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10th International Conference on Signal Processing and Communication, ICSC 2025

作者： Oghorodi, Duke Kingsley, Igulu Singh, Thipendra P Atajeromavwo, Edafe J. Nwankwo, Wilson Delta State University of Science & Technology Department of Comp. Sc. Ozoro Nigeria Kenule Beeson Saro-Wiwa Polytechnic Bori Department of Computer Science Bori Nigeria School of Computer Science Engineering & Technology Bennett University Greater Noida India Delta State University of Science & Technology Department of Cyber Security Ozoro Nigeria

ISBN: (纸本)9798331511890

This study presents a novel hybrid diagnostic model for early-stage prostate cancer, integrating genetic algorithms, neuro-fuzzy logic, and mobile agent technology. By optimizing the selection of key genetic markers, the system achieves a marked improvement in sensitivity and specificity-up to a 12% increase over conventional diagnostic methods-based on extensive simulations and a controlled clinical dataset. The neuro-fuzzy component adaptively merges genetic, clinical, and historical patient data, refining its predictive accuracy through continuous learning. A decentralized computation strategy enhances scalability and lowers device-level computational overhead, supporting widespread implementation in diverse healthcare environments. While initial results underscore its promise, validation through large-scale clinical trials remains a priority. This approach offers a practical, high-accuracy solution that could significantly reduce late-stage prostate cancer diagnoses, particularly in resourcelimited settings, and stands as a step forward in precision oncology diagnostics. The developed approach can be helpful in diagnosis of the relevant healthcare problem. © 2025 IEEE.

关键词： Fuzzy Logic Genetic Algorithm GNF Inference Model Neural Network Prostate Cancer

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A divide-and-conquer reconstruction method for defending against adversarial example attacks

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Visual Intelligence 2024年第1期2卷 1-17页

作者： Liu, Xiyao Hu, Jiaxin Yang, Qingying Jiang, Ming He, Jianbiao Fang, Hui School of Computer Science and Engineering Central South University Hunan Changsha 410083 China Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Guilin China Department of Computer Science Loughborough University Loughborough LE113TU United Kingdom

In recent years, defending against adversarial examples has gained significant importance, leading to a growing body of research in this area. Among these studies, pre-processing defense approaches have emerged as a prominent research direction. However, existing adversarial example pre-processing techniques often employ a single pre-processing model to counter different types of adversarial attacks. Such a strategy may miss the nuances between different types of attacks, limiting the comprehensiveness and effectiveness of the defense strategy. To address this issue, we propose a divide-and-conquer reconstruction pre-processing algorithm via multi-classification and multi-network training to more effectively defend against different types of mainstream adversarial attacks. The premise and challenge of the divide-and-conquer reconstruction defense is to distinguish between multiple types of adversarial attacks. Our method designs an adversarial attack classification module that exploits the high-frequency information differences between different types of adversarial examples for their multi-classification, which can hardly be achieved by existing adversarial example detection methods. In addition, we construct a divide-and-conquer reconstruction module that utilizes different trained image reconstruction models for each type of adversarial attack, ensuring optimal defense effectiveness. Extensive experiments show that our proposed divide-and-conquer defense algorithm exhibits superior performance compared to state-of-the-art pre-processing methods. © The Author(s) 2024.

关键词： Adversarial attack multi-classification Adversarial example defense Divide-and-conquer strategy Reconstruction network

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ESP32-based Multi-User Secret Key Generation System 8

ESP32-based Multi-User Secret Key Generation System

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8th International Conference on Signal and Image Processing, ICSIP 2023

作者： Li, Dongyang Peng, Linning Fu, Hua Wang, Fengsheng Tian, Ye He, Shen Southeast University School of Cyber Science and Engineering Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Research Institute of China Mobile Communications Corporation Department of Security Technology Beijing China

ISBN: (纸本)9798350397932

This paper proposes a multi-user secret key generation system using off-the-shelf ESP32 modules. We first propose a new differential quantization algorithm and contrast the features of differential quantization with uniform quantization and existing equal-probability quantization. Then, we propose a differential compensation algorithm, which can effectively improve the randomness of differential quantization. The improved randomness is higher than that of equal-probability quantization, and the key disagreement rate (KDR) is lower than that of equal-probability quantization. After that, we create an ESP32-based multi-user secret key generation system. We compare the differences of KDR in three movement modes, stable, people-move and trolly-move, and in two scenarios, line-of-sight (LOS) and non-line-of-sight (NLOS). The results show that the KDR with third-order quantization is less than 0.1 for the LOS scene and less than 0.15 for the NLOS scene. © 2023 IEEE.

关键词： Random processes

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A Highly Robust Secret Key Reconcile System Based on Cyclic Shift Buffer and HARQ Mechanism 8

A Highly Robust Secret Key Reconcile System Based on Cyclic ...

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8th International Conference on Signal and Image Processing, ICSIP 2023

作者： Han, Haojie Peng, Linning Fu, Hua Cheng, Yexia Tian, Ye He, Shen Southeast University School of Cyber Science and Engineering Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Research Institute of China Mobile Communications Corporation Department of Security Technology Beijing China

ISBN: (纸本)9798350397932

Secret keys generated from wireless channel can provide high level of security. The error correction code (ECC) based information reconciliation method owns the benefits of high security and efficiency. In this paper, we designed a highly robust secret key reconcile system using low-density parity-check (LDPC) codes to correct disagreed key bits. In this system, we cache some high-quality secret key bits in a cyclic shift buffer (CSB) for compensation, which can effectively reduce the over-all key disagreement rate (KDR). Combined with a simple hybrid automatic repeat request (HARQ) implementation, this system significantly improves the correction success rate of information reconciliation. We built a realtime secret key generation system using universal software radio peripheral (USRP). Extensive experiments were carried out in different channel state information (CSI) scenarios. The proposed system can improve the success rate of secret key generation effectively in all experiment scenarios, especially in the case of low USRP Tx/Rx gain setups. © 2023 IEEE.

关键词： Software radio

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Single Qubit Quantum Logistic-Sine XYZ-Rotation Maps:An Ultra-Wide Range Dynamics for Image Encryption

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computers, Materials & Continua 2025年第5期83卷 2161-2188页

作者： De Rosal Ignatius Moses Setiadi T.Sutojo Supriadi Rustad Muhamad Akrom Sudipta Kr Ghosal Minh T.Nguyen Arnold Adimabua Ojugo Research Center for Quantum Computing and Materials Informatics Faculty of Computer ScienceDian Nuswantoro UniversitySemarang50131Indonesia Department of Cyber Forensics and Information Security Behala Government Polytechnic756Upendra Nath Banerjee RoadParnasreeBehalaKolkata700060India Thai Nguyen University of Technology Thai Nguyen UniversityThai Nguyen240000Vietnam Department of Computer Science Federal University of Petroleum ResourcesEffurun330102Nigeria

Data security has become a growing priority due to the increasing frequency of cyber-attacks,necessitating the development of more advanced encryption *** paper introduces Single Qubit Quantum Logistic-Sine XYZ-Rotation Maps(SQQLSR),a quantum-based chaos map designed to generate one-dimensional chaotic sequences with an ultra-wide parameter *** proposed model leverages quantum superposition using Hadamard gates and quantum rotations along the X,Y,and Z axes to enhance *** numerical experiments validate the effectiveness of *** proposed method achieves a maximum Lyapunov exponent(LE)of≈55.265,surpassing traditional chaotic maps in *** bifurcation analysis confirms a uniform chaotic distribution,eliminating periodic windows and ensuring higher *** system also generates an expanded key space exceeding 10^(40),enhancing security against brute-force ***,SQQLSR is applied to image encryption using a simple three-layer encryption scheme combining permutation and substitution *** approach is intentionally designed to highlight the impact of SQQLSR-generated chaotic sequences rather than relying on a complex encryption *** method achieves an average entropy of 7.9994,NPCR above 99.6%,and UACI within 32.8%–33.8%,confirming its strong randomness and sensitivity to minor *** robustness tests against noise,cropping,and JPEG compression demonstrate its resistance to statistical and differential ***,the decryption process ensures perfect image reconstruction with an infinite PSNR value,proving the algorithm’s *** results highlight SQQLSR’s potential as a lightweight yet highly secure encryption mechanism suitable for quantum cryptography and secure communications.

关键词： Single qubit quantumchaotic quantumchaotic map quantumimage encryption quantumlogistic map quantum sine map

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Classification of Sleep-Related Disorders Using Machine Learning Techniques 25

Classification of Sleep-Related Disorders Using Machine Lear...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Nasir, Ola Alazaidah, Raed Al-Safarini, Maram Y. Alamleh, Amneh Zaid, Ali Abu Mansur, Hasan Elrashidi, Ali Alzoubi, Haneen Bani-Bakr, Alaa Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan American University of Madaba Faculty of Business Department of Accounting Madaba Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Private University Faculty of It Applied Science Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

Several people suffer from irregular sleep patterns, a problem that negatively affects their daily lives. It presents a multitude of challenges, including daytime distress, disturbances in sleep-wake patterns, feelings of unease, and a variety of other complications. Diabetes, obesity, and psychological disorders are among the diseases that may be responsible for this sleep disorder. Therefore, the primary aim of this research is to employ the robust capabilities of machine learning in forecasting sleep disorders. This research classifies three diseases: diabetes, obesity, and psychological disorders, using thirty-two different classifiers belonging to five learning strategies. The research compares and evaluates their predictive performance on four datasets. The comparative analysis includes four evaluation metrics: Accuracy, Precision, ROC Area, and MCC. The research results indicate that the best classifier is SGD, followed by LMT, while NBM is the weakest classifier. In addition, the research results revealed that the best strategy is Functions, followed by Rules, with Bayes being the least effective strategy. While conducting the experiment and analyzing the data, it is observed that the best feature selection method that yielded the most favorable outcome is the CorrelationAttributeEval. Furthermore, it has been found that the classification algorithm known as SGD achieved the highest Accuracy and Precision in categorizing the data. This indicates that the combination of CorrelationAttributeEval as the feature selection approach and SGD as the classification algorithm proved to be the most successful and efficient strategy for this research © 2024 IEEE.

关键词： Sleep research

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A Review of Lightweight security and Privacy for Resource-Constrained IoT Devices

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computers, Materials & Continua 2024年第1期78卷 31-63页

作者： Sunil Kumar Dilip Kumar Ramraj Dangi Gaurav Choudhary Nicola Dragoni Ilsun You Department of Computer Science and Engineering National Institute of Technology JamshedpurJamshedpurIndia School of Computing Science and Engineering VIT University BhopalBhopal466114India Center for Industrial Software The Maersk Mc-Kinney Moller InstituteUniversity of Southern DenmarkSonderborgOdenseDenmark Department of Applied Mathematics and Computer Science Technical University of DenmarkLyngby2800Denmark Department of Information Security Cryptologyand MathematicsKookmin UniversitySeoulSouth Korea

The widespread and growing interest in the Internet of Things(IoT)may be attributed to its usefulness in many different *** settings are probed for data,which is then transferred via linked *** are several hurdles to overcome when putting IoT into practice,from managing server infrastructure to coordinating the use of tiny *** it comes to deploying IoT,everyone agrees that security is the biggest *** is due to the fact that a large number of IoT devices exist in the physicalworld and thatmany of themhave constrained resources such as electricity,memory,processing power,and square *** research intends to analyse resource-constrained IoT devices,including RFID tags,sensors,and smart cards,and the issues involved with protecting them in such restricted *** lightweight cryptography,the information sent between these gadgets may be *** order to provide a holistic picture,this research evaluates and contrasts well-known algorithms based on their implementation cost,hardware/software efficiency,and attack resistance *** also emphasised how essential lightweight encryption is for striking a good cost-to-performance-to-security ratio.

关键词： IoT a sensor device lightweight cryptography block cipher smart card security and privacy

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An Authentication Model Using Brainwave Panic Region Classifications from Electroencephalography 7th

An Authentication Model Using Brainwave Panic Region Classif...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Abiodun, Opeyemi Anuoluwa Oyinloye, Oghenerukevwe E. Thompson, Aderonke F. Olowoyo, Paul Imoize, Agbotiname Lucky Sur, Samarendra Nath Computer Science Department The Federal Polytechnic Ekiti State Ado-Ekiti360211 Nigeria Computer Science Department Ekiti State University Ekiti State Ado-Ekiti360211 Nigeria Cyber Security Department Federal University of Technology Ondo State Akure350106 Nigeria Federal Teaching Hospital Ekiti State Ido-Ekiti Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos 100213 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India

ISBN: (纸本)9789819764648

Biometric authentication scheme has been widely adopted for authentication purpose due to its uniqueness, universality, and distinctiveness. However, research has shown that these schemes are not necessarily more secured, especially with issues of coercion proliferating the cyberspace. Also, available techniques on repudiation of biometric features for authentication have not adequately explored this exciting topic. Integrating emotional state into the authentication scheme helps to mitigate coercion. This paper presents a framework for emotion as a way of biometric authentication scheme. An emotion classification model was developed by training an emotion classifier brainwave signal from eight subjects under normal state and duress using KNN machine learning algorithm. The authentication scheme grants access to users who pass both verification phases. The emotion classification model achieved an accuracy of 93.6% on the full feature set, while the reduced-feature set, as a result of feature selection, produced an improved accuracy of 94.15%. Hence, the emotion classification model can be integrated into existing biometric authentication system for improved security of critical user information. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Biometrics

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Improved On-Device Hyperdimensional-Computing-based Image Segmentation 3

Improved On-Device Hyperdimensional-Computing-based Image Se...

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3rd IEEE International Conference on Intelligent technology and Embedded Systems, ICITES 2023

作者： Zhou, Xinran Lu, Huayue College of Computer and Network Security Chengdu University of Technology Chengdu China Department of Computer Science and Engineering University of Connecticut StorrsCT United States

ISBN: (纸本)9798350343755

Today’s artificial intelligence technology has made great progress and in many fields involved in artificial intelligence, such as medical imaging, logistics, manufacturing, and other fields, one of the most basic tasks is segmentation. Image segmentation is a more difficult segment in segmentation, and its existing problems are: insufficient labeled data leads to weakened performance, and it is difficult to process on the device. Recently, a hyperdimensional-computing-based unsupervised segmentation approach has been proposed, which can process images on-device. However, there are still some problems, like latency. In this work, we have made some improvements based on previous work and propose ImSegHDC having much lower latency with only a little segmentation performance drop. © 2023 IEEE.

关键词： Image segmentation

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Analysis of Lung Cancer for Developing Smart Healthcare with the Help of BGWO Based TSA-XGBoost Model

Analysis of Lung Cancer for Developing Smart Healthcare with...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Mahaveerakannan, R. Murali Dhar, M.S. Goswami, Sohan Krishna, P. Gopala Jayanthi, K. Kumar, M. Sathish The Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Spatial Informatics India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Technique Polytechnic Institute Department of Computer Science and Technology West Bengal India Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Pollachi Institute of Engineering and Technology Department of Computer Science and Engineering Pollachi Tamil Nadu Coimbatore India Karpagam College of Engineering Department of Cyber Security Tamil Nadu Coimbatore India

ISBN: (纸本)9798350300857

Given the circumstances the healthcare system, the Internet of Things (IoT) is crucial. IoT gadgets offer patient data for the framework of healthcare monitoring. IoT is a key component in every aspect of the health care management system since people can use smart devices to check on their health. Lung cancer is a fatal malignancy, and the likelihood of survival is increased by early identification. Because of the computational difficulty involved in gathering characteristics, it is imperative to design an approach using machine learning techniques for categorising cancer disease because the classification results given by the current methods are inadequate. With the use of the synthetic minority oversampling methods (SMOTE) methodology, the incoming data is pre-processed and balanced. With the help of the binary grey wolf optimisation algorithm (BGWOA), the pertinent features are best chosen. Finally, the suggested model's hyper-parameters are best chosen by the tunicate swarm optimisation (TSA) model, and the classification is carried out by the extreme gradient boosting (XGBoost) model. The experimental analysis demonstrates that the suggested model attained accuracy and recall values of 98% and 95%, respectively, compared to 95% and 95%, respectively, for the identical proposed model without the feature selection (FS) method. © 2023 IEEE.

关键词： Internet of things

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