Components and Analysis Method of Enterprise Resource Planning (ERP) Requirements in Small and Medium Enterprises (SMEs)

With the fast development of information technologies and enterprise software, Enterprise Resource Planning (ERP) systems are increasingly adopted by more small and medium enterprises (SMEs). Based on this trend, it is necessary to develop ERP systems in a manner that meets and fits the SMEs requirements and needs. This paper proposes conceptual components of ERP requirements that are required for generating ERP system functions. In addition, it proposes an ERP requirements analysis method for ERP system developments in order to produce the proper ERP system functions for SMEs. The advantage of this analysis method is that it is easy to analyze and integrate the special requirements of the ERP development for distinguishing a sub-sector of SMEs. In this paper, by analyzing the components of requirements and the relationship of the business process modelling, several basic concepts are given and the method of the process analysis and modelling is also expressed

Protecting home agent client from IPv6 routing header vulnerability in mixed IP networks

Mixed IPv4/IPv6 networks will continue to use mobility support over tunneling mechanisms for a long period of time until the establishment of IPv6 end-to-end connectivity. Encapsulating IPv6 traffi c within IPv4 increases the level of hiding internal contents. Thus, mobility in mixed IPv4/IPv6 networks introduces new security vulnerabilities. One of the most critical vulnerabilities associated with the IPv6 protocol is the routing header that potentially may be used by attackers to bypass the network security devices. This paper proposes an algorithm (V6HAPA) for protecting home agent clients from the routing header vulnerability, considering that the home agents reside behind an IPv4 Network Address Translation (NAT) router. The experimental results show that the V6HAPA provides enough confidence to protect the home agent clients from attackers

A Machine Learning Approach to Detect Router Advertisement Flooding Attacks in Next-Generation IPv6 Networks

Router advertisement (RA) flooding attack aims to exhaust all node resources, such as CPU and memory, attached to routers on the same link. A biologically inspired machine learning-based approach is proposed in this study to detect RA flooding attacks. The proposed technique exploits information gain ratio (IGR) and principal component analysis (PCA) for feature selection and a support vector machine (SVM)-based predictor model, which can also detect input traffic anomaly. A real benchmark dataset obtained from National Advanced IPv6 Center of Excellence laboratory is used to evaluate the proposed technique. The evaluation process is conducted with two experiments. The first experiment investigates the effect of IGR and PCA feature selection methods to identify the most contributed features for the SVM training model. The second experiment evaluates the capability of SVM to detect RA flooding attacks. The results show that the proposed technique demonstrates excellent detection accuracy and is thus an effective choice for detecting RA flooding attacks. The main contribution of this study is identification of a set of new features that are related to RA flooding attack by utilizing IGR and PCA algorithms. The proposed technique in this paper can effectively detect the presence of RA flooding attack in IPv6 network

Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts

Offline Arabic Handwriting Recognition (OAHR) has recently become instrumental in the areas of pattern recognition and image processing due to its application in several fields, such as office automation and document processing. However, OAHR continues to face several challenges, including the high variability of the Arabic script and its intrinsic characteristics such as cursiveness, ligatures, and diacritics, the unlimited variation in human handwriting, and the lack of large public databases. In this paper, we have introduced a novel context-aware model based on deep neural networks to address the challenges of recognizing offline handwritten Arabic text, including isolated digits, characters, and words. Specifically, we have proposed a supervised Convolutional Neural Network (CNN) model that contextually extracts optimal features and employs batch normalization and dropout regularization parameters to prevent overfitting and further enhance its generalization performance when compared to conventional deep learning models. We employed numerous deep stacked-convolutional layers to design the proposed Deep CNN (DCNN) architecture. The proposed model was extensively evaluated, and it was observed to achieve excellent classification accuracy when compared to the existing state-of-the-art OAHR approaches on a diverse set of six benchmark databases, including MADBase (Digits), CMATERDB (Digits), HACDB (Characters), SUST-ALT (Digits), SUST-ALT (Characters), and SUST-ALT (Names). Further comparative experiments were conducted on the respective databases using the pre-trained VGGNet-19 and Mobile-Net models; additionally, generalization capabilities experiments on another language database (i.e., MNIST English Digits) were conducted, which showed the superiority of the proposed DCNN model

A Novel Hierarchical Extreme Machine-Learning-Based Approach for Linear Attenuation Coefficient Forecasting

The development of reinforced polymer composite materials has had a significant influence on the challenging problem of shielding against high-energy photons, particularly X-rays and γ-rays in industrial and healthcare facilities. Heavy materials’ shielding characteristics hold a lot of potential for bolstering concrete chunks. The mass attenuation coefficient is the main physical factor that is utilized to measure the narrow beam γ-ray attenuation of various combinations of magnetite and mineral powders with concrete. Data-driven machine learning approaches can be investigated to assess the gamma-ray shielding behavior of composites as an alternative to theoretical calculations, which are often time- and resource-intensive during workbench testing. We developed a dataset using magnetite and seventeen mineral powder combinations at different densities and water/cement ratios, exposed to photon energy ranging from 1 to 1006 kiloelectronvolt (KeV). The National Institute of Standards and Technology (NIST) photon cross-section database and software methodology (XCOM) was used to compute the concrete’s γ-ray shielding characteristics (LAC). The XCOM-calculated LACs and seventeen mineral powders were exploited using a range of machine learning (ML) regressors. The goal was to investigate whether the available dataset and XCOM-simulated LAC can be replicated using ML techniques in a data-driven approach. The minimum absolute error (MAE), root mean square error (RMSE), and R2score were employed to assess the performance of our proposed ML models, specifically a support vector machine (SVM), 1d-convolutional neural network (CNN), multi-Layer perceptrons (MLP), linear regressor, decision tree, hierarchical extreme machine learning (HELM), extreme learning machine (ELM), and random forest networks. Comparative results showed that our proposed HELM architecture outperformed state-of-the-art SVM, decision tree, polynomial regressor, random forest, MLP, CNN, and conventional ELM models. Stepwise regression and correlation analysis were further used to evaluate the forecasting capability of ML techniques compared to the benchmark XCOM approach. According to the statistical analysis, the HELM model showed strong consistency between XCOM and predicted LAC values. Additionally, the HELM model performed better in terms of accuracy than the other models used in this study, yielding the highest R2score and the lowest MAE and RMSE

Arabic Sentiment Analysis Based on Word Embeddings and Deep Learning

Social media networks have grown exponentially over the last two decades, providing the opportunity for users of the internet to communicate and exchange ideas on a variety of topics. The outcome is that opinion mining plays a crucial role in analyzing user opinions and applying these to guide choices, making it one of the most popular areas of research in the field of natural language processing. Despite the fact that several languages, including English, have been the subjects of several studies, not much has been conducted in the area of the Arabic language. The morphological complexities and various dialects of the language make semantic analysis particularly challenging. Moreover, the lack of accurate pre-processing tools and limited resources are constraining factors. This novel study was motivated by the accomplishments of deep learning algorithms and word embeddings in the field of English sentiment analysis. Extensive experiments were conducted based on supervised machine learning in which word embeddings were exploited to determine the sentiment of Arabic reviews. Three deep learning algorithms, convolutional neural networks (CNNs), long short-term memory (LSTM), and a hybrid CNN-LSTM, were introduced. The models used features learned by word embeddings such as Word2Vec and fastText rather than hand-crafted features. The models were tested using two benchmark Arabic datasets: Hotel Arabic Reviews Dataset (HARD) for hotel reviews and Large-Scale Arabic Book Reviews (LARB) for book reviews, with different setups. Comparative experiments utilized the three models with two-word embeddings and different setups of the datasets. The main novelty of this study is to explore the effectiveness of using various word embeddings and different setups of benchmark datasets relating to balance, imbalance, and binary and multi-classification aspects. Findings showed that the best results were obtained in most cases when applying the fastText word embedding using the HARD 2-imbalance dataset for all three proposed models: CNN, LSTM, and CNN-LSTM. Further, the proposed CNN model outperformed the LSTM and CNN-LSTM models for the benchmark HARD dataset by achieving 94.69%, 94.63%, and 94.54% accuracy with fastText, respectively. Although the worst results were obtained for the LABR 3-imbalance dataset using both Word2Vec and FastText, they still outperformed other researchers’ state-of-the-art outcomes applying the same dataset

PROTECTING HOME AGENT CLIENT FROM IPv6 ROUTING HEADER VULNERABILITY IN MIXED IP NETWORKS

Mixed IPv4/IPv6 networks will continue to use mobility support over tunneling mechanisms for a long period of time until the establishment of IPv6 end-to-end connectivity. Encapsulating IPv6 traffi c within IPv4 increases the level of hiding internal contents. Thus, mobility in mixed IPv4/IPv6 networks introduces new security vulnerabilities. One of the most critical vulnerabilities associated with the IPv6 protocol is the routing header that potentially may be used by attackers to bypass the network security devices. This paper proposes an algorithm (V6HAPA) for protecting home agent clients from the routing header vulnerability, considering that the home agents reside behind an IPv4 Network Address Translation (NAT) router. The experimental results show that the V6HAPA provides enough confidence to protect the home agent clients from attackers.

Cybersecurity Challenges and Implications for the Adoption of Cloud Computing and IoT: DDoS Attacks as an Example

Cloud computing and Internet of Things have expanded rapidly and dramatically all over the world. Cloud computing has emerged as a new paradigm for enabling broad access to distributed shared IT resources. Although, sharing IT resources is an essential cloud computing characteristic, it has brought many security challenges for both users and service providers. Security is one of the main issues why many users and enterprises are reluctant to migrate to cloud computing. Distributed Denial of Service (DDoS) attacks are one of such critical threats which are growing in the cloud space. In this paper, we present a comprehensive study of potential cloud computing security threats caused by DDoS attacks. Additionally, we discuss DDoS risks to cloud computing caused by Internet of Things devices, highlight the reasons behind these threats and analyse why this is overlooked by common security standards and frameworks. We also review cloud architectures and introduce a number of scenarios to enable researchers to better understand and mitigate DDoS attacks on enterprises. Finally, some further research directions addressing current security issues in cloud computing are outlined

Cybersecurity Challenges and Implications for the Adoption of Cloud Computing and IoT: DDoS Attacks as an Example

Cloud computing and Internet of Things have expanded rapidly and dramatically all over the world. Cloud computing has emerged as a new paradigm for enabling broad access to distributed shared IT resources. Although, sharing IT resources is an essential cloud computing characteristic, it has brought many security challenges for both users and service providers. Security is one of the main issues why many users and enterprises are reluctant to migrate to cloud computing. Distributed Denial of Service (DDoS) attacks are one of such critical threats which are growing in the cloud space. In this paper, we present a comprehensive study of potential cloud computing security threats caused by DDoS attacks. Additionally, we discuss DDoS risks to cloud computing caused by Internet of Things devices, highlight the reasons behind these threats and analyse why this is overlooked by common security standards and frameworks. We also review cloud architectures and introduce a number of scenarios to enable researchers to better understand and mitigate DDoS attacks on enterprises. Finally, some further research directions addressing current security issues in cloud computing are outlined

A Novel Hierarchical Extreme Machine-Learning-Based Approach for Linear Attenuation Coefficient Forecasting

The development of reinforced polymer composite materials has had a significant influence on the challenging problem of shielding against high-energy photons, particularly X-rays and γ-rays in industrial and healthcare facilities. Heavy materials’ shielding characteristics hold a lot of potential for bolstering concrete chunks. The mass attenuation coefficient is the main physical factor that is utilized to measure the narrow beam γ-ray attenuation of various combinations of magnetite and mineral powders with concrete. Data-driven machine learning approaches can be investigated to assess the gamma-ray shielding behavior of composites as an alternative to theoretical calculations, which are often time- and resource-intensive during workbench testing. We developed a dataset using magnetite and seventeen mineral powder combinations at different densities and water/cement ratios, exposed to photon energy ranging from 1 to 1006 kiloelectronvolt (KeV). The National Institute of Standards and Technology (NIST) photon cross-section database and software methodology (XCOM) was used to compute the concrete’s γ-ray shielding characteristics (LAC). The XCOM-calculated LACs and seventeen mineral powders were exploited using a range of machine learning (ML) regressors. The goal was to investigate whether the available dataset and XCOM-simulated LAC can be replicated using ML techniques in a data-driven approach. The minimum absolute error (MAE), root mean square error (RMSE), and R2score were employed to assess the performance of our proposed ML models, specifically a support vector machine (SVM), 1d-convolutional neural network (CNN), multi-Layer perceptrons (MLP), linear regressor, decision tree, hierarchical extreme machine learning (HELM), extreme learning machine (ELM), and random forest networks. Comparative results showed that our proposed HELM architecture outperformed state-of-the-art SVM, decision tree, polynomial regressor, random forest, MLP, CNN, and conventional ELM models. Stepwise regression and correlation analysis were further used to evaluate the forecasting capability of ML techniques compared to the benchmark XCOM approach. According to the statistical analysis, the HELM model showed strong consistency between XCOM and predicted LAC values. Additionally, the HELM model performed better in terms of accuracy than the other models used in this study, yielding the highest R2score and the lowest MAE and RMSE