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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Ghayoula, Ridha Amara, Wided Smida, Amor Fattahi, Jaouhar Waly, Mohamed I. Bahroun, Sahbi Gmati, Issam El University of Moncton Faculty of Engineering New Brunswick Canada Enit University of Tunis El Manar SysCom Laboratory Tunis Tunisia Majmaah University College of Applied Medical Sciences Majmaah Saudi Arabia Laval University Department of Computer Science && Software Engineering Quebec Canada Institut Superieur d'Informatique University of Tunis El Manar Tunis Tunisia Umm Al Qura University College of Engineering Umm Al Qura Saudi Arabia

ISBN: (纸本)9798350371154

In the domain of medical imaging, ensuring the security of confidential patient data stands as a paramount concern. The imperative for robust encryption to safeguard medical images against unauthorized access has driven the widespread adoption of the RSA algorithm, celebrated for its efficacy in data security assurance. This study specifically centers on the deployment of RSA-1024 cryptography on an Artix-7 FPGA, targeting applications in medical imaging. Acknowledging the demand for heightened security and accelerated processing, we have engineered RSA crypto-accelerators as hardware implementations of the RSA algorithm. We present a comprehensive methodology for implementing the 1024-bit RSA encryption/decryption algorithm, leveraging the square and multiply technique for modular exponentiation. Our algorithms are meticulously instantiated through VHDL code, tailored for execution on the Artix-7 xc7a35tcsg324-1 FPGA manufactured by Xilinx. Rigorous testing using the Vivado 2022.2 tool validates the functionality and dependability of our design. This research underscores the pivotal role of advanced cryptographic methodologies in safeguarding medical images and underscores the strides made in FPGA-based security solutions. © 2024 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Explainable-AI-based two-stage solution for WSN object localization using zero-touch mobile transceivers

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science China(Information sciences) 2024年第7期67卷 27-45页

作者： Kai FANG Junxin CHEN Han ZHU Thippa Reddy GADEKALLU Xiaoping WU Wei WANG School of Mathematics and Computer Science Zhejiang A&F University School of Software Dalian University of Technology Faculty of Applied Sciences Macao Polytechnic University Department of Electrical and Computer Engineering Lebanese American University School of Information Engineering Huzhou University Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University

Artificial intelligence technology is widely used in the field of wireless sensor networks(WSN).Due to its inexplicability, the interference factors in the process of WSN object localization cannot be effectively eliminated. In this paper, an explainable-AI-based two-stage solution is proposed for WSN object localization. In this solution, mobile transceivers are used to enlarge the positioning range and eliminate the blind area for object localization. The motion parameters of transceivers are considered to be unavailable,and the localization problem is highly nonlinear with respect to the unknown parameters. To address this,an explainable AI model is proposed to solve the localization problem. Since the relationship among the variables is difficult to fully include in the first-stage traditional model, we develop a two-stage explainable AI solution for this localization problem. The two-stage solution is actually a comprehensive consideration of the relationship between variables. The solution can continue to use the constraints unused in the firststage during the second-stage, thereby improving the performance of the solution. Therefore, the two-stage solution has stronger robustness compared to the closed-form solution. Experimental results show that the performance of both the two-stage solution and the traditional solution will be affected by numerical changes in unknown parameters. However, the two-stage solution performs better than the traditional solution, especially with a small number of mobile transceivers and sensors or in the presence of high noise. Furthermore,we have also verified the feasibility of the proposed explainable-AI-based two-stage solution.

关键词： explainable AI object localization semidefinite relaxation mobile transceiver two-stage solution closed-form solution

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Femtosecond Laser based Post-Processing Free True Random Number Generator

Femtosecond Laser based Post-Processing Free True Random Num...

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Frontiers in Optics + Laser science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser science 2023

作者： Sharma, Amritash Paithankar, Prasanna Varshney, Shailendra K. Group Advanced Photonics Laboratory Department of Electronics and Electrical Communication Engineering Indian Institute of Technology West Bengal Kharagpur721302 India Department of Applied Optics and Photonics University of Calcutta Technology Campus JD 2 Salt Lake Kolkata700098 India Department of Computer Science and Engineering Indian Institute of Technology West Bengal Kharagpur721302 India Authors contributed equally

We experimentally demonstrate a high-entropy source based on the intensity fluctuation in a femtosecond laser. The setup successfully passes statistical tests without any post-processing. The generator can be potentia... 详细信息

ISBN: (纸本)9781957171296

关键词： Femtosecond lasers Laser sources Mode-locked fiber lasers Quantum communications Quantum cryptography Ultrafast lasers

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3D Head Pose Estimation through Facial Features and Deep Convolutional Neural Networks

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computers, Materials & Continua 2021年第2期66卷 1757-1770页

作者： Khalil Khan Jehad Ali Kashif Ahmad Asma Gul Ghulam Sarwar Sahib Khan Qui Thanh Hoai Ta Tae-Sun Chung Muhammad Attique Department of Computer Science and Software Engineering Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur-KPKPakistan Department of Computer Engineering and Department of AI Convergence Network Ajou UniversitySuwon16499South Korea Hamad Bin Khalifa University DohaQatar Department of Statistics Shaheed Benazir Bhutto Women UniversityPeshawarPakistan Department of Software Engineering University of Azad Jammu and KashmirPakistan Department of Electronics and Telecommunications Politecnico di TorinoTorino10129Italy Institute of Research and Development Duy Tan UniversityDanang550000Vietnam Department of Computer Engineering Ajou UniversitySuwon16499Korea Department of Software Sejong UniversitySeoul05006South Korea

Face image analysis is one among several important cues in computer *** the last five decades,methods for face analysis have received immense attention due to large scale applications in various face analysis *** parsing strongly benefits various human face image analysis tasks inducing face pose *** this paper we propose a 3D head pose estimation framework developed through a prior end to end deep face parsing *** have developed an end to end face parts segmentation framework through deep convolutional neural networks(DCNNs).For training a deep face parts parsing model,we label face images for seven different classes,including eyes,brows,nose,hair,mouth,skin,and *** extract features from gray scale images by using *** train a classifier using the extracted *** use the probabilistic classification method to produce gray scale images in the form of probability maps for each dense semantic *** use a next stage of DCNNs and extract features from grayscale images created as probability maps during the segmentation *** assess the performance of our newly proposed model on four standard head pose datasets,including Pointing’04,Annotated Facial Landmarks in the Wild(AFLW),Boston University(BU),and ICT-3DHP,obtaining superior results as compared to previous results.

关键词： Face image analysis face parsing face pose estimation

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A Hybrid Approach Using Survey and Ism Analysis for Security Coding Risks and Best Practices for Gsd Enterprises

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SSRN 2023年

作者： Khan, Rafiq Ahmad Akbar, Muhammad Azeem Alzahrani, Abdulrahman Habibullah, Safa Alwageed, Hathal Salamah Alzahrani, Musaad School of Software Northwestern Polytechnical University Shaanxi Xian710129 China Software Engineering Research Group Department of Computer Science & IT University of Malakand Pakistan Software engineering LUT University Finland Information Systems and Technology Department College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia College of Computer and Information Sciences Jouf University Sakaka42421 Saudi Arabia Department of Computer Science Al-Baha University Al-Baha65799 Saudi Arabia

Context: Global software development (GSD) offers quality results, cost-effectiveness, and uninterrupted project delivery. However, integrating security into GSD remains a ***: This study aims to enhance security in GSD projects by developing a hybrid approach using an empirical survey and Interpretive Structural Model (ISM).Methods: Initially, we conduct a systematic literature review and questionnaire survey with 50 GSD security experts. Moreover, 13 experts were invited to analyze the interrelationships among the practices using ***: Initially, we identified 13 major security-coding risks and 82 practices to mitigate these risks. The ISM analysis revealed that out of the identified security-coding practices, "never submit security measures to illegitimate authority", "avoiding buffer overflow and format string vulnerabilities", "control the brute force attack", and "identify a middleman attack" were considered fully dependent. While "avoid revealing information to achieve a secure design" is entirely independent within the GSD security coding ***: The study aids GSD professionals in assessing readiness in establishing contractual trust, understanding the current process pros and cons, and addressing urgent issues in secure software development processes. © 2023, The Authors. All rights reserved.

关键词： Cost effectiveness

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A Decision Model for Selecting Serverless Platforms

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SSRN 2025年

作者： Hamza, Muhammad Farshidi, Siamak Akbar, Muhammad Azeem Capilla, Rafael Jansen, Slinger Smolander, Kari Department of Software Engineering LUT University Lahti15210 Finland Information Technology Group Wageningen University & Research Wageningen6700 Netherlands Department of Computer Science and Statistics Rey Juan Carlos University Madrid28933 Spain Department of Information and Computer Science Utrecht University Utrecht3584 Netherlands

Context: Serverless computing has revolutionized cloud computing by enabling developers to concentrate on developing the application logic without managing the underlying infrastructure. Organizations need to select among various serverless platforms such as AWS Lambda and OpenWhisk to develop serverless applications. However, the increasing number of serverless platforms and their diverse features, such as timeout limitation, cold start, and package size limitations, make the decision-making process increasingly complex. Moreover, decision-makers often lack specialized expertise in each platform, compelling them to continuously adapt to the rapidly evolving ***: The main objective of this study is to support decision-makers in selecting appropriate serverless platforms by designing an effective and systematic decision model. The model aims to simplify the selection process, reduce time and effort, and provide deeper insights into platform suitability based on specific organizational ***: We employed a mixed-method research design to develop a decision model for the serverless platforms selection problem. The model contains a mapping of 219 features across 16 serverless ***: The model was evaluated through five real-world case studies conducted at different software development companies. It suggests and prioritizes more than one serverless platform based on the participants’ requirements. The case study participants reported that the model offered valuable insights and significantly simplified the selection process by reducing the time and costs associated with ***: We observe in the empirical evidence that decision-makers can make more rational, efficient, and effective decisions with the decision model. Additionally, the model provides reusable insights that can support future research, such as developing new frameworks and solutions for emerging challenges in serverless computing. © 2025, The

关键词： computer software reusability

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Detection and Replay of Distributed Denial of Service Attacks In Smart Cities Using a Hybrid Deep Learning Approach

Detection and Replay of Distributed Denial of Service Attack...

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Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Muhammad Waseem Iqbal Ghassan F. Issa Muhammad Yousif Muhammad Atif Department of Software Engineering Superior University Lahore Pakistan School of Information Technology Skyline University College Sharjah UAE Department of Computer Science NCBAE Lahore. Pakistan Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science NCBAE Lahore Pakistan

Infrastructure for smart cities is presently being built, mostly because of the (IoT) platform, which connects a wide variety of things by using the website's underlying infrastructure. As a result, it is possible to employ a uniform platform to automate the services that are delivered. Smart city infrastructure, on the other hand, is susceptible to cyberattacks owing to security flaws in IoT networks. Distributing denial of service (DDoS) and replay assaults, for example, violate the requirements for certification in smart cities. The lack of citations in this part backs up many of the statements it makes. Who needs a citation? Both assaults have the potential to substantially damage smart city infrastructure, which might also cost money and even result in human fatalities. This paper covers the creation of a blended deep- learning algorithm for replay and DDoS intrusion prevention on a growth-oriented smart city platform. This is made possible by combining proven machine-learning approaches with more recent advances in artificial intelligence. We simulate scattered denial of service and replay attacks on three datasets acquired from real-world smart cities to assess the efficacy of the proposed hybrid strategy on environmental, smart river, and smart soil dataset. The suggested model has shown excellent rates of accuracy. The ecosystem collection has an accuracy level of 98.37%, the smart riverbed dataset must have an accuracy of 98.13%, and the smart mud dataset must have an accuracy of 99.51%. The findings demonstrated that the proposed model outperformed past instances of evolutionary computation and machine learning techniques employed in the corpus of academic literature.

关键词： Analytical models Machine learning algorithms Smart cities Computational modeling Soil Probability distribution Real-time systems

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Evaluating the Inclusiveness of Artificial Intelligence software in Enhancing Project Management Efficiency – A review and examples of quantitative measurement methods

Evaluating the Inclusiveness of Artificial Intelligence Soft...

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Artificial Intelligence, computer, Data sciences and Applications (ACDSA), International Conference on

作者： Vasileios Alevizos Ilias Georgousis Akebu Simasiku Antonis Messinis Sotiria Karypidou Dimitra Malliarou Department of Learning Informatics Managament and Ethics Karolinska Institute Stockholm Sweden Department of Computer Science MLV Research Group International Hellenic University Kavala Greece Department Of Computer Software Engineering Zambia University Ndola Zambia HEDNO SA (Hellenic Electricity Distribution Network Operator) Department of Computer Science MLV Research Group International Hellenic University IntelliSolutions Athens Greece

The escalating integration of Artificial Intelligence (AI) in various domains, especially Project Management (PM), has brought to light the imperative need for inclusivity in AI systems. This paper investigates the role of AI software in augmenting both the inclusiveness and efficiency within the realm of PM. The research pivots around specific criteria that define and measure the inclusiveness of AI in PM, highlighting how AI, when developed with inclusiveness in mind, can significantly enhance project outcomes. However, there are inherent challenges in achieving this inclusiveness, primarily due to biases embedded in AI learning databases and the design and development processes of AI systems. The study offers a comprehensive examination of AI's potential to revolutionize PM by enabling managers to concentrate more on people-centric aspects of their work. This is achieved through AI’s ability to perform tasks such as data collection, reporting, and predictive analysis more consistently and efficiently than human counterparts. However, the incorporation of AI in PM extends beyond mere efficiency; it represents a paradigm shift in the epistemology of PM, calling for a deeper understanding of AI's role and impact on society. Despite these advantages, the adoption of AI comes with significant challenges, particularly in terms of bias and inclusiveness. Biased AI learning databases, which use shared and reusable datasets, often perpetuate initially discriminatory algorithms. Moreover, unconscious biases and stereotypes of AI designers, developers, and trainers can inadvertently influence the behavior of the AI systems they create. This necessitates a paradigmatic shift in how AI systems are developed and governed to ensure they do not replicate or exacerbate existing social inequalities. The research proposes a methodological approach involving the development of criteria for inclusion and exclusion, alongside data extraction, to evaluate the inclusiveness and efficien

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Malware Attacks Detection in IoT Using Recurrent Neural Network(RNN)

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Intelligent Automation & Soft Computing 2024年第2期39卷 135-155页

作者： Abeer Abdullah Alsadhan Abdullah A.Al-Atawi Hanen karamti Abid Jameel Islam Zada Tan N.Nguyen Computer Science Department Imam Abdulrahman Bin Faisal UniversityDamam32232Saudi Arabia Department of Computer Science Applied CollegeUniversity of TabukTabuk47512Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences and Information Technology Hazara UniversityMansehraPakistan Faculty of Computing Department of Software EngineeringInternational Islamic UniversityIslamabadPakistan Department of Architectural Engineering Sejong UniversitySeoul05006Korea

IoT(Internet of Things)devices are being used more and more in a variety of businesses and for a variety of tasks,such as environmental data collection in both civilian and military *** are a desirable attack target for malware intended to infect specific IoT devices due to their growing use in a variety of applications and their increasing computational and processing *** this study,we investigate the possibility of detecting IoT malware using recurrent neural networks(RNNs).RNNis used in the proposed method to investigate the execution operation codes of ARM-based Internet of Things apps(OpCodes).To train our algorithms,we employ a dataset of IoT applications that includes 281 malicious and 270 benign pieces of *** trained model is then put to the test using 100 brand-new IoT malware samples across three separate LSTM *** exposure was not previously conducted on these *** newly crafted malware samples with 2-layer neurons had the highest accuracy(98.18%)in the 10-fold cross validation experiment.A comparison of the LSTMtechnique to other machine learning classifiers shows that it yields the best results.

关键词： Malware malicious code code obfuscation IoT machine learning deep learning

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FAMOS: robust privacy-preserving authentication on payment apps via federated multi-modal contrastive learning 24

FAMOS: robust privacy-preserving authentication on payment a...

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Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Yifeng Cai Ziqi Zhang Jiaping Gui Bingyan Liu Xiaoke Zhao Ruoyu Li Zhe Li Ding Li Key Laboratory of High Confidence Software Technologies (PKU) Ministry of Education and School of Computer Science Peking University Department of Computer Science University of Illinois Urbana-Champaign School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University School of Computer Science Beijing University of Posts and Telecommunications Ant Group

ISBN: (纸本)9781939133441

The rise of mobile payment apps necessitates robust user authentication to ensure legitimate user access. Traditional methods, like passwords and biometrics, are vulnerable once a device is compromised. To overcome these limitations, modern solutions utilize sensor data to achieve user-agnostic and scalable behavioral authentication. However, existing solutions face two problems when deployed to real-world applications. First, it is not robust to noisy background activities. Second, it faces the risks of privacy leakage as it relies on centralized training with users' sensor *** this paper, we introduce FAMOS, a novel authentication framework based on federated multi-modal contrastive learning. The intuition of FAMOS is to fuse multi-modal sensor data and cluster the representation of one user's data by the action category so that we can eliminate the influence of background noise and guarantee the user's privacy. Furthermore, we incorporate FAMOS with federated learning to enhance performance while protecting users' privacy. We comprehensively evaluate FAMOS using real-world datasets and devices. Experimental results show that FAMOS is efficient and accurate for real-world deployment. FAMOS has an F1-Score of 0.91 and an AUC of 0.97, which are 42.19% and 27.63% higher than the baselines, respectively.

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