Mohamed elkholy | October 6 University (original) (raw)

Papers by Mohamed elkholy

Indonesian Journal of Electrical Engineering and Computer Science

Internet of things (IoT) systems collect large size of data from huge numbers of sensors. A wide ... more Internet of things (IoT) systems collect large size of data from huge numbers of sensors. A wide rage of IoT systems relies on cloud resources to process and analyze the collected data. However, passing large amount of data to the cloud affects the overall performance and cannot support real-time requirements. Serverless computing is a promising technique that allows developer to write an application code, in any programming language, and specify an event to start its execution. Thus, IoT system can get a good benefit of serverless environment. The proposed work introduces a framework to allow Serverless computing to take place on the Fog nodes near the data collectors. The proposed framework is implemented as an extension to a Kubernetes cluster that manages a set of Docker containers at the fog layer. A prototype of the proposed solution was implemented using Node.Js for coding and YAML files to transfer data. The proposed framework was evaluated against traditional cloud Serverle...

Journal of Information Security and Cybercrimes Research

Microservices architecture emerges as a promising software design approach that provides large sc... more Microservices architecture emerges as a promising software design approach that provides large scale software systems with flexibility, scalability and fault tolerance. Moreover, it is considered a suitable design to be implemented using software containers provided with several cloud providers. However, microservices suffer from several security challenges that hinder its progress. The concept of microservices is to break down the system functionality to a number of small coherent services. Hence, using microservices as a design approach increases the security risks by expanding the risk surface. In contrast to microservices, monolithic applications are implemented as a bulk of codes using single programming language. Such environment has several drawbacks related to flexibility and maintainability, but limits security issues. On the other hand, microservices implementation uses several programming languages and frameworks to implement small units of system functionality. Such envi...

Cloud computing has emerged as a flexible computing paradigm that reshaped the Information Techno... more Cloud computing has emerged as a flexible computing paradigm that reshaped the Information Technology map. However, cloud computing brought about a number of security challenges as a result of the physical distribution of computational resources and the limited control that users have over the physical storage. This situation raises many security challenges for data integrity and confidentiality as well as authentication and access control. This work proposes a security mechanism for data integrity that allows a data owner to be aware of any modification that takes place to his data. The data integrity mechanism is integrated with an extended Kerberos authentication that ensures authorized access control. The proposed mechanism protects data confidentiality even if data are stored on an untrusted storage. The proposed mechanism has been evaluated against different types of attacks and proved its efficiency to protect cloud data storage from different malicious attacks.

International Journal of Advanced Computer Science and Applications, 2022

Service Oriented Architecture (SOA) has emerged as a promising architectural style that provides ... more Service Oriented Architecture (SOA) has emerged as a promising architectural style that provides software applications with high level of flexibility and reusability. However, in several cases where legacy software components are wrapped to be used as web services the final solution does not completely satisfy the SOA aims of flexibility and reusability. The literature review and the industrial applications show that SOA lacks a formal definition and measurement for optimal granularity of web services. Indeed, wrapping several business functionalities as a coarse-grained web services lacks reusability and flexibility. On the other hand, a huge number of fine-grained web services results in a high coupling between services and large size messages transferred over the Internet. The main research question still concerns with "How to determine an optimal level of service granularity when wrapping business functionalities as web services?" This research proposes the Snowball framework as a promising approach to integrate and compose web services. The framework is made up three-step process. The process uses the rules in deciding the web services that have an optimal granularity that maintains the required performance. To demonstrate and evaluate the framework, we realized a car insurance application that was already implemented by a traditional approach. The results show the efficiency of snowball framework over other approaches.

Advances in Intelligent Systems and Computing, 2020

Cloud model covers the randomness gap in fuzzy logic model and represents the uncertainty transfo... more Cloud model covers the randomness gap in fuzzy logic model and represents the uncertainty transformation between two different concepts. First concept is the linguistic term that represent the qualitative mean. While the second is the crisp term which represent the quantitative mean. The proposed work presents promising model which combines cloud model, fuzzy time series, and Heikin-Ashi candlestick to predict and confirm accurate stock trend. The model solves several challenging such as: nonlinearity, uncertainty and noises in stock market trend. Heikin-Ashi Candlesticks are an extended branch of Japanese candlesticks, such candlestick filters out stock noise and effort to highlight the trend. Heikin-Ashi Candlestick is constructed by calculating averages of the previous and current period prices. Cloud model handle the ambiguous and uncertainty in the Japanese candlestick definitions (qualitative information) and actual stock prices (quantitative data). It is applied to build membership functions by handling the uncertainty and vagueness of the stock historical data. Then the suggested model constructs dynamic weighted fuzzy logical relationships based on the membership functions to predict the next open and close prices of the stock as well as the high and low values. Finally it constructs the next Heikin-Ashi Japanese candlestick pattern that clarify the trend direction based on the patterns sequence. The imperial evaluation proves that the proposed model has high forecasting accuracy and is feasible to be implemented.

FRWSC: a framework for robust Web service composition

The deployment of Web services in a highly dynamic environment brings about a number of research ... more The deployment of Web services in a highly dynamic environment brings about a number of research challenges. In dynamic Web services composition, failures and changes to atomic services cannot be detected before invocation. Hence, the failure or even the change in an atomic service may lead to the overall failure of the composite service. In addition, SOAP error code is not sufficient for the client to analyze the failure reason and handle it. In this work, we introduce a framework to deal with unexpected failures during runtime composition. The proposed framework is built on top of composite services stack as an interface between the composite service and its external service partners. The evaluation results show that by using the proposed framework, it is possible to avoid composite service failures that are caused by changes or failures in atomic services.

A Knowledge based Approach for Semantic Web Services Discovery, 2016

Ever since the introduction of the service oriented model of computing (SOA), service discovery h... more Ever since the introduction of the service oriented model of computing (SOA), service discovery has been the major research challenge in SOA. Service consumers usually prefer to express their requirements informally. Expressing requirements in such a way leads to difficulties in the matching procedure, and hence results in poor matching results. In this paper, we present the concept of multi-level search as a solution for matching informal expression of user requirements. In the suggested approach, intermediate brokers receive service requests and suggest suitable services that match the given requests. We present a mechanism by which an intelligent broker utilizes a knowledge based system to overcome the drawbacks of syntactic and semantic discovery. The intelligent broker receives informal user requirements and performs multi-level search. The search starts with key word search, then meaning search, and finally expert search. If the keyword search fails to produce a proper matching, then, the search progresses to the following levels: semantic, and then intelligent search. In this paper we argue that multi-level search could revive the dream of automatic service discovery and present a detailed model for implementation.

A Framework for Providing Augmented Reality as a Service Provided by Cloud Computing for E-Learning, 2022

The main objective of the proposed study is to develop an e-learning system using augmented reali... more The main objective of the proposed study is to develop an e-learning system using augmented reality technology one of the main problems faces using AR in education is the huge computational power needed to transfer 2D animation to enrich learning facilities. Such problem increases when using smart mobile devices that suffer from hardware limitation. A promising framework is used to utilize cloud services to support augmented reality applications on the cloud. Such method significantly reduces consumption of memory and processing units when dealing with large size videos or images. Hence the augmented reality processing is speeded up to meet the requirements of E-learning systems. The proposed work was conducted on 100 students from different academic levels in the first semester of the year 2022. Three experiments were conducted for different fields of education including two-dimensional images using Unity Program (3D Software) to draw 3D objects and Vufoira software development kit. The experimental results showed promising results as the application has the flexibility to work on different platforms. Moreover the consumed memory to run the application is reduced significantly. The results also showed high performance for the application when drawing complex 3D images and when dealing with different animations. The study supported with a detailed questioner that proofs the importance of AR in the field of E-learning.

Deep learning-based classification of eye diseases using Convolutional Neural Network for OCT images, 2024

Deep learning shows promising results in extracting useful information from medical images. The p... more Deep learning shows promising results in extracting useful information from medical images. The proposed work applies a Convolutional Neural Network (CNN) on retinal images to extract features that allow early detection of ophthalmic diseases. Early disease diagnosis is critical to retinal treatment. Any damage that occurs to retinal tissues that cannot be recovered can result in permanent degradation or even complete loss of sight. The proposed deep-learning algorithm detects three di erent diseases from features extracted from Optical Coherence Tomography (OCT) images. The deeplearning algorithm uses CNN to classify OCT images into four categories. The four categories are Normal retina, Diabetic Macular Edema (DME), Choroidal Neovascular Membranes (CNM), and Age-related Macular Degeneration (AMD). The proposed work uses publicly available OCT retinal images as a dataset. The experimental results show significant enhancement in classification accuracy while detecting the features of the three listed diseases.

The openness environment of IoT ecosystem arises several security and privacy issues. However, th... more The openness environment of IoT ecosystem arises several security and privacy issues. However, the huge amount of data produced by several IoT devices restricts using traditional security methods. Another security challenge for IoT system is the interoperability between heterogeneous IoT devices. Semantic Web has risen as a promising technology that provides semantic annotations allowing interoperability between IoT devices. Semantic web uses RDF triples to allow semantic data exchange between heterogeneous applications. Hence, RDF files used in IoT systems require specific security mechanism that regards large data size as well as rapidly data updates. The proposed work introduces a security novel that provides RDF files with a fine grained partial encryption. The proposed method allows applying security for the sensitive parts of RDF files without affecting the public parts. Encryption metadata is stored in a container related to each individual sensitive triple. Thus accessing public data in RDF file is not affected with the encryption overheads. A motivation scenario for privacy in a smart city is used to evaluate the proposed method. Experimental results showed that the proposed methodology enhances the access time of RDF triples from 10.4 msec to 6.2 msec. Moreover the proposed method facilitates integration of separated parts of a RDF graph together. The empirical evaluation proved the enhancement in efficiency and flexibility by applying the proposed method to RDF files used in IoT systems. Moreover the insensitive triples in RDF files are not affected with the security overheads.

Deep Image: An Efficient Image-Based Deep Conventional Neural Network Method for Android Malware Detection, 2023

The continuous increment of malware and its complexity motivated researchers to implement techniq... more The continuous increment of malware and its complexity motivated researchers to implement techniques to detect and classify it. Manual detection of malicious files is time consuming and shows poor results. Recently, Deep Convolution Neural Networks (DCNN) shows promising results in malware detection. DCNNs include large number of fully connected layers that are capable to deal with fast iterations of Android malware. Compared to the existing approach, DCNN shows high performance and accuracy in detecting different types of malwares. The proposed work combines Scale-Invariant Feature Transform (SIFT) and DCNN to detect malware features. Combining SIFT with DCNN allow higher accuracy of features classification and overcome the problem of single-feature extraction. The proposed method is compared to existing approaches to malware detection in terms of anticipated time and detection accuracy. The experimental results showed the significant enhancement offered by the proposed work in terms of accuracy and performance.

Light weight Serverless Computing at Fog Nodes for Internet of Things Systems, 2022

Internets of Things (IoT) systems collect large size of data from huge numbers of sensors. A wide... more Internets of Things (IoT) systems collect large size of data from huge numbers of sensors. A wide rage of IoT systems relies on cloud resources to process and analyze the collected data. However, passing large amount of data to the cloud affects the overall performance and cannot support realtime requirements. Serverless computing is a promising technique that allows developer to write an application code, in any programming language, and specify an event to start its execution. Thus IoT system can get a good benefit of Serverless environment. The proposed work introduces a framework to allow Serverless computing to take place on the Fog nodes near the data collectors. The proposed framework is implemented as an extension to a Kubernetes cluster that manages a set of Docker containers at the fog layer. A prototype of the proposed solution was implemented using Node.Js for coding and YAML files to transfer data. The proposed framework was evaluated against traditional cloud Serverless execution. The experimental results proved the significant enhancement of the framework by dcreasing the respond time especially for data intensive IoT applications.

Snowball Framework for Web Service Composition in SOA Applications

Although traditional image classification techniques are often used in authentic ways, they have ... more Although traditional image classification techniques are often used in authentic ways, they have several drawbacks, such as unsatisfactory results, poor classification accuracy, and a lack of flexibility. In this study, we introduce a combination of convolutional neural network (CNN) and support vector machine (SVM), along with a modified bag of visual words (BoVW)-based image classification model. BoVW uses scale-invariant feature transform (SIFT) and Oriented Fast and Rotated BRIEF (ORB) descriptors; as a consequence, the SIFT-ORB-BoVW model developed contains highly discriminating features, which enhance the performance of the classifier. To identify appropriate images and overcome challenges, we have also explored the possibility of utilizing a fuzzy Bag of Visual Words (BoVW) approach. This study also discusses using CNNs/SVM to improve the proposed feature extractor's ability to learn more relevant visual vocabulary from the image. The proposed technique was compared with classic BoVW. The experimental results proved the significant enhancement of the proposed technique in terms of performance and accuracy over state-of-the-art models of BoVW.

Predicting Stock Market Trends for Japanese Candlestick Using Cloud Model, 2021

Combining bag of visual words-based features with CNN in image classification, 2023