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Feature Selection for Robust Spoofing Detection: A Chi-Square-based Machine Learning Approach 2

Feature Selection for Robust Spoofing Detection: A Chi-Squar...

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2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Al-Na'amneh, Qais Aljaidi, Mohammad Gharaibeh, Hasan Nasayreh, Ahmad Emhamed Al Mamlook, Rabia Almatarneh, Sattam Alzu'bi, Dalia Suliman Husien, Abla Applied Science Private University Dep. of Cyber Security Amman Jordan Zarqa University Dep. of Computer Science Zarqa Jordan Yarmouk University Dep. Computer Science Irbid Jordan Trine University Dep. of Business Administration Indiana United States University of Zawia Department of Industrial Engineering Tripoli Libya Zarqa University Dep. of Data Science and Artificial Intelligence Zarqa Jordan Concordia University Dep. Information and Systems Engineering Montreal Canada

ISBN: (纸本)9798350373363

Nowadays, the Internet of Things (IoT) system is vulnerable to spoofing attacks that can easily where attackers can easily pose as a legal entity of the network. A 'spoofing attack' refers to a type of cyber-attack when an attacker purposefully impersonates or masquerades as someone or something else to deceive the target or obtain unauthorized access to systems, information, or resources. In such attacks, the attacker alters their name, IP address, or other attributes to fool the victim into thinking they are engaging with a legitimate entity. Spoofing attacks can take place via a variety of channels, including, ARP and DNS spoofing. Therefore. Spoofing attacks can have serious consequences. We proposed a new approach based on three machine learning models LightGBM, Gradient Boost, and XGBoost to classify attacks on spoofing, we used Chi-square to select the best features to get the highest performance, and we demonstrated that the results using Chi-square achieved higher results than without Chi-square and improved the result with a rate three percent of accuracy. In terms of results, LightGBM outperformed other models by achieving 89%, 91 %, 87 %, and 89 % for accuracy, precision, recall, and f1-score, respectively. This shows the potential and efficiency of Chi-square to achieve the best performance by selecting the best features, thus providing a secure system and identifying cyber-attacks on systems. © 2023 IEEE.

关键词： Internet of things

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Logistic Regression Classification for Assessing the Risk of Kidney Tumor 2

Logistic Regression Classification for Assessing the Risk of...

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2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Alzubi, Dalia Al Mamlook, Rabia Emhamed Nasayreh, Ahmad Aljaidi, Mohammad Alazab, Rami Gharaibeh, Hasan Al-Naamneh, Qais Concordia University Dep. Information and Systems Engineering Montreal Canada Trine University Dep. of Business Administration IN United States University Zawia Department of Industrial Engineering Tripoli Libya Yarmouk University Dep. Computer Science Irbid Jordan Zarqa University Dep. of Computer Science Zarqa Jordan Jordan University of Science and Technology Dep. Surgery and Urology Irbid Jordan Applied Science Private University Dep. of Cyber Security Amman Jordan

ISBN: (纸本)9798350373363

The kidneys have the important function of filtering waste products and toxins from the blood in the human body. Any problems affecting the kidneys can potentially impact their efficiency and overall function. Kidney Tumor (KT) is a disease that specifically affects kidney cells and causes the abnormal growth of tissue in one or both kidneys. Assessing the risk of developing a kidney tumor and identifying the key factors that contribute to this risk are crucial for ensuring patient well-being. Additionally, this information assists doctors and specialists in making faster and more accurate diagnoses, determining appropriate treatment methods, and potentially influencing the course of the disease to minimize its severity and impact. Machine learning algorithms have recently been introduced to evaluate disease risks, and in this study, we specifically focus on examining the risk of kidney tumor development and investigating the influencing factors. We employed a logistic regression model to predict if a patient is at risk of developing a kidney tumor or not, and further categorized the samples into high-risk and low-risk groups. Our model was trained and tested using a unique dataset obtained from the (KAUH) hospital in Jordan, consisting of well-balanced metadata from 120 patients with kidney issues. Our work demonstrated accuracy results ranging from 90% to 98%. Ultimately, specialists can utilize our model as an additional tool to enhance the speed and accuracy of patient diagnoses © 2023 IEEE.

关键词： Tumors

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The Influence of Applying Digital Toolkits to Assist 3D Software Development on Individual Work Performance and Perceived Stress 10

The Influence of Applying Digital Toolkits to Assist 3D Soft...

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10th International Conference on Applied System Innovation, ICASI 2024

作者： Wu, Wen-Chuan Lee, Ming-Han National Ilan University Dep. of Computer Science and Information Engineering Yilan City Taiwan National Ilan University Master Program of E-Learning for Multimedia and Network Communications Yilan City Taiwan

ISBN: (纸本)9798350394924

Digital content industry, encompassing areas such as digital games, computer animation, digital audio-visual applications, and digital art, plays a pivotal role in today's and future developmental trends. Maya 3D software stands out as a crucial development tool in these domains. However, the multitude of built-in tools in Maya 3D can lead to overly complex interfaces, impacting development efficiency for software developers. To address this issue, this paper designs a digital toolkit for Maya 3D software, presented in a Menu Bar format. This toolkit offers features like naming, sorting, categorization, and automatic saving to JSON files for future use. Through a crossover experimental design, this paper investigates the impact of this proposed toolkit on 3D software developers' individual work performance, work engagement, and stress perception. Results shows a significant positive influence of that toolkit on software developers, particularly in enhancing work performance. It means that the proposed digital toolkit effectively improves the performance of software developers' work and reduces their stress during the software development process. © 2024 IEEE.

关键词： Digital devices

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Fine-Tuning Pre-Trained Models for Automated Analysis of Ophthalmic Imaging in Diagnosing Eye Diseases 24

Fine-Tuning Pre-Trained Models for Automated Analysis of Oph...

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24th International Arab Conference on information Technology, ACIT 2023

作者： Al Mamlook, Rabia Emhamed Nasayreh, Ahmad Gharaibeh, Hasan Aljaidi, Mohammad Al-Na'amneh, Qais Alzu'bi, Dalia Bzizi, Hanin Abdeltawab, Abdelrahman Trine University Dep. of Business Administration Indiana United States University Zawia Department of Industrial Engineering Tripoli Libya Yarmouk University Dep. Computer Science Irbid Jordan Zarqa University Dep. of Computer Science Zarqa Jordan Applied Science Private University Dep. of Cyber Security Amman Jordan Concordia University Dep. Information and Systems Engineering Montreal Canada Western Michigan University Dep of Biomedical Science KalamazooMI49008 United States Bani Sweif University Faculty of Pharmacy Bani Suwayf Egypt

ISBN: (纸本)9798350384307

This paper searches into the convergence of such ad-vanced techniques with architectures like DenseNet201, VGG16, InceptionResNetV2, and NasNetMobile. Our focus centers on harnessing deep learning capabilities for the precise detection of eye ailments. Preliminary findings spotlight a notable uplift in diagnostic precision. Among the tested algorithms, the Nas-NetMobile architecture emerged superior, boasting an impressive accuracy rate of 92% on the chosen dataset. Through this work, we extend a significant advancement in the realm of automated eye disease diagnosis, potentially equipping medical professionals with sharper, swifter diagnostic tools. The outcomes presented herein could play a seminal role in sculpting more sophisticated and precise diagnostic approaches within ophthalmology. © 2023 IEEE.

关键词： Deep Learning Eye Disease Fine Tuning Transfer Learning

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A Proposed Approach for Risk Planning and Management 25

A Proposed Approach for Risk Planning and Management

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25th International Arab Conference on information Technology, ACIT 2024

作者： Al Refai, Mohammed N. Al-Smadi, Yazan Al-Momani, Adai Jamhawi, Zeyad M. Otoom, Ahmed Ali Atoum, Issa Yaseen, Ahid Ababneh, Jafar Kanan, Mohammad Zarqa University Software Engineering Dep. Zarqa Jordan Zarqa University Department of Computer Science Zarqa Jordan Applied Science Private University Department of Computer Science Amman Jordan Applied Science Private University Department of Cyber Security Amman Jordan Philadelphia University Faculty of Information Technology Software Engineering Department Jordan Zarqa University Department of Cyber Security Zarqa Jordan University of Business and Technelogy Dep. of Industry Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Project Risk management is the process of identifying, evaluating, avoiding, or reducing risks. Where there is no software project without risks existence are natural in the context of project planning and management. This study focuses on proposing an enhanced risk management approach where testing phases were added to make the risk management process more effective by proposing solutions to many mistakes and challenges that occur through dealing with risks. The proposed approach focuses on dealing with risks that cannot be easily defined, more accurate risk evaluation, planning, and managing risk coordinated with a contingency plan. © 2024 IEEE.

关键词： Project management

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Secure Password Using EEG-based BrainPrint System: Unlock Smartphone Password Using Brain-computer Interface Technology

Secure Password Using EEG-based BrainPrint System: Unlock Sm...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Alkhyeli, Zuwaina Alshehhi, Ayesha Alhemeiri, Mazna Aldhanhani, Salma Albalushi, Khalil Alnuaimi, Fatima Ali Belkacem, Abdelkader Nasreddine UAE University CIT Dep. Computer Science and Software Engineering Al Ain United Arab Emirates UAE University CIT Dep. Information Systems and Security Al Ain United Arab Emirates United Arab Emirates University CIT Dep. Computer & Network Engineering Al Ain United Arab Emirates Autonomous Robotics Research Center TII Abu Dhabi United Arab Emirates

ISBN: (纸本)9781665468190

As security becomes a strong factor in daily activities, finding secure ways to unlock machines and smartphones is a challenge due to hardware limitations and the high risk of hacking. Considering the level of security and privacy in the digital world, attackers tend to be one step ahead. Therefore, this technical paper introduces a brain-computer interface (BCI) for increasing subject-based security using unique biometric features as a solution to build complex passwords. The BCI measures brain changes and extracts relevant bio-features from each subject using non-invasive electroencephalogram (EEG) tests. The proposed system allows users to gain access to their devices using brain waves (bypass) instead of inserting their password manually (normal path), which saves the user time and upgrades the level of privacy as no physical actions are required during this process. This system is also well suited for individuals with mobility impairments. We used the P300-based BCI controlling paradigm which dep.nds on reading the electrical brain activity of the user when observing a particular object. The other feature of the system is that it can extract unique features of each individual brain to produce a network that uniquely identifies them, which is used as a security layer. Users need to enter their unique network to access their device with failed attempts requiring an EEG test to identify the user. The system plays an active role in facilitating user processes for authentication while accessing devices. The system establishes an urgent call whenever the user's brain currents command it to. The project outcomes were assessed by simulating the BCI before real-time implementation to determine errors and resolve the validity of the project scope. © 2022 IEEE.

关键词： Brain computer interface

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Pruning neural network models for gene regulatory dynamics using data and domain knowledge 38

Pruning neural network models for gene regulatory dynamics u...

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38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Hossain, Intekhab Fischer, Jonas Burkholz, Rebekka Quackenbush, John Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA02115 United States Dep. for Computer Vision and Machine Learning Max Planck Institute for Informatics Saarbrücken Germany Helmholtz Center CISPA for Information Security Saarbrücken Germany

The practical utility of machine learning models in the sciences often hinges on their interpretability. It is common to assess a model's merit for scientific discovery, and thus novel insights, by how well it aligns with already available domain knowledge-a dimension that is currently largely disregarded in the comparison of neural network models. While pruning can simplify deep neural network architectures and excels in identifying sparse models, as we show in the context of gene regulatory network inference, state-of-the-art techniques struggle with biologically meaningful structure learning. To address this issue, we propose DASH‡, a generalizable framework that guides network pruning by using domain-specific structural information in model fitting and leads to sparser, better interpretable models that are more robust to noise. Using both synthetic data with ground truth information, as well as real-world gene expression data, we show that DASH, using knowledge about gene interaction partners within the putative regulatory network, outperforms general pruning methods by a large margin and yields deeper insights into the biological systems being studied. © 2024 Neural information processing systems foundation. All rights reserved.

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On the Efficiency of Combinatorial Generation for Adaptive Image Steganography

On the Efficiency of Combinatorial Generation for Adaptive I...

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2021 International Conference Engineering and Telecommunication, En and T 2021

作者： Melman, Anna Evsutin, Oleg Shablya, Yuriy Higher School of Economics Dep. of Information Security of Cyber-Physical Systems Moscow Russia Tomsk State University of Control Systems and Radioelectronics Dep. of Complex Information Security of Computer Systems Tomsk Russia

ISBN: (纸本)9781728183909

Steganographic embedding is an effective method of protecting information transmitted over an open communication channel. Steganography techniques hide information in digital objects by embedding to make it invisible to an attacker. The most common covers for hiding additional information are multimedia data, in particular digital images. The embedding effectiveness is mainly assessed by the criteria of invisibility and capacity. One of the ways to improve the embedding efficiency is to choose the best location of message bits within the cover object, but it usually takes a long time to iterate through a large number of such embedding options. In this paper, we propose a new approach to finding the best option for steganographic embedding of information into the phase spectrum of the discrete Fourier transform based on the methods of combinatorial generation. The results of the experiments show that the new approach allows us to reduce the best embedding option search time, while the invisibility and capacity indicators remain high. © 2021 IEEE.

关键词： Steganography

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Next Generation Image Watermarking via Combined DWT-SVD Technique 2

Next Generation Image Watermarking via Combined DWT-SVD Tech...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Na'amneh, Qais Almomani, Ammar Nasayreh, Ahmad Nahar, Khalid M.O. Gharaibeh, Hasan Al Mamlook, Rabia Emhamed Alauthman, Mohammad Applied Science Private University Department Of Cyber Security Amman Jordan School Of Computing Skyline University College P.O. Box 1797 Sharjah United Arab Emirates AlHuson University College AlBalqa Applied University I. T. Department Irbid Jordan Yarmouk University Department Of Computer Sciences Irbid211633 Jordan Yarmouk University Faculty Of Information Technology Computer Sciences Irbid211633 Jordan Trine University Dep. Of Business Administration IN United States University Of Zawia Dep.of Mechanical And Industrial Engineering Tripoli Libya University Of Petra Faculty Of Information Technology Department Of Information Security Amman Jordan

ISBN: (纸本)9798350394962

Personal computers and the Internet are used in different areas and are easier to use. Most data is easy to transmit and duplicate in digital format, and being tampered with and stolen easily leads to issues for content owners and service providers. The Paper describes a secure (tampering-resistant) method for watermarking images for protection and authentication and digital media like audio, video, and multimedia data, and a combination of singular value decomposition (SVD) and Discrete wavelet transform (DWT) for insertion watermark by MATLAB to increase accuracy, simplicity, and flexibility Readability, secure robustness, and imperceptibility the visibility of the image. The proposed algorithm consists of three main steps: Firstly, we decomposed the original image into four sub-bands and the original watermark. Secondly, embed the watermark in the LL sub-band by replacing the singular value for the watermark. Finally, embed a watermark in the other three sub-bands by adding LL values for a watermark in each sub-band in the original image. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Logistic Regression Classification for Assessing the Risk of Kidney Tumor

Logistic Regression Classification for Assessing the Risk of...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International Engineering Conference on

作者： Dalia Alzu'bi Rabia Emhamed Al Mamlook Ahmad Nasayreh Mohammad Aljaidi Rami AlAzab Hasan Gharaibeh Qais Al-Na'amneh Dep. Information and Systems Engineering Concordia University Montreal Canada Dep. of Business Administration Trine University Indiana USA Department of Industrial Engineering University Zawia Tripoli Libia Dep. Computer Science Yarmouk University Irbid Jordan Dep. of Computer Science Zarqa University Zarqa Jordan Dep. Surgery and Urology Jordan University of Science and Technology Irbid Jordan Dep. of Cyber Security Applied Science Private University Amman Jordan

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

关键词： Logistic regression Analytical models Accuracy Predictive models Genetics History Reliability

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