Badji Mokhtar-Annaba University

In this paper, we propose a new comprehensive realistic cyber security dataset of IoT and IIoT applications, called Edge-IIoTset, which can be used by machine learning-based intrusion detection systems in two different modes, namely, centralized and federated learning. Specifically, the dataset has been generated using a purpose-built IoT/IIoT testbed with a large representative set of devices, sensors, protocols and cloud/edge configurations. The IoT data are generated from various IoT devices (more than 10 types) such as Low-cost digital sensors for sensing temperature and humidity, Ultrasonic sensor, Water level detection sensor, pH Sensor Meter, Soil Moisture sensor, Heart Rate Sensor, Flame Sensor, etc.). Furthermore, we identify and analyze fourteen attacks related to IoT and IIoT connectivity protocols, which are categorized into five threats, including, DoS/DDoS attacks, Information gathering, Man in the middle attacks, Injection attacks, and Malware attacks. In addition, we extract features obtained from different sources, including alerts, system resources, logs, network traffic, and propose new 61 features with high correlations from 1176 found features. After processing and analyzing the proposed realistic cyber security dataset, we provide a primary exploratory data analysis and evaluate the performance of machine learning approaches (i.e., traditional machine learning as well as deep learning) in both centralized and federated learning modes. The Edge-IIoTset dataset can be publicly accessed from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://ieee-dataport.org/8939</uri> .

This paper presents a comprehensive review of emerging technologies for the internet of things (IoT)-based smart agriculture. We begin by summarizing the existing surveys and describing emergent technologies for the agricultural IoT, such as unmanned aerial vehicles, wireless technologies, open-source IoT platforms, software defined networking (SDN), network function virtualization (NFV) technologies, cloud/fog computing, and middleware platforms. We also provide a classification of IoT applications for smart agriculture into seven categories: including smart monitoring, smart water management, agrochemicals applications, disease management, smart harvesting, supply chain management, and smart agricultural practices. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward supply chain management based on the blockchain technology for agricultural IoTs. Furthermore, we present real projects that use most of the aforementioned technologies, which demonstrate their great performance in the field of smart agriculture. Finally, we highlight open research challenges and discuss possible future research directions for agricultural IoTs.

Fog computing paradigm extends the storage, networking, and computing facilities of the cloud computing toward the edge of the networks while offloading the cloud data centers and reducing service latency to the end users. However, the characteristics of fog computing arise new security and privacy challenges. The existing security and privacy measurements for cloud computing cannot be directly applied to the fog computing due to its features, such as mobility, heterogeneity, and large-scale geo-distribution. This paper provides an overview of existing security and privacy concerns, particularly for the fog computing. Afterward, this survey highlights ongoing research effort, open challenges, and research trends in privacy and security issues for fog computing.

This book contains a selection of refereed and revised papers originally presented at the 5th ICLC. After an introduction by the editors, the book opens with a long-needed chapter on historical precedents for the Cognitive Linguistic theory of metaphor. Two chapters demonstrate the method of lexical analysis of linguistic metaphors and how it can be fruitfully applied to a characterization of the conceptual domains of smell and economics. Three chapters deal with theoretical aspects of conceptual metaphor, one of which is a commissioned chapter on the relation between conceptual metaphor theory and conceptual blending. Finally there are five chapters presenting novel theoretical issues and empirical findings about the relation between conceptual metaphor and culture. This book is hence a wide-ranging sample of current approaches to metaphor in Cognitive Linguistics, with some chapters breaking new grounds for future research.

The widespread use of artificial nestboxes has led to significant advances in our knowledge of the ecology, behaviour and physiology of cavity nesting birds, especially small passerines. Nestboxes have made it easier to perform routine monitoring and experimental manipulation of eggs or nestlings, and also repeatedly to capture, identify and manipulate the parents. However, when comparing results across study sites the use of nestboxes may also introduce a potentially significant confounding variable in the form of differences in nestbox design amongst studies, such as their physical dimensions, placement height, and the way in which they are constructed and maintained. However, the use of nestboxes may also introduce an unconsidered and potentially significant confounding variable due to differences in nestbox design amongst studies, such as their physical dimensions, placement height, and the way in which they are constructed and maintained. Here we review to what extent the characteristics of artificial nestboxes (e.g. size, shape, construction material, colour) are documented in the ‘methods’ sections of publications involving hole-nesting passerine birds using natural or excavated cavities or artificial nestboxes for reproduction and roosting. Despite explicit previous recommendations that authors describe in detail the characteristics of the nestboxes used, we found that the description of nestbox characteristics in most recent publications remains poor and insufficient. We therefore list the types of descriptive data that should be included in the methods sections of relevant manuscripts and justify this by discussing how variation in nestbox characteristics can affect or confound conclusions from nestbox studies. We also propose several recommendations to improve the reliability and usefulness of research based on long-term studies of any secondary hole-nesting species using artificial nestboxes for breeding or roosting.

In this article, we present a comprehensive study with an experimental analysis of federated deep learning approaches for cyber security in the Internet of Things (IoT) applications. Specifically, we first provide a review of the federated learning-based security and privacy systems for several types of IoT applications, including, Industrial IoT, Edge Computing, Internet of Drones, Internet of Healthcare Things, Internet of Vehicles, etc. Second, the use of federated learning with blockchain and malware/intrusion detection systems for IoT applications is discussed. Then, we review the vulnerabilities in federated learning-based security and privacy systems. Finally, we provide an experimental analysis of federated deep learning with three deep learning approaches, namely, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Deep Neural Network (DNN). For each deep learning model, we study the performance of centralized and federated learning under three new real IoT traffic datasets, namely, the Bot-IoT dataset, the MQTTset dataset, and the TON_IoT dataset. The goal of this article is to provide important information on federated deep learning approaches with emerging technologies for cyber security. In addition, it demonstrates that federated deep learning approaches outperform the classic/centralized versions of machine learning (non-federated learning) in assuring the privacy of IoT device data and provide the higher accuracy in detecting attacks.

In this paper, a comprehensive survey of authentication protocols for Internet of Things (IoT) is presented. Specifically more than forty authentication protocols developed for or applied in the context of the IoT are selected and examined in detail. These protocols are categorized based on the target environment: (1) Machine to Machine Communications (M2M), (2) Internet of Vehicles (IoV), (3) Internet of Energy (IoE), and (4) Internet of Sensors (IoS). Threat models, countermeasures, and formal security verification techniques used in authentication protocols for the IoT are presented. In addition a taxonomy and comparison of authentication protocols that are developed for the IoT in terms of network model, specific security goals, main processes, computation complexity, and communication overhead are provided. Based on the current survey, open issues are identified and future research directions are proposed.

Summary 1. We introduce a novel method that analyses environmental filtering of plant species in a geographic and phylogenetic context. By connecting species traits with phylogeny, traits with environment, and environment with geography, this comprehensive approach partitions the ecological and evolutionary processes that influence community assembly. 2. Our analysis extends RLQ ordination, which connects site attributes in matrix R (here environmental variables and spatial positions) with species attributes in matrix Q (here biological traits and phylogenetic positions), through the composition of sites in terms of species presences or abundances (matrix L ). This methodology, which explores and identifies environmental filters that organize communities, was developed to answer four questions: which combinations of trait states are filtered by the environment, which lineages are affected by these filters, which environmental variables contribute to the assemblage of local communities and where do these filters act? 3. At La Mafragh in north‐eastern Algeria, our approach shows that plant species traits were distributed according to environmental filters associated with a salinity gradient. Traits associated with the salinity gradient were convergent among Juncaceae, Cyperaceae and Amaranthaceae. The observed phylogenetic and trait patterns were related to how species survived the xeric season. Juncaceae and Cyperaceae, being perennials and anemogamous, tolerate the xeric hot season by restricting their range to the humid centre of the study area (where conditions are close to a subtropical climate). Several Amaranthaceae species co‐occur with the Juncaceae and Cyperaceae in two areas with the highest salinity. Most dicots were observed at higher elevations (up to 7 m a.s.l.), had hairy structures that can retain water and reflect solar radiation and were mostly annual or biennial, completing their life cycle before the onset of the xeric season. 4. Synthesis . Our methodology describes environmental filters in terms of identified combinations of traits and environmental factors. It allows spatial and phylogenetic signals to be determined by identifying convergent and conserved patterns in the evolution of traits and spatial scales that structured the environment. Our statistical framework is generic and can be readily extended to a wide range of exciting issues, such as host‐parasite, plant‐pollinator and predator–prey interactions.

Central events of ultrasonic action are the bubbles of cavitation that can be considered as powered microreactors within which high-energy chemistry occurs. This work presents the results of a comprehensive numerical assessment of frequency and saturating gases effects on single bubble sonochemistry. Computer simulations of chemical reactions occurring inside a bubble oscillating in liquid water irradiated by an ultrasonic wave have been performed for a wide range of ultrasonic frequencies (213-1100kHz) under different saturating gases (O2, air, N2 and H2). For O2 and H2 bubbles, reactions mechanism consisting in 25 reversible chemical reactions were proposed for studying the internal bubble-chemistry whereas 73 reversible reactions were taken into account for air and N2 bubbles. The numerical simulations have indicated that radicals such as OH, H, HO2 and O are created in the bubble during the strong collapse. In all cases, hydroxyl radical (OH) is the main oxidant created in the bubble. The production rate of the oxidants decreases as the driving ultrasonic frequency increases. The production rate of OH radical followed the order O2>air>N2>H2 and the order becomes more remarkable at higher ultrasonic frequencies. The effect of ultrasonic frequency on single bubble sonochemistry was attributed to its significant impact on the cavitation process whereas the effects of gases were attributed to the nature of the chemistry produced in the bubble at the strong collapse. It was concluded that, in addition to the gas solubility, the nature of the internal bubble chemistry is another parameter of a paramount importance that controls the overall sonochemical activity in aqueous solutions.

Since ancient times, essential oils are recognized for their medicinal value and they are very interesting and powerful natural plant products. They continue to be of paramount importance until the present day. Essential oils have been used as perfumes, flavors for foods and beverages, or to heal both body and mind for thousands of years (Baris et al., 2006; Margaris et al., 1982; Tisserand, 1997; Wei & Shibamoto 2010). Record findings in Mesopotamia, China, India, Persia and ancient Egypt show their uses for many treatments in various forms. For example, in the ancient Egypt, the population extracted oils by infusion. Later; Greeks and Romans used distillation and thus gave aromatic plants an additional value. With the advent of Islamic civilization, extraction techniques have been further refined. In the era of the Renaissance, Europeans have taken over the task and with the development of science the composition and the nature of essential oils have been well established and studied (Burt, 2004; Peeyush et al., 2011; Steven, 2010; Suaib et al., 2007). Nowadays, peppermint, lavender, geranium, eucalyptus, rose, bergamot, sandalwood and chamomile essential oils are the most frequently traded ones.

The emergence and global spread of carbapenemase-producing Enterobacteriaceae and Acinetobacter baumannii are of great concern to health services worldwide. These β-lactamases hydrolyse almost all β-lactams, are plasmid-encoded, and are easily transferable among bacterial species. They are mostly of the KPC, VIM, IMP, NDM, and OXA-48 types. Their current extensive spread worldwide in Enterobacteriaceae is an important source of concern. Infections caused by these bacteria have limited treatment options and have been associated with high mortality rates. Carbapenemase producers are mainly identified among Klebsiella pneumoniae, Escherichia coli, and A. baumannii and still mostly in hospital settings and rarely in the community. The Mediterranean region is of interest due to a great diversity and population mixing. The prevalence of carbapenemases is particularly high, with this area constituting one of the most important reservoirs. The types of carbapenemase vary among countries, partially depending on the population exchange relationship between the regions and the possible reservoirs of each carbapenemase. This review described the epidemiology of carbapenemases produced by enterobacteria and A. baumannii in this part of the world highlighting the worrisome situation and the need to screen and detect these enzymes to prevent and control their dissemination.

Potato peel (PP) was used as a biosorbent to remove malachite green (MG) from aqueous solution under various operating conditions. The effect of the experimental parameters such as initial dye concentration, biosorbent dose, initial pH, stirring speed, temperature, ionic strength and biosorbent particle size was investigated through a number of batch sorption experiments. The sorption kinetic uptake for MG by PP at various initial dye concentrations was analyzed by non-linear method using pseudo-first, pseudo-second and pseudo-nth order models. It was found that the pseudo-nth order kinetic model was the best applicable model to describe the sorption kinetic data and the order n of sorption reaction was calculated in the range from 0.71 to 2.71. Three sorption isotherms namely the Langmuir, Freundlich and Redlich–Peterson isotherms in their non-linear forms were applied to the biosorption equilibrium data. Both the Langmuir and Redlich–Peterson models were found to fit the sorption isotherm data well, but the Redlich–Peterson model was better. Thermodynamic parameters show that the sorption process of MG is endothermic and more effective process at high temperatures. The results revealed that PP is very effective for the biosorption of MG from aqueous solutions.

Handling the internal parametric variations and the nonlinearities of the high rated wind energy conversion system (WECS) is remained among the main challenges to maximize the produced energy, ameliorate its quality and ensure its efficient integration on the grid. In this context, a robust integral sliding mode control (ISMC) with Lyapunov function is proposed to control the active and reactive powers of a doubly fed induction generator (DFIG) based wind turbine, and to assure high dynamic performances according to the wind speed variation. To operate around an optimal rotational speed, a robust MPPT algorithm with mechanical speed control based on artificial Neural Network Controller (ANNC) is presented in order to extract the maximum power. Thereafter, the robust integral SMC are replaced by Field Oriented Control (FOC_PI) for comparative purposes. The objective is to prove the best performances of the system obtained by the proposed control method in terms of the dynamic response, total harmonic distortion THD (%) of the injected current into the grid, the reference tracking ability, Overshoot (%), precision and robustness. The effectiveness and robustness of each control techniques has been implemented and tested under MATLAB/Simulink environment by using a 1.5 MW wind system model.

In this paper, we review and analyze intrusion detection systems for Agriculture 4.0 cyber security. Specifically, we present cyber security threats and evaluation metrics used in the performance evaluation of an intrusion detection system for Agriculture 4.0. Then, we evaluate intrusion detection systems according to emerging technologies, including, Cloud computing, Fog/Edge computing, Network virtualization, Autonomous tractors, Drones, Internet of Things, Industrial agriculture, and Smart Grids. Based on the machine learning technique used, we provide a comprehensive classification of intrusion detection systems in each emerging technology. Furthermore, we present public datasets, and the implementation frameworks applied in the performance evaluation of intrusion detection systems for Agriculture 4.0. Finally, we outline challenges and future research directions in cyber security intrusion detection for Agriculture 4.0.

In the present study the genetic structure of Dicentrarchus labrax (14 samples from the Mediterranean) was analysed at six microsatellite loci, in order to test the hypothesis that some enzymatic loci undergo selection between marine and lagoon habitat. Eight of the 14 samples were analysed at both microsatellite and allozyme markers. The analysis of the genetic variation among the Mediterranean samples showed that (i) &Fcirc;ST values obtained with the six microsatellite loci were much smaller than those obtained with the 28 allozymes and (ii) microsatellite loci seemed to reflect more the geographical proximity than an ecological one. Thirteen enzymatic loci exhibited moderate to high values compared with microsatellites. This was interpreted as evidence that these allozymes are non-neutral. However, only six loci seemed to be implicated in differentiation between marine and lagoon samples, the causes of selection being unknown for the others. A possible scenario of population dynamics of the sea bass between marine and lagoon habitat is suggested.

In order to evaluate and project the quality of groundwater utilized for irrigation in the Sahara aquifer in Algeria, this research employed irrigation water quality indices (IWQIs), artificial neural network (ANN) models, and Gradient Boosting Regression (GBR), alongside multivariate statistical analysis and a geographic information system (GIS), to assess and forecast the quality of groundwater used for irrigation in the Sahara aquifer in Algeria. Twenty-seven groundwater samples were examined using conventional analytical methods. The obtained physicochemical parameters for the collected groundwater samples showed that Ca2+ &gt; Mg2+ &gt; Na+ &gt; K+, and Cl− &gt; SO42− &gt; HCO3− &gt; NO3−, owing to the predominance of limestone, sandstone, and clay minerals under the effects of human activity, ion dissolution, rock weathering, and exchange processes, which indicate a Ca-Cl water type. For evaluating the quality of irrigation water, the IWQIs values such as irrigation water quality index (IWQI), sodium adsorption ratio (SAR), Kelly index (KI), sodium percentage (Na%), permeability index (PI), and magnesium hazard (MH) showed mean values of 47.17, 1.88, 0.25, 19.96, 41.18, and 27.87, respectively. For instance, the IWQI values revealed that 33% of samples were severely restricted for irrigation, while 67% of samples varied from moderate to high restriction for irrigation, indicating that crops that are moderately to highly hypersensitive to salt should be watered in soft soils without any compressed layers. Two-machine learning models were applied, i.e., the ANN and GBR for IWQI, and the ANN model, which surpassed the GBR model. The findings showed that ANN-2F had the highest correlation between IWQI and exceptional features, making it the most accurate prediction model. For example, this model has two qualities that are critical for the IWQI prediction. The outputs’ R2 values for the training and validation sets are 0.973 (RMSE = 2.492) and 0.958 (RMSE = 2.175), respectively. Finally, the application of physicochemical parameters and water quality indices supported by GIS methods, machine learning, and multivariate modeling is a useful and practical strategy for evaluating the quality and development of groundwater.

This study presents two machine learning models, namely, the light gradient boosting machine (LightGBM) and categorical boosting (CatBoost), for the first time for predicting flash flood susceptibility (FFS) in the Wadi System (Hurghada, Egypt). A flood inventory map with 445 flash flood sites was produced and randomly divided into two groups for training (70%) and testing (30%). Fourteen flood controlling factors were selected and evaluated for their relative importance in flood occurrence prediction. The performance of the two models was assessed using various indexes in comparison to the common random forest (RF) method. The results show areas under the receiver operating characteristic curves (AUROC) of above 97% for all models and that LightGBM outperforms other models in terms of classification metrics and processing time. The developed FFS maps demonstrate that highly populated areas are the most susceptible to flash floods. The present study proves that the employed algorithms (LightGBM and CatBoost) can be efficiently used for FFS mapping.

Phylogenetic analysis of mitochondrial DNA (mtDNA) performed in Western Mediterranean populations has shown that both shores share a common set of mtDNA haplogroups already found in Europe and the Middle East. Principal co-ordinates of genetic distances and principal components analyses based on the haplotype frequencies show that the main genetic difference is attributed to the higher frequency of sub-Saharan L haplogroups in NW Africa, showing some gene flow across the Sahara desert, with a major impact in the southern populations of NW Africa. The AMOVA demonstrates that SW European populations are highly homogeneous whereas NW African populations display a more heterogeneous genetic pattern, due to an east-west differentiation as a result of gene flow coming from the East. Despite the shared haplogroups found in both areas, the European V and the NW African U6 haplogroups reveal the traces of the Mediterranean Sea permeability to female migrations, and allowed for determination and quantification of the genetic contribution of both shores to the genetic landscape of the geographic area. Comparison of mtDNA data with autosomal markers and Y-chromosome lineages, analysed in the same populations, shows a congruent pattern, although female-mediated gene flow seems to have been more intense than male-mediated gene flow.

The distribution of the Whiskered Tern (Chlidonias hybridus) is scattered, numbers fluctuate and they are threatened in many regions. Its breeding ecology has only occasionally been studied so far. It is relatively common in some North African wetlands. This study was carried out on Lake Tonga, northern Algeria, south of El-Kala (3651’N; 0820’E) in 1996 and 1997, with 169 and 215 initiated clutches studied, respectively. Basic characteristics of the Whiskered Tern breeding ecology studied were: laying date, clutch size, hatchling number, nest size and shape and egg size. Hatching success differed significantly between 1996 and 1997, probably due to weather. Laying date did not influence breeding parameters. Clutch size and hatching success were correlated with size and shape of nests. Hatchability was also correlated with egg length (negatively) and mass (positively). Relationships between breeding characteristic in relation to weather are discussed.

We review the state of the art of privacypreserving schemes for ad hoc social networks including mobile social networks (MSNs) and vehicular social networks (VSNs). Specifically, we select and examine in-detail 33 privacy-preserving schemes developed for or applied in the context of ad hoc social networks. Based on novel schemes published between 2008 and 2016, we survey privacy preservation models including location privacy, identity privacy, anonymity, traceability, interest privacy, backward privacy, and content oriented privacy. Recent significant attacks of leaking privacy, countermeasures, and game theoretic approaches in VSNs and MSNs are summarized in the form of tables. In addition, an overview of recommendations for further research is provided. With this survey, readers can acquire a thorough understanding of research trends in privacy-preserving schemes for ad hoc social networks.