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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI)

Seifedine Kadry

Beirut Arab University

Author-ID : 2373871

Computer Science & IT Electrical & Electronics Engineering

Published : 16 Documents Claim Missing Document

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Articles

Title

A new framework to alleviate DDoS vulnerabilities in cloud computing A. Saravanan; S. SathyaBama; Seifedine Kadry; Lakshmana Kumar Ramasamy
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 5: October 2019
Publisher : Institute of Advanced Engineering and Science

In the communication age, the Internet has growing very fast and most industries rely on it. An essential part of Internet, Web applications like online booking, e-banking, online shopping, and e-learning plays a vital role in everyday life. Enhancements have been made in this domain, in which the web servers depend on cloud location for resources. Many organizations around the world change their operations and data storage from local to cloud platforms for many reasons especially the availability factor. Even though cloud computing is considered a renowned technology, it has many challenges, the most important one is security. One of the major issue in the cloud security is Distributed Denial of Service attack (DDoS), which results in serious loss if the attack is successful and left unnoticed. This paper focuses on preventing and detecting DDoS attacks in distributed and cloud environment. A new framework has been suggested to alleviate the DDoS attack and to provide availability of cloud resources to its users. The framework introduces three screening tests VISUALCOM, IMGCOM, and AD-IMGCOM to prevent the attack and two queues with certain constraints to detect the attack. The result of our framework shows an improvement and better outcomes and provides a recovered from attack detection with high availability rate. Also, the performance of the queuing model has been analysed.

Image multi-level-thresholding with Mayfly optimization Seifedine Kadry; Venkatesan Rajinikanth; Jamin Koo; Byeong-Gwon Kang
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp5420-5429

Image thresholding is a well approved pre-processing methodology and enhancing the image information based on a chosen threshold is always preferred. This research implements the mayfly optimization algorithm (MOA) based image multi-level-thresholding on a class of benchmark images of dimension 512x512x1. The MOA is a novel methodology with the algorithm phases, such as; i) Initialization, ii) Exploration with male-mayfly (MM), iii) Exploration with female-mayfly (FM), iv) Offspring generation and, v) Termination. This algorithm implements a strict two-step search procedure, in which every Mayfly is forced to attain the global best solution. The proposed research considers the threshold value from 2 to 5 and the superiority of the result is confirmed by computing the essential Image quality measures (IQM). The performance of MOA is also compared and validated against the other procedures, such as particle-swarm-optimization (PSO), bacterial foraging optimization(BFO), firefly-algorithm(FA), bat algorithm (BA), cuckoo search(CS) and moth-flame optimization (MFO) and the attained p-value of Wilcoxon rank test confirmed the superiority of the MOA compared with other algorithms considered in this work

Detecting malicious URLs using binary classification through adaboost algorithm Firoz Khan; Jinesh Ahamed; Seifedine Kadry; Lakshmana Kumar Ramasamy
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 1: February 2020
Publisher : Institute of Advanced Engineering and Science

Malicious Uniform Resource Locator (URL) is a frequent and severe menace to cybersecurity. Malicious URLs are used to extract unsolicited information and trick inexperienced end users as a sufferer of scams and create losses of billions of money each year. It is crucial to identify and appropriately respond to such URLs. Usually, this discovery is made by the practice and use of blacklists in the cyber world. However, blacklists cannot be exhaustive, and cannot recognize zero-day malicious URLs. So to increase the observation of malicious URL indicators, machine learning procedures should be incorporated. This study aims to discuss the exposure of malicious URLs as a binary classification problem using machine learning through an AdaBoost algorithm.

Online Data Preprocessing: A Case Study Approach Mohammed Zuhair Al-Taie; Seifedine Kadry; Joel Pinho Lucas
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 4: August 2019
Publisher : Institute of Advanced Engineering and Science

Besides the Internet search facility and e-mails, social networking is now one of the three best uses of the Internet. A tremendous number of volunteers every day write articles, share photos, videos and links at a scope and scale never imagined before. However, because social network data are huge and come from heterogeneous sources, the data are highly susceptible to inconsistency, redundancy, noise, and loss. For data scientists, preparing the data and getting it into a standard format is critical because the quality of data is going to directly affect the performance of mining algorithms that are going to be applied next. Low-quality data will certainly limit the analysis and lower the quality of mining results. To this end, the goal of this study is to provide an overview of the different phases involved in data preprocessing, with a focus on social network data. As a case study, we will show how we applied preprocessing to the data that we collected for the Malaysian Flight MH370 that disappeared in 2014.

Performance analysis of sentiments in Twitter dataset using SVM models Lakshmana Kumar Ramasamy; Seifedine Kadry; Yunyoung Nam; Maytham N. Meqdad
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2275-2284

Sentiment Analysis is a current research topic by many researches using supervised and machine learning algorithms. The analysis can be done on movie reviews, twitter reviews, online product reviews, blogs, discussion forums, Myspace comments and social networks. The Twitter data set is analyzed using support vector machines (SVM) classifier with various parameters. The content of tweet is classified to find whether it contains fact data or opinion data. The deep analysis is required to find the opinion of the tweets posted by the individual. The sentiment is classified in to positive, negative and neutral. From this classification and analysis, an important decision can be made to improve the productivity. The performance of SVM radial kernel, SVM linear grid and SVM radial grid was compared and found that SVM linear grid performs better than other SVM models.

The future of software engineering: Visions of 2025 and beyond Firoz Khan; R. Lakshmana Kumar; Seifedine Kadry; Yunyoung Nam
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3443-3450

In the current technological scenario of the industry and businesses, there has been increasing need of software within systems and also an increasing demand being put onto software-intensive systems. This in effect will lead to a significant evolution of software engineering processes over the next twenty years. This is due to the fact of emerging technological advancements like Industry 4.0 and Internet of Things in the IT field, among other new developments. This paper addresses and tries to analyses the key research challenges being faced by the software engineering field and articulates information that is derived from the key research specializations within software engineering. The paper analyses the past and current trends in software engineering. The future of software engineering is also looked with respect to Industry 4.0 which including emerging technological platforms like Internet of Things. The societal impact aspect of future trends in software engineering is also addressed in this paper.

An effective identification of crop diseases using faster region based convolutional neural network and expert systems P. Chandana; G. S. Pradeep Ghantasala; J. Rethna Virgil Jeny; Kaushik Sekaran; Deepika N.; Yunyoung Nam; Seifedine Kadry
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v10i6.pp6531-6540

The majority of research Study is moving towards cognitive computing, ubiquitous computing, internet of things (IoT) which focus on some of the real time applications like smart cities, smart agriculture, wearable smart devices. The objective of the research in this paper is to integrate the image processing strategies to the smart agriculture techniques to help the farmers to use the latest innovations of technology in order to resolve the issues of crops like infections or diseases to their crops which may be due to bugs or due to climatic conditions or may be due to soil consistency. As IoT is playing a crucial role in smart agriculture, the concept of infection recognition using object recognition the image processing strategy can help out the farmers greatly without making them to learn much about the technology and also helps them to sort out the issues with respect to crop. In this paper, an attempt of integrating kissan application with expert systems and image processing is made in order to help the farmers to have an immediate solution for the problem identified in a crop.

Co-Authors A. Saravanan B. Vijayalaxmi Byeong-Gwon Kang Chavali Anuradha Deepika N. Fardin Abdali-Mohammadi Firoz Khan G. S. Pradeep Ghantasala J. Rethna Virgil Jeny Jamin Koo Jinesh Ahamed Joel Pinho Lucas K. Dinesh Kaushik Sekaran Kaushik Sekaran Lakshmana Kumar Ramasamy Lakshmana Kumar Ramasamy M. Navaneetha Krishnan M. Sornalakshmi M. Venkatesulu Maytham N. Meqdad Maytham N. Meqdad Mohammed Zuhair Al-Taie P. Chandana Pardeep Kumar R. Lakshmana Kumar R. Rajakumar Rawya Al-Akam S. Balamurali S. SathyaBama Sangsoon Lim Soundar Rajan T. P. Latchoumi Venkatesan Rajinikanth Venkatesan Rajinikanth Venkatesan Rajinikanth Y. Rajkumar Yunyoung Nam

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Journal : International Journal of Electrical and Computer Engineering