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Physical Layer Security in AmBC-NOMA Networks with Random Eavesdroppers

IEEE Internet of Things Journal 2025年第12期12卷 18628-18642页

作者： Pei, Xinyue Wang, Xingwei Huang, Min Chen, Yingyang Li, Xiaofan Tsiftsis, Theodoros A. Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University College of Information Science and Engineering Shenyang110819 China Jinan University College of Information Science and Technology Guangzhou510632 China Jinan University School of Intelligent Systems Science and Engineering Zhuhai519070 China University of Thessaly Department of Informatics Telecommunications Lamia35100 Greece University of Nottingham Ningbo China Department of Electrical and Electronic Engineering Ningbo315100 China

In this work, we investigate the physical layer security (PLS) of ambient backscatter communication non-orthogonal multiple access (AmBC-NOMA) networks where non-colluding eavesdroppers (Eves) are randomly distributed. In the proposed system, a base station (BS) transmits a superimposed signal to a typical NOMA user pair, while a backscatter device (BD) simultaneously transmits its unique signal by reflecting and modulating the BS’s signal. Meanwhile, Eves passively attempt to wiretap the ongoing transmissions. Notably, the number and locations of Eves are unknown, posing a substantial security threat to the system. To address this challenge, the BS injects artificial noise (AN) to mislead the Eves, and a protected zone is employed to create an Eve-exclusion area around the BS. Theoretical expressions for outage probability (OP) and intercept probability (IP) are provided to evaluate the system’s reliability-security trade-off. Asymptotic behavior at high signal-to-noise ratio (SNR) is further explored, including the derivation of diversity orders for the OP. Numerical results validate the analytical findings through extensive simulations, demonstrating that both the AN injection and protected zone can effectively enhance PLS. Furthermore, analysis and insights of different key parameters, including transmit SNR, reflection efficiency at the BD, power allocation coefficient, power fraction allocated to desired signal, Eve-exclusion area radius, Eve distribution density, and backscattered AN cancellation efficiency, on OP and IP are also provided. © 2014 IEEE.

关键词： Signal to noise ratio

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Novel Darknet traffic data synthesis using Generative Adversarial Networks enhanced with oscillatory Growing Cosine Unit activated convolution layers

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 58-65页

作者： Pradeep C, Antony Amali D, Geraldine Bessie Noel, Mathew Mithra Ghalib, Muhammad Rukunuddin Subramaniam, Prabhakar Rontala Venugopal, Chitra School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Electrical Engineering Vellore Institute of Technology Vellore India School of Computing De Montfort University Dubai United Arab Emirates Information Systems and Technology Westville South Africa Electrical Engineering & Renewable Energy Oregon Institute of Technology United States

The Darknet is an anonymous, encrypted collection of websites, with a passive listening nature - accepting incoming packets, while not supporting outgoing packets. Thus, it can potentially host criminal or malicious activity and software, becoming a cyber security threat. Network Detection systems are effective in identifying dark net traffic and mitigating its ill effects. However, capturing and extracting data from raw network traffic for training these systems can be time-intensive and costly. Using the CIC-Darknet 2020 dataset, this paper proposes using a Novel Generative Adversarial Networks (GAN) Architecture generating the required training and testing data for these systems. This uses a combination of Growing Cosine Unit (GCU) activated convolution layers and Dense layers for the Generator. Feature selection with statistical correlation methods is used to select the most relevant features. An independently trained Evaluator network is used to evaluate the generated data. The proposed system is compared to other established Tabular data GANs like Conditional Tabular GAN (CTGAN) and Copula GAN with similar parameters and on the same data. The Proposed GAN architecture outperforms CTGAN and CopulaGAN by 20 % and 10 % in ters of accuracy while also taking 90 % and 30 % less time to train respectively. Results from measuring similarity of data using the Inverted Kolmogorov - Smirnov D statistic also show significantly better results for the Proposed GAN Architecture. This shows significant promise in using Generative models to reduce the time and effort costs associated with collecting and formatting data to use in research and for training detection systems. © 2024

关键词： Generative adversarial networks

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Investigation of DNA discontinuity for detecting tuberculosis

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1149-1163页

作者： Nimmy, Sonia Farhana Sarowar, Md. Golam Dey, Nilanjan Ashour, Amira S. Santosh, K.C. Department of Computer Science and Engineering Notre Dame University Dhaka Bangladesh Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Information Technology Techno India College of Technology Kolkata India Department of Electronics and Electrical Communications Engineering Faculty of Engineering Tanta University Tanta Egypt Vermillion United States

Discontinuity in long Deoxyribonucleic Acid (DNA) sequences creates harmful diseases. Changes in the DNA structure refers to changes in the human immunity system. Tuberculosis is a critical disease that causes coughing, fatigue, unintentional weight loss and fever on aged people due to the disorder in the DNA. Breaks or mutations over long DNA sequences are the pivotal reasons for this fatal disease. This study developed an automated machine learning technique to assess the total number of such breaks in the long DNA sequences. Data cleansing and deep neural network techniques are applied to handle this big data. The National Center for Biotechnology information (NCBI) database has been used to extract the amino acid sequences for Tuberculosis disease from the big DNA datasets. Results reveal that the proposed automated approach is significantly effective for the determination of DNA sequence breaks for the tuberculosis diseases due to the high sensitivity of Markov chain as well as the effective normalization techniques. This approach fixed the size of the training datasets and recursively divide the whole dataset into certain length. The study also adopts multiple predictions approaches, such as the hidden Markov chain, Box-Cox transformation and linear transformation to forecast about the breaks for any long positions of the training and testing datasets. The results demonstrated that hidden the Markov chain model provided faster analysis with more accurate and reliable results. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： DNA

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Improved Multileader Optimization with Shadow Encryption for Medical Images in IoT Environment

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computers, Materials & Continua 2023年第2期74卷 3133-3149页

作者： Mesfer Al Duhayyim Mohammed Maray Ayman Qahmash Fatma S.Alrayes Nuha Alshuqayran Jaber S.Alzahrani Mohammed Alghamdi Abdullah Mohamed Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic UniversitySaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information and Technology Systems College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt

Nowadays,security plays an important role in Internet of Things(IoT)environment especially in medical services’domains like disease prediction and medical data *** healthcare sector,huge volumes of data are generated on a daily basis,owing to the involvement of advanced health care *** general terms,health care images are highly sensitive to alterations due to which any modifications in its content can result in faulty *** the same time,it is also significant to maintain the delicate contents of health care images during reconstruction ***,an encryption system is required in order to raise the privacy and security of healthcare data by not leaking any sensitive *** current study introduces Improved Multileader Optimization with Shadow Image Encryption for Medical Image Security(IMLOSIE-MIS)technique for IoT *** aim of the proposed IMLOSIE-MIS model is to accomplish security by generating shadows and encrypting them *** do so,the presented IMLOSIE-MIS model initially generates a set of shadows for every input medical ***,shadow image encryption process takes place with the help of Multileader Optimization(MLO)withHomomorphic Encryption(IMLO-HE)technique,where the optimal keys are generated with the help of MLO *** the receiver side,decryption process is initially carried out and shadow image reconstruction process is *** experimentation analysis was carried out on medical images and the results inferred that the proposed IMLOSIE-MIS model is an excellent performer compared to other *** comparison study outcomes demonstrate that IMLOSIE-MIS model is robust and offers high security in IoT-enabled healthcare environment.

关键词： Medical image security image encryption shadow images homomorphic encryption optimal key generation

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Project ALVI: Privacy-Driven Defences: Federated Learning Security and Authentication 6

Project ALVI: Privacy-Driven Defences: Federated Learning Se...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Peiris, Livindu Shaluka, Aman Hasaranga, Virushan Athauda, Iman Yapa, Kanishka Rathnayake, Samadhi Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

Federated Learning (FL) is a revolutionary approach to decentralized machine learning, enabling collaborative model training across multiple nodes without sharing raw data, thereby enhancing privacy. However, FL systems face significant security challenges, including man-in-the-middle (MITM) attacks, backdoor injections, label inference attacks, and data/model poisoning. To address these threats, we present Project ALVI: Privacy-Driven Defenses, a comprehensive security framework designed to fortify FL systems. This paper integrates four key components: (1) CodeNexa, a dynamic metric verification mechanism that mitigates MITM attacks by ensuring the integrity of model updates;(2) HydraGuard, a hybrid defense mechanism combining trigger inversion and selective layer finetuning to counter backdoor attacks;(3) Confusional Autoencoders with noise addition and Secure Multi-Party Computation (SMC) to protect against label inference attacks in Vertical Federated Learning (VFL);and (4) SECUNID, an anomaly detection framework that leverages LayerCAM and Autoencoders to identify and neutralize data and model poisoning attacks. Experimental evaluations on the MNIST dataset demonstrate the effectiveness of Project ALVI in maintaining model accuracy while significantly reducing the impact of adversarial threats. The integration of these components into a unified framework offers a robust, scalable, and privacy-driven solution for securing FL systems. © 2024 IEEE.

关键词： Federated learning

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Enhancing Financial Sector Cybersecurity: A Multi-Component Framework for Supply Chain Risk Mitigation 6

Enhancing Financial Sector Cybersecurity: A Multi-Component ...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Kiran, Suryaa Praveen, Yomal Silva, Akila Bagya, Yasith Yapa, Kavinga Siriwardana, Deemantha Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

Supply chains are being used at scale within new industries, mainly in the financial sector and the cyber security risks emerging in this area have been on the rise. Given recent attack scenarios the mitigation strategies taken within these areas are limited. This research paper focuses on the main areas where the risk can be reduced to provide an overall effective solution to reduce the risks. However, there are certain challenges in minimizing the risks associated, such as third-party vendor identification, classifying and securing data, and gathering relevant external threat information. This research utilizes a centralized risk assessment gateway to assess the security level of each third-party vendor. Further, there will be a secure transfer of text and image data once the vendors have been assessed and these will be done in correlation with a zero-trust policy engine where data classification occurs based on unique trust levels. In addition, it will also use machine learning to tailor the external cyber threat feeds to provide valuable insights regarding threat attacks, vulnerabilities, and patches within the sector to gain more granular intelligence and reduce analysis time. The overall solution will serve as a zero-trust approach to mitigate the attacks within the financial fields of the supply chain sector. © 2024 IEEE.

关键词： data classification machine learning risk assessment supply chains zero trust policy engine

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The value of formative evaluation in an education program

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International Journal of Applied Systemic Studies 2022年第4期9卷 381-388页

作者： Tsipianitis, Dimitris Mandellos, Georgios Systems and Automatic Control Division Electrical and Computer Engineering Department University of Patras RionGR-26504 Greece Telecommunications and Information Technology Division Electrical and Computer Engineering Department University of Patras RionGR-26504 Greece

Evaluation is a process that compares the results with the target, an initial situation with the final one. ‘Evaluate’ means making an assessment based on some measure or information and referring to some criteria. The Formative evaluation is a process which is used by trainers and trainees and provides feedback on updating current teaching and learning methods in order to improve trainer’s achievement in relation of teaching goals. In this case study a research was carried out using the technique of formative evaluation in order to evaluate two thematic areas with the cooperation of the program’s leaders. The aim of the study was to extract conclusions and opinions about the program’s value, the achievement goals and the organisation implementation requirements and how all this results will been feed backed to program managers and policymakers with the appropriate information which are necessary to adopt appropriate corrective interventions for programs quality improvement. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Quality control

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A hybrid machine-learning model for solar irradiance forecasting

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Clean Energy 2024年第1期8卷 100-110页

作者： Ameera M.Almarzooqi Maher Maalouf Tarek H.M.El-Fouly Vasileios E.Katzourakis Mohamed S.El Moursi Constantinos V.Chrysikopoulos Department of Industrial and Systems Engineering Khalifa UniversityAbu DhabiPO Box 127788UAE Electrical Engineering and Computer Science Department Khalifa University Abu DhabiUAE Department of Civil Infrastructure and Environmental Engineering Khalifa UniversityAbu DhabiPO Box 127788UAE School of Chemical and Environmental Engineering Technical University of CreteChaniaGreece

Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic(PV)power *** plants face operational challenges and scheduling dispatch difficulties due to the fluctuating nature of their power *** the generation capacity within the electric grid increases,accurately predicting this output becomes increasingly essential,especially given the random and non-linear characteristics of solar irradiance under variable weather *** study presents a novel prediction method for solar irradiance,which is directly in correlation with PV power output,targeting both short-term and medium-term forecast *** proposed hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression *** proposed method excels in forecasting solar irradiance,especially during highly intermittent weather periods.A key strength of our model is the incorporation of multiple historical weather parameters as inputs to generate accurate predictions of future solar irradiance values in its scalable *** evaluated the performance of our model using data sets from both cloudy and sunny days in Seattle and Medford,USA and compared it against three forecasting models:persistence,modified 24-hour persistence and least *** on three widely accepted statistical performance metrics(root mean squared error,mean absolute error and coefficient of determination),our hybrid model demonstrated superior predictive accuracy in varying weather conditions and forecast horizons.

关键词： solar power generation kernel ridge regression hybrid model forecasting

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Optimal Integration of Distributed Renewable Generation for Reliability Improvement in Distribution Network

Optimal Integration of Distributed Renewable Generation for ...

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2024 IEEE PES/IAS PowerAfrica, PowerAfrica 2024

作者： Aderibigbe, Adeleke Adoghe, Anthony Agbetuyi, Felix Airoboman, Abel Ogu, Reginald Covenant University Department of Electrical & Information Engineering Ota Nigeria Nigerian Defence Academy Department of Electrical/Electronic Engineering Kaduna Nigeria Federal University of Technology Department of Computer Engineering Owerri Nigeria

ISBN: (纸本)9798350389388

The fundamental characteristics of electrical power system is the provision of economical and reliable electricity to the consumers. The inability of the Centralized Generation (CG) which are mainly from fossil fuels to provide reliable electrical power necessitate the need for Distributed Renewable Generation (DRG) to augment the existing power system at the distribution level. Therefore, this paper presents a study on how to integrate DRG optimally in a real distribution network for reliability enhancement using Cuckoo Search Algorithm (CSA). The line, load and reliability parameters/data of the selected WAEC 11kV distribution network were collected, processed and analysed on developed single line diagram. Cuckoo search algorithm was used for identifying the DRGs optimal location and size in the developed distribution network while Backward-Forward Sweep method in MATLAB 2021 software environment was used to determine the power loss and reliability indices with and without DGs integration. The result at base case revealed a power loss of 2120kW at bus 85 with a minimum voltage of 0.9368 with reliability indices values: SAIFI, SAIDI, CAIDI and ASUI given as 10.9019f/c. yr, 38.2089hr/c. yr, 3.5048hr/*** and 4.3617-3 p.u. respectively. Therefore, with 3DGs injected the power loss reduced to 970.6kW at an improved voltage of 0.95899 at 83bus. The percentage reduction in SAIFI, SAIDI, CAIDI and ASUI are 1.28%, 22.96%, 21.96% and 21.96% respectively which resulted in enhanced reliability. © 2024 IEEE.

关键词： Power distribution reliability

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Business intelligent connection between quality and evaluation methods: a case study

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International Journal of Applied Systemic Studies 2022年第4期9卷 330-343页

作者： Mandellos, Georgios Tsipianitis, Dimitris Telecommunications and Information Technology Division Electrical and Computer Engineering Department University of Patras RionGR-26504 Greece Systems and Automatic Control Division Electrical and Computer Engineering Department University of Patras RionGR-26504 Greece

Nowadays, information and knowledge represent the fundamental wealth of an organisation. Enterprises try to utilise this wealth to gain advantage when making decisions such as a project’s acceptance. After a project finishes, a quality management plan defines its quality level. The business intelligence theories help the individual to evaluate the proposed plan in order to target the project’s results. But, do all the evaluation methods have the same reliability? We check the project’s quality by using paper and online questionnaire administration. Although the scope is the same, there are differences, giving different results. So, we intelligently investigated the quality of the university educational process. The results show that the administration method has a significant effect on overall results. Thus, the quality check of a project using questionnaires needs to have an attentive design, avoid points that introduce uncertainties and able to show the real opinion of the respondents. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Surveys

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