Recently, the cyberattack surface has expanded significantly due to the proliferation of Internet of Things (IoT) devices, and intrusion detection remains a vital component of modern network security. Traditional Intrusion Detection Systems (IDSs) often cannot detect complex and newly generated threats due to the heterogeneous characteristics and constrained computing capabilities of IoT devices. In this paper, we utilise ensemble learning techniques, specifically Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM), in combination with a Feed-Forward Neural Network (FFNN) to develop a hybrid IDS. To address class imbalance and complex feature interactions that can be managed by Gradient Boosting (GB) techniques, the proposed model combines the robustness of gradient boosting with that of deep learning in learning complex non-linear patterns in network traffic. The experimental results show the hybrid model achieves strong performance, such as an accuracy of 99.42%, precision of 98.43%, recall of 99.65%, F1-score of 99.04%, and Area Under the receiver operating characteristic Curve (AUC) of 0.9994. These findings indicate the effectiveness of the proposed hybrid IDS in securing the IoT from diverse online threats.

Attacks are growing more complex and dangerous as network capabilities improve at a rapid pace. Network intrusion detection is usually regarded as an efficient means of dealing with security attacks. Many ways have been presented, utilizing various strategies and focusing on different types of visitors. Anomaly-based network intrusion monitoring is an essential area of intrusion detection investigation and development. Despite extensive research on anomaly-based network detection, there is still a lack of comprehensive literature reviews covering current methodologies and datasets. Despite the substantial research into anomaly-based network intrusion detection algorithms, there is a dearth of a research evaluation of new methodologies and datasets. We explore and evaluate 50 highest publications on anomaly-based intrusion detection using an in-depth review of related literature techniques. Our work thoroughly explores the technological environment of the subject in order to help future research in this sector. Our examination is carried out from the relevant angles: application areas, data preprocessing and threat detection approaches, assessment measures, and datasets. We select unresolved research difficulties and underexplored research areas from every viewpoint recommendation of the study. Finally, we outline five potentially increased research areas for the future.

Nowadays, devices are connected across all areas, from intelligent buildings and smart cities to Industry 4.0 and smart healthcare. With the exponential growth of Internet of Things usage in our world, IoT security is still the biggest challenge for its deployment. The main goal of IoT security is to ensure the accessibility of services provided by an IoT environment, protect privacy, and confidentiality, and guarantee the safety of IoT users, infrastructures, data, and devices. Authentication, as the first line of defense against security threats, becomes the priority of everyone. It can either grant or deny users access to resources according to their legitimacy. As a result, studying and researching authentication issues within IoT is extremely important. As a result, studying and researching authentication issues within IoT is extremely important. This article presents a comparative study of recent research in IoT security; it provides an analysis of recent authentication protocols from 2019 to 2023 that cover several areas within IoT (such as smart cities, healthcare, and industry). This survey sought to provide an IoT security research summary, the biggest susceptibilities, and attacks, the appropriate technologies, and the most used simulators. It illustrates that the resistance of protocols against attacks, and their computational and communication cost are linked directly to the cryptography technique used to build it. Furthermore, it discusses the gaps in recent schemes and provides some future research directions.

Internet of Things (IoT) refers to the infrastructures that connect smart devices to the Internet, operating autonomously. This connectivity makes it possible to harvest vast quantities of data, creating new opportunities for the emergence of unprecedented knowledge. To ensure IoT securit, various approaches have been implemented, such as authentication, encoding, as well as devices to guarantee data integrity and availability. Among these approaches, Intrusion Detection Systems (IDS) is an actual security solution, whose performance can be enhanced by integrating various algorithms, including Machine Learning (ML) and Deep Learning (DL), enabling proactive and accurate detection of threats. This study proposes to optimize the performance of network IDS using an ensemble learning method based on a voting classification algorithm. By combining the strengths of three powerful algorithms, Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) to detect both normal behavior and different categories of attack. Our analysis focuses primarily on the NSL-KDD dataset, while also integrating the recent Edge-IIoT dataset, tailored to industrial IoT environments. Experimental results show significant enhancements on the Edge-IIoT and NSL-KDD datasets, reaching accuracy levels between 72% to 99%, with precision between 87% and 99%, while recall values and F1-scores are also between 72% and 99%, for both normal and attack detection. Despite the promising results of this study, it suffers from certain limitations, notably the use of specific datasets and the lack of evaluations in a variety of environments. Future work could include applying this model to various datasets and evaluating more advanced ensemble strategies, with the aim of further enhancing the effectiveness of IDS.

Industrial Internet of Things (IIoT) represents the expansion of the Internet of Things (IoT) in industrial sectors. It is designed to implicate embedded technologies in manufacturing fields to enhance their operations. However, IIoT involves some security vulnerabilities that are more damaging than those of IoT. Accordingly, Intrusion Detection Systems (IDSs) have been developed to forestall inevitable harmful intrusions. IDSs survey the environment to identify intrusions in real time. This study designs an intrusion detection model exploiting feature engineering and machine learning for IIoT security. We combine Isolation Forest (IF) with Pearson’s Correlation Coefficient (PCC) to reduce computational cost and prediction time. IF is exploited to detect and remove outliers from datasets. We apply PCC to choose the most appropriate features. PCC and IF are applied exchangeably (PCCIF and IFPCC). The Random Forest (RF) classifier is implemented to enhance IDS performances. For evaluation, we use the Bot-IoT and NF-UNSW-NB15-v2 datasets. RF-PCCIF and RF-IFPCC show noteworthy results with 99.98% and 99.99% Accuracy (ACC) and 6.18 s and 6.25 s prediction time on Bot-IoT, respectively. The two models also score 99.30% and 99.18% ACC and 6.71 s and 6.87 s prediction time on NF-UNSW-NB15-v2, respectively. Results prove that our designed model has several advantages and higher performance than related models.

Solar radiation is capable of producing heat, causing chemical reactions, or generating electricity. Thus, the amount of solar radiation at different times of the day must be determined to design and equip all solar systems. Moreover, it is necessary to have a thorough understanding of different solar radiation components, such as Direct Normal Irradiance (DNI), Diffuse Horizontal Irradiance (DHI), and Global Horizontal Irradiance (GHI). Unfortunately, measurements of solar radiation are not easily accessible for the majority of regions on the globe. This paper aims to develop a set of deep learning models through feature importance algorithms to predict the DNI data. The proposed models are based on historical data of meteorological parameters and solar radiation properties in a specific location of the region of Errachidia, Morocco, from January 1, 2017, to December 31, 2019, with an interval of 60 minutes. The findings demonstrated that feature selection approaches play a crucial role in forecasting of solar radiation accurately when compared with the available data.

Artificial Intelligence (AI) is based on algorithms that allow machines to make decisions for humans. This technology enhances the users’ experience in various ways. Several studies have been conducted in the field of education to solve the problem of student orientation and performance using various Machine Learning (ML) algorithms. The main goal of this article is to predict Moroccan students’ performance in the region of Guelmim Oued Noun using an intelligent system based on neural networks, one of the best data mining techniques that provided us with the best results.

Mechanization is a depollution activity, because it provides an energetic and ecological response to the problem of organic waste treatment. Through burning, biogas from mechanization reduces gas pollution from fermentation by a factor of 20. This study aims to better understand the influence of the seasons on the emitted biogas in the landfill of the city Mohammedia. The composition of the biogas that naturally emanates from the landfill has been continuously analyzed by our intelligent system, from different wells drilled in recent and old waste repositories. During the rainy season, the average production of methane, carbon dioxide, and oxygen and nitrogen are currently 56%, 32%, and 1%, respectively, compared to 51%, 31%, and 0.8%, respectively, for old waste. Hazards levels, potential fire, and explosion risks associated with biogas are lower than those of natural gases in most cases. For this reason a system is proposed to measure and monitor the biogas production of the landfill site remotely. Measurement results carried out at various sites of the landfill in the city of Mohammedia by the system show that the biogas contents present dangers and sanitary risks which are of another order.

Internet of Things (IoT) refers to a new extended network that enables to any object to be linked to the Internet in order to exchange data and to be controlled remotely. Nowadays, due to its multiple advantages, the IoT is useful in many areas like environment, water monitoring, industry, public security, medicine, and so on. For covering all spaces and operating correctly, the IoT benefits from advantages of other recent technologies, like radio frequency identification, wireless sensor networks, big data, and mobile network. However, despite of the integration of various things in one network and the exchange of data among heterogeneous sources, the security of user’s data is a central question. For this reason, the authentication of interconnected objects is received as an interested importance. In 2012, Ye et al. suggested a new authentication and key exchanging protocol for Internet of things devices. However, we have proved that their protocol cannot resist to various attacks. In this paper, we propose an enhanced authentication protocol for IoT. Furthermore, we present the comparative results between our proposed scheme and other related ones.