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International Conference on Engineering and Emerging Technologies (ICEET)

作者： Irfan Ahmed Sadia Naseer Umm - e - Habiba Jabar Mahmood Shehzad Ashraf Chaudhry Khadija Batool Department of Computer Science University of Sialkot Sialkot Pakistan State Key Laboratory of Blockchain School of Cyber Science and Technology College of Computer Science and Technology Zhejiang University Hangzhou China Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi U.A.E International Institute of Science Arts and Technology (IISAT) Gujranwala Pakistan

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

Formal methods (FM) are innovative methods which employ mathematical notations to specify explicit and precise requirements. This paper presents a formal model of an automated teller machine (ATM) using Object Constraint Language (OCL) to illustrate the benefits of employing FM to develop commercial applications. FM provide a mathematically rigorous framework for specifying, developing, and verifying software systems. It enhances reliability, robustness, and security. FM plays a crucial role in the context of ATM, which requires high levels of trust and correctness due to their financial and security-critical nature. Using OCL, we demonstrate how formal specifications can be applied to define an ATM system's precise behavior and constraints. It allows for the early detection and correction of errors, reducing development costs and improving overall system quality. This study argues that adopting FM in commercial applications not only boosts the reliability and security of the systems but also increases customer satisfaction and trust. Results of ATM development under OCL have been compared with traditional methods (TM), showing reductions such as 51 % in development time, 53 % in cost, 94 % in ambiguity and 97% in quality and customer trust.

关键词： Frequency modulation Costs Automated teller machine Software systems Software reliability security Formal specifications Stakeholders Testing Software engineering

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Joint Secure Beamforming for RIS-Aided Integrated Multibeam-Satellite Terrestrial Networks

Joint Secure Beamforming for RIS-Aided Integrated Multibeam-...

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Future Communications and Networks (FCN), International Conference on

作者： Yu Zhang Yang Yang Shuangrui Zhao Teng Li Yulong Shen Xiaohong Jiang Jianfeng Ma School of Computer Science and Technology Xidian University Xi’an China Shaanxi Key Laboratory of Network and System Security Xidian University Xi’an China School of Cyber Engineering Xidian University Xi’an China School of Systems Information Science Future University Hakodate Japan

ISBN: (数字)9798350396034

ISBN: (纸本)9798350396041

This paper focuses on a reconfigurable intelligent surface (RIS) enabled integrated multibeam-satellite terrestrial network, which consists of a satellite, a base station (BS), an RIS, multiple earth stations (ESs), an eavesdropper (Eve) and a terrestrial user (TU). In such a network, the satellite provides broadband services to multiple ESs, while the BS transmits friendly interference to the Eve for crippling confidential information interception with the help of the RIS. To enhance the security performance, we provide a theoretical modelling secrecy rate (SR) and formulate the SR maximization problem subject to the signal-to-interference-plus-noise ratio (SINR) requirements, the total transmit power limit and the RIS unitmodulus constraints. By combining approaches like alternative optimization (AO), bisection search, semi-definite programming (SDP), eigenvalue decomposition, and Gaussian randomization, we then propose a joint secure beamforming scheme to solve the non-convex optimization problem and thus identify the optimal beamforming designs for the satellite, the BS, and the RIS. Simulation results indicate the effectiveness of security enhancement via the proposed joint secure beamforming scheme.

关键词： Satellites Array signal processing Simulation Interference Reconfigurable intelligent surfaces Space-air-ground integrated networks Search problems

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Efficient multimodal cancelable biometric system based on steganography and cryptography

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Iran Journal of computer science 2023年第2期6卷 109-121页

作者： Salama, Gerges M. El-Gazar, Safaa Nassar, Rana M. El-Shafai, Walid Khalaf, Ashraf A. M. El-Banby, Ghada M. Hamed, Hesham F. A. El-Samie, Fathi E. Abd Department of Electrical Engineering Faculty of Engineering Minia University Minia 61111 Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Security Engineering Laboratory Department of Computer Science Prince Sultan University Riyadh 11586 Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electrical Engineering Faculty of Engineering Egyptian-Russian University Cairo Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh 84428 Saudi Arabia

Due to the development of hacking programs, it has become easy to penetrate systems. Hence, there is a need for strong security mechanisms. The use of traditional passwords has become insufficient to secure systems. Biometric authentication is now widely used for security applications, and it has proven to be superior compared to traditional authentication methods. However, two issues need to be considered in biometric systems. The first is not to keep biometric data in its original form in the database. If biometric traits are hacked, they will no longer be of use. Biometric data should be kept in cancelable forms for reuse. The second issue is the reliance on a single biometric, which limits the verification accuracy. This can be solved by using a multimodal biometric system. Using steganography and cryptography, this paper introduces a cancelable multimodal biometric system. As voiceprints, facial images, and fingerprint images are used. In this paper, the verification is performed through the Mel frequency cepstral coefficients (MFCCs) of the voiceprints. Steganography is used as a tool to secure features extracted from voiceprints by embedding them into the facial image using block-based singular value decomposition (BSVD). Double random phase encoding (DRPE) is utilized as an encryption algorithm to generate the final cancelable templates. To increase the level of system security, fingerprint images are used as random phase masks (RPMs). Verification is performed by estimating the correlation between registered and test MFCCs. The correlation value is then compared with a threshold value, which is calculated using the distribution curves for the genuine and imposter correlations. Equal error rate (EER) values close to zero and an area under the receiver operator characteristic curve (AROC) that is close to one are obtained from the simulation results, demonstrating the outstanding performance of the suggested system. The proposed system achieves good performan

关键词： BSVD Cancelable biometrics DRPE MFCCs

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Significance-aware Medication Recommendation with Medication Representation Learning

Significance-aware Medication Recommendation with Medication...

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International Conference on computer Supported Cooperative Work in Design

作者： Yishuo Li Zhufeng Shao Weimin Chen Shoujin Wang Yuehan Du Wenpeng Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Mental Health Center Jinan China Data Science Institute University of Technology Sydney Sydney Australia Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The goal of medication recommendation system is to recommend appropriate pharmaceutical interventions based on a patient’s diagnosis. Most of existing approaches often formulate these recommendations use data on diagnoses, procedures, and prescriptions accumulated in the electronic health records (EHR), and despite the great successes, they seem to have limitations on modelling the significance of medication to a patient’s current visit and mining fine-grained medication representation information. To address these issues, we propose a novel Significanceaware Medication Recommendation (SMRec) framework built on significance of medication to patients and fine-grained medication representation learning. Specifically, we first design a encoding mechanism to compute significance information of medications for each patient’s visit. Then, we utilize the set-level medication co-occurrence graph based on patients’ medical history which integrates temporal dependency to learn fine-grained medication representations. Experimental results on the publicly available MIMIC-III dataset demonstrate the superior effectiveness of our model compared to other approaches 1 .

关键词： Representation learning Drugs Federated learning Computational modeling MIMICs Encoding History

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Safe Probabilistic Invariance Verification for Stochastic Discrete-Time Dynamical systems

Safe Probabilistic Invariance Verification for Stochastic Di...

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IEEE Conference on Decision and Control

作者： Yiqing Yu Taoran Wu Bican Xia Ji Wang Bai Xue Department of Information Science School of Mathematical Sciences Peking University China State Key Laboratory of Computer Science Institute of Software CAS China State Key Laboratory of High Performance Computing and College of Computer Science and Technology National University of Defense Technology China

Ensuring safety through set invariance has proven a useful method in a variety of applications in robotics and control. In this paper, we focus on the safe probabilistic invariance verification problem for discrete-time dynamical systems subject to stochastic disturbances over the infinite time horizon. Our goal is to compute the lower and upper bounds of the liveness probability for a given safe set and set of initial states. This probability represents the likelihood that the system will remain within the safe set for all time. To address this problem, we draw inspiration from stochastic barrier certificates for safety verification and build upon the findings in [21], where an equation was presented for exact probability analysis. We present two sets of optimizations and demonstrate their effectiveness through two examples, using semi-definite programming tools.

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Online Discriminative Semantic-Preserving Hashing for Large-Scale Cross-Modal Retrieval 18th

Online Discriminative Semantic-Preserving Hashing for Large-...

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Yi, Jinhan He, Yi Liu, Xin Department of Computer Science and Technology Huaqiao University Xiamen361021 China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing210094 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Xiamen China Fujian Key Laboratory of Big Data Intelligence and Security Xiamen China

ISBN: (纸本)9783030891879

Cross-modal hashing has drawn increasing attentions for efficient retrieval across different modalities, and existing methods primarily learn the hash functions in a batch based mode, i.e., offline methods. Nevertheless, the multimedia data often comes in a streaming fashion, which makes the batch based learning methods uncompetitive for large-scale streaming data due to the large memory consumption and calculation. To address this problem, we present an Online Discriminative Semantic-Preserving Hashing (ODSPH) method for large-scale cross-modal retrieval, featuring on fast training speed, low memory consumption and high retrieval accuracy. Within the proposed ODSPH framework, we utilize the newly coming data points to learn the hash codes and update hash functions in a stream manner. When new data comes, the corresponding hash codes are obtained by regressing the class label of the training examples. For hash function, we update it with the accumulated information from each round. Besides, we design a novel momentum updating method to adaptively update the hash function and reduce quantization loss, which can produce discriminative hash codes for high retrieval precision. Extensive experiments on three benchmark datasets show that the proposed ODSPH method improves the retrieval performance over the state-of-the-arts. © 2021, Springer Nature Switzerland AG.

关键词： Semantics

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Federated Learning-Based Intelligent Indoor Smoke and Fire Detection system for Smart Buildings

Federated Learning-Based Intelligent Indoor Smoke and Fire D...

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2024 International Conference on Telecommunications and Intelligent systems, ICTIS 2024

作者： Aymene Berkani, Mohamed Rafik Chouchane, Ammar Himeur, Yassine Abdennebi, Anes Sagiroglu, Seref Amira, Abbes Medea Algeria University Biskra University Center of Barika Laboratory of LI3C Amdoukal Road Barika05001 Algeria University of Dubai College of Engineering and Information Technology Dubai United Arab Emirates École de Technologie Supérieure Software and Information Technology Engineering Montréal Canada Analytics Security R&D Center Gazi University Artificial Intelligence and Big Data Ankara Turkey University of Sharjah Department of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798331527396

Ensuring safety in smart buildings is crucial due to the increasing prevalence of smoke and fire hazards in modern environments. This paper introduces a novel privacy-preserving FL approach based on a CNN1D for smoke and fire detection in smart buildings. Our system integrates data from wearable environmental sensors to train a lightweight, edge-deployable DL-CNN1D model, ensuring data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. Experiments conducted on a comprehensive air measurement dataset for smoke and fire detection demonstrate exceptional performance, with the global model achieving 99.97% accuracy, 99.96% precision, in smoke and fire recognition. Our model demonstrates a low communication cost of 0.4 MB, underscoring its efficiency for real-time applications. Our FL-based approach represents a significant step towards balancing the need for robust safety systems with growing privacy concerns in smart building environments. © 2024 IEEE.

关键词： Federated learning

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Free-space Continuous-variable Quantum Secret Sharing

arXiv

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arXiv 2025年

作者： Yang, Fangli Chang, Liang Qiu, Daowen Pan, Minghua Xiong, Wanjun Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin541004 China

Free-space quantum cryptography has the potential to enable global quantum communication. However, most existing continuous-variable quantum secret sharing (CV-QSS) schemes rely on fiber channels. In this paper, we present a CV-QSS protocol designed for free-space transmission and construct models of crucial parameters, including channel transmittance, excess noise, and interruption probability, thus deriving the bound of the secret key rate. In particular, we provide a multi-source excess noise model for free-space CV-QSS based on the local local oscillator (LLO) scheme and a straightforward optimization of the noise. Furthermore, our research considers the impact of atmospheric turbulence on the protocol. Simulation results demonstrate that the intensification of atmospheric turbulence adversely affects the aforementioned crucial parameters, leading to a significant reduction in the key rate. However, our protocol shows its capability to securely share secrets over a distance of exceeding 60 km among five participants in the presence of turbulence with Cn2 = 1 × 10−15m−2/3, while maintaining a high key rate of approximately 0.55 bit/pulse over a distance of 10 km across twenty participants. These findings suggest that efficient CV-QSS in free space is indeed achievable. This research may serve as a reference for the design and optimization of the practical CV-QSS system. Copyright © 2025, The Authors. All rights reserved.

关键词： Atmospheric turbulence

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Industrial Intrusion Detection technology Based on One-dimensional Multi-scale Residual Network 2021

Industrial Intrusion Detection Technology Based on One-dimen...

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7th International Conference on Computing and Artificial Intelligence, ICCAI 2021

作者： Kong, De Peng Du, Ye Li, Mei Hong Li, Gui Ling Beijing Key Laboratory of Security and Privacy in Intelligent Transportation School of Computer Science and Information Technology Beijing Jiaotong University China National Education Examinations Authority China

ISBN: (纸本)9781450389501

In order to solve the problem that the traditional intrusion detection algorithm cannot learn more information based on the traffic data effectively and the detection accuracy is not ideal, an intrusion detection algorithm based on the one-dimensional multi-scale residual network for industrial control systems is proposed. Firstly, the nondimensionalization of input data is realized by defining the centrosymmetric logarithmic function. Then, a one-dimensional multi-scale residual neural network model is constructed to learn the characteristic information of industrial control data, and through cross-validation, parameter tuning is realized to obtain the best model. The experimental results show that the accuracy of this method is 98.99% and the AUC score is 0.9984, which can effectively realize the intrusion detection function under the industrial control system. © 2021 ACM.

关键词： Deep learning

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Air-Ground Integrated Online Federated Learning Under Unreliable Communication

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Jing, Yuqian Qu, Yuben Wu, Tao Dong, Chao Guo, Song Wu, Qihui Ministry of Industry and Information Technology Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong National University of Defense Technology Anhui Hefei230009 China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong999077 Hong Kong

In recent years, unmanned aerial vehicles (UAVs) equipped with intelligent computing modules for machine learning (ML) have garnered attention. Federated learning (FL), as an emerging distributed learning paradigm, aligns seamlessly with this scenario. UAVs train local models while a terrestrial base station (BS) aggregates the global model, forming an air-ground integrated federated learning (AGIFL) system. However, under air-ground integrated networks with unreliable wireless links, the online retraining of ML models with new samples is promising but challenging in AGIFL. To this end, we study how to enhance the performance of air-ground integrated online FL (AGIOFL) considering packet transmission error. Specifically, we formulate a joint optimization problem for sample selection and client scheduling, aiming to minimize the loss function that maximizes the performance of AGIOFL. To solve the aforementioned problem without explicit expression, we first derive a closed-form expression for the expected convergence rate of the FL algorithm to quantify the impact of AGIOFL and unreliable wireless transmission. Based on this relationship, we reformulate the optimization problem into a mixed-integer nonlinear programming problem. Subsequently, we develop an alternating iterative optimization algorithm (OFL-PE) combining branch-and-bound with the Dinkelbach algorithm to solve this problem. Simulations indicate that OFL-PE converges within 10 iterations only, with a maximum error of less than 7% compared to the exhaustive brute force approach. In addition, we construct a proof-of-concept system based on widely used UAV embedded computers, and experimental results show that OFL-PE demonstrates superior accuracy, convergence, and robustness compared to three existing FL algorithms. © 2015 IEEE.

关键词： Federated learning

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