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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Ahmed, alaa M. saeed, Mogeeb A. Hamood, Amr A. alazab, Abduljabar A. Ahmed, Kamel A. Computer Networks & Distributed Systems Al-saeed It Taiz University Faculty of Engineering Taiz Yemen Taiz University Al-saeed Faculty of Engineering & It Computer Networks & Distributed Systems Department Taiz Yemen

ISBN: (纸本)9798350305333

Android currently dominates the smartphone market, accounting for an impressive market share of over 70%. However, because of its widespread acceptance, mobile operating systems have become a prime target for bad actors looking to profit from them. Particularly Android has been subjected to an increasing barrage of malware assaults, including the infamous Android Banking Trojans. This study investigates the effectiveness of static analysis in locating Android banking malware in order to counter this threat. It does so by utilizing a wide range of features, including permissions, application programming interface (API) calls, opcodes, API packages, system commands, intents, strings, services, receivers, and activities. The study suggests using machine learning techniques to assess the detection of Android malware by utilizing various sets of classifiers in order to achieve this goal. The study also uses a feature selection approach to determine which features are most useful for telling malicious code apart from good code. 500 samples of malicious code and 500 samples of benign code make up the dataset that was used. The XGboost algorithm outperforms others in terms of accuracy, achieving an impressive accuracy value of 99.5% in malware detection after conducting a thorough comparison of various classifier sets. These results demonstrate the potential of static analysis and machine learning as useful tools in fending off the growing threats posed by Android malware. © 2023 IEEE.

关键词： Android malware

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Enhancing MANETs Security Against Black Hole Attacks through Time-Based Node Analysis in AOMDV Protocol

Enhancing MANETs Security Against Black Hole Attacks through...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Abdullah A. Naji Mogeeb A. saeed Ammar H. almagashi alaa M. Ahmed Hashem S. al-Atefi Mohammed N. Khaled Olayan M. Rashed Computer Network & Distributed Systems Department Al-Saeed Faculty Of Engineering &IT Taiz University Computer Networks & Distributed Systems Al-Saeed Faculty Of Engineering &IT Taiz University Computer Center & Information Technology Taiz University

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Mobile ad-hoc networks (MANETs) rely solely on direct node communication and function without a fixed infrastructure. Due to their decentralized design, they are susceptible to numerous security risks, with black hole attacks posing significant threats to routing integrity and confidentiality. This paper proposes a technique to enhance the Ad hoc On-Demand Multi-path Distance Vector (AOMDV) routing protocol's security against black hole attacks. The method involves a time-based reply message analysis to identify and mitigate malicious nodes. This algorithm improves the protocol's ability to detect and prevent black hole attacks by closely analyzing response time differences between malicious and normal nodes. The results of the simulation show that the suggested solution greatly enhances network performance by lowering the Routing Overhead Ratio from 1388.04% to 0.73% and the Packet Loss Rate from 74% to 11% while keeping a high Packet Delivery Ratio—up to 93% in different scenarios. These enhancements sustain strong data transmission in dynamic MANET environments, supporting network integrity and reliability.

关键词： Protocols Packet loss Routing Ad hoc networks Vectors Security Time factors

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Comparative Study of Static Analysis and Machine Learning Approaches for Detecting Android Banking Malware

Comparative Study of Static Analysis and Machine Learning Ap...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： alaa M. Ahmed Mogeeb A. saeed Amr A. Hamood Abduljabar A. alazab Kamel A. Ahmed Faculty of Engineering Computer Networks & Distributed Systems Al-saeed IT Taiz University Taiz Yemen Computer Networks & Distributed Systems department Al-saeed Faculty of Engineering & IT Taiz University Taiz Yemen

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Real-Time Diabetes Detection Using Machine Learning and Apache Spark

Real-Time Diabetes Detection Using Machine Learning and Apac...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Mohammed A. saeed Mogeeb A. saeed Dep. Al-Saeed Faculty of Engineering & IT Computer Networks & Distributed System Taiz University Yemen

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

The convergence of contemporary and cutting-edge technologies, including big data analytics and machine learning, is reshaping the scale and efficiency of healthcare systems. This work presents distinct approaches and contributions, addressing specific challenges in healthcare and disease and diabetes analytics. The real-time healthcare for disease diabetes and the integration of big data analytics, machine learning, and real-time processing have paved the way for innovative solutions to address disease diabetes prediction and monitoring. It’s explored innovative solutions to overcome challenges in healthcare analytics, offering real-time predictions and continuous monitoring for improved patient care. The real-time Healthcare-Diabetes dataset was analyzed using various machine learning models, and processing in real-time has led to innovative solutions to address diabetes prediction and monitoring. The Gradient Boosted Tree Classifier emerged as the most accurate model with an accuracy of 90.14%, followed by the Decision Tree Classifier at 84.62%, the Random Forest Classifier at 82.84%, the Linear Support Vector Classifier at 78.70%, and Logistic Regression at 64.69%. These results demonstrate the system’s robustness and efficiency in real-time data collection, processing, and prediction. Leveraging Apache Spark and open-source big data technologies, specify data challenges and advocate for scalable, efficient, and cost-effective healthcare analytics. It contributes to the ongoing transformation of healthcare systems, demonstrating the effectiveness of advanced technologies in enhancing disease prediction, monitoring, and overall healthcare services.

关键词： Accuracy Medical services Cluster computing Big Data Real-time systems Diabetes Sparks

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Mobile-based Intelligent Skin Diseases Diagnosis System 1

Mobile-based Intelligent Skin Diseases Diagnosis System

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1st International Conference of Intelligent Computing and Engineering, ICOICE 2019

作者： Sallam, Amer Ba alawi, Abdulfattah E. Al-Saced Faculty of Engineering IT Taiz University Computer Networks and Distributed Systems Dept Taiz Yemen Al-Saecd Faculty of Engineering IT Taiz University Software Engineering Dept Taiz Yemen

ISBN: (纸本)9781728144870

Skin diseases are the most common diseases in humans. The inherent variability in the appearance of skin diseases makes it hard even for medical experts to detect disease type from dermoscopic images. Recent advances in image processing using the Convolution Neural networks have led to better results in diagnosing systems. We aim to develop an advanced diagnostic system in a manner that meets the requirements of real-Time and extensibility of medical services for skin disease detection. The proposed system provides offline diagnosis for the users who have not Internet connection or online diagnosing uses on-cloud service. The user captures the affected area and get the offline immediate report. The schema offers the users a communication window with dermatologists to get medical recommendations in addition to an online accurate diagnosis service. The new images that are labeled by dermatologists are used to retrain the model to enhance model accuracy. To maximize the number of users, the system is implemented in a mobile-based environment. With the growing numbers of portable apps, it becomes easy for people to obtain up-To-date data. Users are familiar with looking for answers from the virtual globe including health issues. The following experimental results demonstrate the feasibility of the proposed method. The average obtained accuracy is 83% in testing cases. © 2019 IEEE.

关键词： Diagnosis

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Checkpointing distributed application running on mobile ad hoc networks

Checkpointing distributed application running on mobile ad h...

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International Journal of High Performance Computing and Networking

作者： Mansouri, Houssem Badache, Nadjib aliouat, Makhlouf Pathan, al-Sakib Khan Department of Computer Science Faculty of Exact Sciences University of Bejaia Bejaia06000 Algeria Research Center on Scientific and Technical Information CERIST Ben-Aknoun Algiers16000 Algeria Laboratory of Networks and Distributed Systems Computer Science Department University of Ferhat Abbas S?tif1 S?tif19000 Algeria Faculty of Computer and Information Systems Islamic University in Madinah Madinah al-Munawwarah Saudi Arabia CSE Department University of Asia Pacific Bangladesh

Mobile ad hoc network (MANET) is a type of wireless network consisting of a set of self-configured mobile hosts that can communicate with each other using wireless links without the assistance of any fixed infrastructure. This has made it possible for us to create distributed mobile computing applications and has also brought several new challenges in the field of distributed algorithm design. Checkpointing is a well explored fault tolerance technique for the wired and cellular mobile networks. However, it is not directly applicable to MANET owing to its dynamic topology, limited availability of stable storage, partitioning and the absence of fixed infrastructure. In this paper, we propose an adaptive, coordinated and non-blocking checkpointing algorithm to provide fault tolerance in cluster-based MANET, where only a minimum number of mobile hosts in the cluster should take checkpoints. The performance analysis and simulation results show that the proposed scheme requires less coordinating-message cost and performs well compared to the related previous works. Copyright ? 2018 Inderscience Enterprises Ltd.

关键词： Mobile ad hoc networks distributed computer systems Fault tolerance Mobile computing Cellular mobile networks Check pointing Clustering distributed mobile computing Fault tolerance techniques MANET Non blocking checkpointing Performance analysis and simulation

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Checkpointing distributed application running on mobile ad hoc networks

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International Journal of High Performance Computing and Networking 2018年第2期11卷 95-107页

作者： Mansouri, Houssem Badache, Nadjib aliouat, Makhlouf Pathan, al-Sakib Khan Department of Computer Science Faculty of Exact Sciences University of Bejaia Bejaia06000 Algeria Research Center on Scientific and Technical Information CERIST Ben-Aknoun Algiers16000 Algeria Laboratory of Networks and Distributed Systems Computer Science Department University of Ferhat Abbas Stif1 Stif19000 Algeria Faculty of Computer and Information Systems Islamic University in Madinah Madinah al-Munawwarah Saudi Arabia CSE Department University of Asia Pacific Bangladesh

关键词： Fault tolerance

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