Intrusion Detection Framework for Industrial Internet of Things Using Software Defined Network

The Industrial Internet of Things (IIoT) refers to the employment of the Internet of Things in industrial management, where a substantial number of machines and devices are linked and synchronized with the help of software programs and third platforms to improve the overall productivity. The acquisition of the industrial IoT provides benefits that range from automation and optimization to eliminating manual processes and improving overall efficiencies, but security remains to be forethought. The absence of reliable security mechanisms and the magnitude of security features are significant obstacles to enhancing IIoT security. Over the last few years, alarming attacks have been witnessed utilizing the vulnerabilities of the IIoT network devices. Moreover, the attackers can also sink deep into the network by using the relationships amidst the vulnerabilities. Such network security threats cause industries and businesses to suffer financial losses, reputational damage, and theft of important information. This paper proposes an SDN-based framework using machine learning techniques for intrusion detection in an industrial IoT environment. SDN is an approach that enables the network to be centrally and intelligently controlled through software applications. In our framework, the SDN controller employs a machine-learning algorithm to monitor the behavior of industrial IoT devices and networks by analyzing traffic flow data and ultimately determining the flow rules for SDN switches. We use SVM and Decision Tree classification models to analyze our framework’s network intrusion and attack detection performance. The results indicate that the proposed framework can detect attacks in industrial IoT networks and devices with an accuracy of 99.7%

Website Phishing Detection Using Machine Learning Techniques

Phishing is a cybercrime that is constantly increasing in the recent years due to the increased use of the Internet and its applications. It is one of the most common types of social engineering that aims to disclose or steel users sensitive or personal information. In this paper, two main objectives are considered. The first is to identify the best classifier that can detect phishing among twenty-four different classifiers that represent six learning strategies. The second objective aims to identify the best feature selection method for websites phishing datasets. Using two datasets that are related to Phishing with different characteristics and considering eight evaluation metrics, the results revealed the superiority of RandomForest, FilteredClassifier, and J-48 classifiers in detecting phishing websites. Also, InfoGainAttributeEval method showed the best performance among the four considered feature selection methods

Spousal Concordance of Diabetes Mellitus among Women in Ajman, United Arab Emirates

Objectives: Spousal concordance is defined as similar behaviours and associated health statuses between spouses. This study aimed to identify the concordance of diabetes mellitus (DM) and related variables among genetically unrelated couples in Ajman, United Arab Emirates (UAE). Methods: This cross-sectional study included 270 married women attending either the Mushairef Health Center or the Gulf Medical College Hospital in Ajman between May and November 2012. A validated questionnaire was designed to determine sociodemographic characteristics and a history or family history of DM, hypertension, coronary artery disease or dyslipidaemia among the women and their husbands. The weight, height, body mass index, waist circumference, fasting blood sugar and glycated haemoglobin (HbA1c) levels of all women were measured. Results: Of the women, 39.3% of those with diabetic husbands and 39.9% of those with non-diabetic husbands were diabetic themselves (P >0.050). The prevalence of DM spousal concordance was 17.8%. A history of hypertension, coronary artery disease and dyslipidaemia was significantly more frequent among women whose husbands had a history of the same conditions (P = 0.001, 0.040 and 0.002, respectively). Spousal concordance of abnormal glycaemia among non-diabetic women with diabetic husbands was significant (P = 0.001). Having a diabetic husband (P = 0.006) and being obese (P = 0.009) were the only significant predictors of hyperglycaemia among non-diabetic women after controlling for confounding factors. Conclusion: There was significant concordance of abnormal glycaemia among non-diabetic women with diabetic husbands. The spouses of diabetic patients may therefore be a target population for regular hyperglycaemia and DM screening

Spousal Concordance of Diabetes Mellitus among Women in Ajman, United Arab Emirates

Objectives: Spousal concordance is defined as similar behaviours and associated health statuses between spouses. This study aimed to identify the concordance of diabetes mellitus (DM) and related variables among genetically unrelated couples in Ajman, United Arab Emirates (UAE). Methods: This cross-sectional study included 270 married women attending either the Mushairef Health Center or the Gulf Medical College Hospital in Ajman between May and November 2012. A validated questionnaire was designed to determine sociodemographic characteristics and a history or family history of DM, hypertension, coronary artery disease or dyslipidaemia among the women and their husbands. The weight, height, body mass index, waist circumference, fasting blood sugar and glycated haemoglobin (HbA1c) levels of all women were measured. Results: Of the women, 39.3% of those with diabetic husbands and 39.9% of those with non-diabetic husbands were diabetic themselves (P >0.050). The prevalence of DM spousal concordance was 17.8%. A history of hypertension, coronary artery disease and dyslipidaemia was significantly more frequent among women whose husbands had a history of the same conditions (P = 0.001, 0.040 and 0.002, respectively). Spousal concordance of abnormal glycaemia among non-diabetic women with diabetic husbands was significant (P = 0.001). Having a diabetic husband (P = 0.006) and being obese (P = 0.009) were the only significant predictors of hyperglycaemia among non-diabetic women after controlling for confounding factors. Conclusion: There was significant concordance of abnormal glycaemia among non-diabetic women with diabetic husbands. The spouses of diabetic patients may therefore be a target population for regular hyperglycaemia and DM screening

The Role of Business Intelligence adoption as a Mediator of Big Data Analytics in the Management of Outsourced Reverse Supply Chain Operations

The fluctuating and disorganized state of todays global markets is the result of several factors. COVID-19 is an illustration. Supply chain managers should re-evaluate their competitive strategy and leverage big data analytics in light of the rising volatility in demand and supply, rivalry among supply chain partners, and the requirement to deliver tailored goods and services (BDA). Supply chain firms require sophisticated BDA processes and procedures to provide useful insights from big data to better decision-making and supply chain operations, as many leaders in the sector have acknowledged the necessity for improving with data (SCO). This research gives theoretical justification for the influence that BDA has on SCO

Diagnosing failed distribution transformers using neural networks

An artificial neural networks (ANN) system was developed for distribution transformer's failure diagnosis. The diagnosis was based on the latest standards and expert experiences in this field. The ANN was trained utilizing backpropagation algorithm using a real (out of the field) data obtained from utilities distribution networks transformer's failures. The ANN consists of six individual ANN according to six important factors used to give certain outputs. These factors are: the age of the transformer, the weather condition, if there are any damaged bushings, if there are any damaged casing or enclosure, if there is oil leakage, and if there are any faults in the windings. The six ANNs are combined in one ANN to give all the outputs of the individual six ANNs. The developed ANN can be used to give recommended complete diagnosis for working transformers to avoid possible failures depending on their operating conditions. Good diagnosis accuracy is obtained with the proposed approach applied and with the analysis of the attainable result

Extreme 15N Depletion in Seagrasses

Seagrass beds form an important part of the coastal ecosystem in many parts of the world but are very sensitive to anthropogenic nutrient increases. In the last decades, stable isotopes have been used as tracers of anthropogenic nutrient sources and to distinguish these impacts from natural environmental change, as well as in the identification of food sources in isotopic food web reconstruction. Thus, it is important to establish the extent of natural variations on the stable isotope composition of seagrass, validating their ability to act as both tracers�of nutrients and food sources. Around the world, depending on the seagrass species and ecosystem, values of seagrass N normally vary from 0 to 8 ? ?15N. In this study, highly unusual seagrass N isotope values were observed on the east coast of Qatar, with significant spatial variation over a scale of a few metres, and with ?15N values ranging from +2.95 to ?12.39 ? within a single bay during March 2012. This pattern of variation was consistent over a period of a year although there was a seasonal effect on the seagrass ?15N values. Seagrass, water column and sediment nutrient profiles were not correlated with seagrass ?15N values and neither were longer-term indicators of nutrient limitation such as seagrass biomass and height. Sediment ?15N values were correlated with Halodule uninervis ?15N values and this, together with the small spatial scale of variation, suggest that localised sediment processes may be responsible for the extreme isotopic values. Consistent differences in sediment to plant 15N discrimination between seagrass species also suggest that species-specific nutrient uptake mechanisms contribute to the observed ?15N values. This study reports some of the most extreme, negative ?15N values ever noted for seagrass (as low as ?12.4 ?) and some of the most highly spatially variable (values varied over 15.4 ? in a relatively small area of only 655�ha). These results are widely relevant, as they demonstrate the need for adequate spatial and temporal sampling when working with N stable isotopes to identify food sources in food web studies or as tracers of anthropogenic nutrients.Scopu

Assessing the Moderating Effect of Innovation on the Relationship between Information Technology and Supply Chain Management: An Empirical Examination

This study examines how innovation (INN) influences the relationship between supply chain management and information technology in Jordan. 211 employees of Jordanian industrial enterprises who work in the Operations Department provided information for the study, which examines this subject. The findings indicate a close connection between information technology and supply chain management. Innovation also dramatically modifies the interaction between supply chain management and information technology. Management help may be the subject of future research

Genetic diversity of Ardi goat based on microsatellite analysis

The aim of this study was to analyze the genetic variability of Ardi goats found in the central regions of the kingdom of Saudi Arabia using 14 microsatellite markers. Allelic richness was considerably high in this population indicating high genetic polymorphism as expected heterozygozity was 0.675. Furthermore, the population showed deviation from Hardy-Weinberg equilibrium in seven loci. Mean polymorphic information content value was found to be 0.553. Inbreeding coefficient was 0.183 suggesting moderate level of inbreeding. There was also no-significant heterozygote excess on basis of different models of infinite allele. These tests along with the mode-shift test of Ardi goat indicated no bottleneck recently. Thus, it can be recommended that the Ardi genetic variability should be maintained for its unique genetic resources, and there is a scope for further improvement in productivity through an appropriate management and breeding program. In general, results of this study can be used to establish a base of national conservation strategy of Ardi goat population in Saudi Arabia.Key words: Ardi goat, genetic diversity, microsatellite markers, inbreeding, bottleneck

Evaluation of DNA polymorphism in the Red Sea Epinephelus species using 12s rRNA and inter simple sequence repeats

1197-1205The true phylogenetic relation among Epinephelus species is under debate. The present investigation was designed for evaluation of DNA polymorphism in some Red sea Epinephelus species using 12s rRNA and Inter Simple Sequence repeats. DNA polymorphism values were detected and evaluated within all estimated fishes. Both applied techniques revealed that E. malabaricus is closely related to E. summana. The distance value between E. chlorostigma and E. areolatus is lower than the distance value between E. chlorostigma and E. radiatus. The Serranidae evolutionary variations were evaluated comparatively with other ray-finned fishes belonging to four fish genera (Carangidae, Labridae, Mugilidae and Cichlidae) based on 12s rRNA sequence variations (obtained from NCBI). The developed DNA markers were reliably branched in the evaluated fishes. More molecular investigations using more spatial and temporal fish samples should be carried out in the future for exploring the true Epinephelus species biodiversity in the Red sea