superior. The optimum percentage of GGBS is an essential aspect of better performance. Previous studies have suggested different optimum percentages of GGBS varying from 10 to 20%, depending on the source of GGBS, concrete mix design and particle size of GGBS. Finally, the review also presents some basic process improvement tips for future generations to use GGBS in concrete.

In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices in IoT networks can be problematic due to their resource-constrained nature, and integrating security on these devices is often overlooked. This has resulted in attackers having an increased incentive to target IoT devices. As the number of attacks possible on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS. In this work, ML algorithms are compared for both binary and multi-class classification on Bot-IoT dataset. Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms. In the case of HTTP distributed denial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, other simulation results-based precision, recall, F1 score, and log loss metric reveal that RF outperforms on all types of attacks in binary classification. However, in multi-class classification, KNN outperforms other ML algorithms with an accuracy of 99%, which is 4% higher than RF.

The domain of Internet of Things (IoT) has witnessed immense adaptability over the last few years by drastically transforming human lives to automate their ordinary daily tasks. This is achieved by interconnecting heterogeneous physical devices with different functionalities. Consequently, the rate of cyber threats has also been raised with the expansion of IoT networks which puts data integrity and stability on stake. In order to secure data from misuse and unusual attempts, several intrusion detection systems (IDSs) have been proposed to detect the malicious activities on the basis of predefined attack patterns. The rapid increase in such kind of attacks requires improvements in the existing IDS. Machine learning has become the key solution to improve intrusion detection systems. In this study, an ensemble-based intrusion detection model has been proposed. In the proposed model, logistic regression, naive Bayes, and decision tree have been deployed with voting classifier after analyzing model’s performance with some prominent existing state-of-the-art techniques. Moreover, the effectiveness of the proposed model has been analyzed using CICIDS2017 dataset. The results illustrate significant improvement in terms of accuracy as compared to existing models in terms of both binary and multi-class classification scenarios.

The Industrial Internet of Things (IIoT) brings together many sensors, machines, industrial applications, databases, services, and people at work. The IIoT is improving our lives in several ways including smarter cities, agriculture, and e-healthcare, etc. Although the IIoT shares several characteristics with the consumer IoT, different cybersecurity mechanisms are adopted for both networks. Unlike consumer IoT solutions that are used by an individual user for a single purpose, IIoT solutions tend to be integrated into larger operational systems. As a result, IIoT security solutions require additional planning and awareness to ensure the security and privacy of the system. In this paper, different cybersecurity attacks such as denial of service (DoS), malicious operation, malicious control, data type probing, spying, scan, and wrong setup are predicted by applying machine learning techniques. To predict the aforementioned attacks, a novel lightweight random neural network (RaNN)-based prediction model has been proposed in this article. To investigate the performance of the RaNN-based prediction model, several evaluation parameters such as accuracy, precision, recall, and F1 score were calculated and compared with the traditional artificial neural network (ANN), support vector machine (SVM) and decision tree (DT). The evaluation results show that the proposed RaNN model achieves an accuracy of 99.20% for a learning rate of 0.01, with a prediction time of 34.51 milliseconds. Other performance parameters such as the precision, recall, and F1 score were 99.11%, 99.13%, and 99.20%, respectively. The proposed scheme improves the attack detection accuracy by an average of 5.65% compared to that of state-of-the-art machine learning schemes for IoT security.