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DNACDS:Cloud IoE big data security and accessing scheme based on DNA cryptography

Frontiers of computer science 2024年第1期18卷 157-170页

作者： Ashish SINGH Abhinav KUMAR Suyel NAMASUDRA School of Computer Engineering KIIT Deemed to be UniversityBhubaneshwar 751024India Department of Computer Science and Engineering Indian Institute of Information Technology SuratSurat 394190India Department of Computer Science and Engineering National Institute of Technology AgartalaAgartala 799046India

The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data *** approach allows efficient infrastructure to store and access big real-time data and smart IoE services from the *** IoE-based cloud computing services are located at remote locations without the control of the data *** data owners mostly depend on the untrusted Cloud Service Provider(CSP)and do not know the implemented security *** lack of knowledge about security capabilities and control over data raises several security *** Acid(DNA)computing is a biological concept that can improve the security of IoE big *** IoE big data security scheme consists of the Station-to-Station Key Agreement Protocol(StS KAP)and Feistel cipher *** paper proposed a DNA-based cryptographic scheme and access control model(DNACDS)to solve IoE big data security and access *** experimental results illustrated that DNACDS performs better than other DNA-based security *** theoretical security analysis of the DNACDS shows better resistance capabilities.

关键词： IoE based cloud computing DNA cryptography IoE big data security StS KAP feistel cipher IoE big data access

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Deep Learning-Based Cloud Security: Innovative Attack Detection and Privacy Focused Key Management

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IEEE Transactions on computers 2025年第6期74卷 1978-1989页

作者： Ahmad, Shahnawaz Arif, Mohd Mehfuz, Shabana Ahmad, Javed Nazim, Mohd Bennett University School of Computer Science Engineering and Technology Greater Noida201310 India Galgotias University School of Computer Science & Engineering Greater Noida203201 India Jamia Millia Islamia Department of Electrical Engineering New Delhi110025 India Sharda University Department of Computer Science and Engineering Sharda School of Engineering and Technology Greater Noida201310 India Noida Institute of Engineering and Technology Department of Computer Science and Engineering School of Computer Science and IT Greater Noida201306 India

Cloud Computing (CC) is widely adopted in sectors like education, healthcare, and banking due to its scalability and cost-effectiveness. However, its internet-based nature exposes it to cyber threats, necessitating advanced security frameworks. Traditional models suffer from high false positives and limited adaptability. To address these challenges, VECGLSTM, an attack detection model integrating Variable Long Short-Term Memory (VLSTM), capsule networks, and the Enhanced Gannet Optimization Algorithm (EGOA), is introduced. This hybrid approach enhances accuracy, reduces false positives, and dynamically adapts to evolving threats. EGOA is employed for its superior optimization capability, ensuring faster convergence and resilience. Additionally, Chaotic Cryptographic Pelican Tunicate Swarm Optimization (CCPTSO) is proposed for privacy-preserving key management. This model combines chaotic cryptographic techniques with the Pelican Tunicate Swarm Optimization Algorithm (PTSOA), leveraging the pelican algorithm’s exploration strength and the tunicate swarm’s exploitation ability for optimal encryption security. Performance evaluation demonstrates 99.675% accuracy, 99.5175% recall, 99.7075% precision, and 99.615% F1-score, along with reduced training (1.79s), encryption (0.986s), and decryption (1.029s) times. This research significantly enhances CC security by providing a scalable, adaptive framework that effectively counters evolving cyber threats while ensuring efficient key management. © 1968-2012 IEEE.

关键词： Cloud security

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Research on Image Defogging Algorithm Based on Improved FFA-Net

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第6期51卷 634-641页

作者： Qinrong, Li Chi, Ma Qiang, Guo Hui, Hu School of Computer Science and Software Engineering University of Science and Technology LiaoNing AnShan114051 China School of Computer Science and Engineering Huizhou University Huizhou516007 China

Images captured under severe weather conditions, such as haze and fog, suffer from image quality degradation caused by atmospheric particle diffusion. This degradation manifests as color fading, reduced contrast, and adversely affects the performance of various computer vision tasks. To address this, this paper presents an end-to-end feature fusion attention network (FFA-Net) designed to directly restore haze-free images. By incorporating the SSIM loss into the original loss function, the proposed method effectively captures the visual disparities between the estimated defogged image and the authentic haze-free image. Additionally, it mitigates the color distortion problem inherent in the original algorithm. To address the challenge of low brightness in input images, a low illumination enhancement module is introduced, seamlessly integrated with the FFA-Net defogging method. Subsequently, a comparative analysis of different defogging algorithms is conducted using two distinct foggy datasets. Multiple evaluation metrics are employed to assess the performance of these algorithms. The findings indicate that our algorithm significantly outperforms others in terms of objective indicators such as PSNR and SSIM, as well as visual effects. © (2024), (International Association of Engineers). All rights reserved.

关键词： Image enhancement

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Semantic Communications for Digital Signals via Carrier Images

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IEEE Wireless Communications Letters 2025年第6期14卷 1816-1820页

作者： Yan, Zhigang Li, Dong Macau University of Science and Technology School of Computer Science and Engineering China

Most of current semantic communication (SemCom) frameworks focus on the image transmission, which, however, do not address the problem on how to deliver digital signals without any semantic features. This paper proposes a novel SemCom approach to transmit digital signals by using the image as the carrier signal. Specifically, the proposed approach encodes the digital signal as a binary stream and maps it to mask locations on an image. This allows binary data to be visually represented, enabling the use of existing model, pre-trained Masked Autoencoders (MAE), which are optimized for masked image reconstruction, as the SemCom encoder and decoder. Since MAE can both process and recover masked images, this approach allows for the joint transmission of digital signals and images without incurring significant communication overheads. In addition, considering the mask tokens transmission encoded by the MAE still faces extra costs, we design a sparse encoding module at the transmitter to encode the mask tokens into a sparse matrix, and it can be recovered at the receiver. Thus, this approach simply needs to transmit the latent representations of the unmasked patches and a sparse matrix, which further reduce the transmission overhead compared with the original MAE encoder. Simulation results show that the approach maintains reliable transmission even in a high mask ratio of images. © 2012 IEEE.

关键词： Encoding (symbols)

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An Apricot Detection Algorithm in Complex Environments Based on Improved YOLOv7

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第12期51卷 2135-2144页

作者： Guo, Qiang Ma, Chi Hu, Hui School of Computer Science and Software Engineering University of Science and Technology LiaoNing AnShan114051 China School of Computer Science and Engineering Huizhou University Huizhou516007 China

Apricot detection is a prerequisite for counting and harvesting tasks. Existing algorithms face challenges in adapting to the impacts of complex environmental factors such as lighting variations, shadows, dense foliage, and the uneven distribution of samples in mechanized apricot harvesting. This paper proposes an enhanced model, YOLOv7-DC, based on YOLOv7, to address these challenges. YOLOv7-DC preprocesses diverse apricot tree samples to accommodate real-world harvesting detection scenarios. To improve model inference speed and detection accuracy, the detection network is redesigned with a new feature fusion method. DCNv2 is embedded within the efficient layer aggregation network (ELAN), and PConv is introduced to replace conventional convolutions, reducing the parameter impact of DCNv2. The training process incorporates the CBAM attention mechanism to enhance spatial and channel information. The ConvMixer architecture captures spatial and channel relationships transmitted to the detection head through the attention mechanism, improving the model’s detection accuracy for each specific classification sample. Experimental results show that YOLOv7-DC maintains good detection speed and recognition rates across various classification tasks. The improved model achieves a 6.2% increase in average detection accuracy compared to previous algorithms, with a 13% reduction in model parameters. YOLOv7-DC is better suited for handling imbalanced samples and complex environmental scenarios. © (2024), (International Association of Engineers). All rights reserved.

关键词： Apricot biloba detection Attention mechanism Feature fusion YOLOv7

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Data-Driven Collaborative Scheduling Method for Multi-Satellite Data-Transmission

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Tsinghua science and Technology 2024年第5期29卷 1463-1480页

作者： Xiaoyu Chen Weichao Gu Guangming Dai Lining Xing Tian Tian Weilai Luo Shi Cheng Mengyun Zhou School of Computer Science China University of GeosciencesWuhan 430074China School of Electronic Engineering Xidian UniversityXi’an 710071China School of Computer Science Shaanxi Normal UniversityTaiyuan 710119China

With continuous expansion of satellite applications,the requirements for satellite communication services,such as communication delay,transmission bandwidth,transmission power consumption,and communication coverage,are becoming *** paper first presents an overview of the current development status of Low Earth Orbit(LEO)satellite constellations,and then conducts a demand analysis for multi-satellite data transmission based on LEO satellite *** problem is described,and the challenges and difficulties of the problem are analyzed *** this basis,a multi-satellite data-transmission mathematical model is then *** classical heuristic allocating strategies on the features of the proposed model,with the reinforcement learning algorithm Deep Q-Network(DQN),a two-stage optimization framework based on heuristic and DQN is ***,by taking into account the spatial and temporal distribution characteristics of satellite and facility resources,a multi-satellite scheduling instance dataset is *** results validate the rationality and correctness of the DQN algorithm in solving the collaborative scheduling problem of multi-satellite data transmission.

关键词： relay satellite scheduling data transmission Deep Q-Network(DQN) Genetic Algorithm(GA)

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Privacy-Enhanced Federated Expanded Graph Learning for Secure QoS Prediction

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IEEE Transactions on Services Computing 2025年第03期18卷 1641-1654页

作者： Zou, Guobing Yan, Zhi Hu, Shengxiang Gan, Yanglan Zhang, Bofeng Chen, Yixin Shanghai University School of Computer Engineering and Science Shanghai200444 China Donghua University School of Computer Science and Technology Shanghai201620 China Shanghai Polytechnic University School of Computer and Information Engineering Shanghai201209 China Washington University Department of Computer Science and Engineering St. LouisMO63130 United States

Current state-of-the-art QoS prediction methods face two main limitations. Firstly, most existing QoS prediction approaches are centralized, gathering all user-service invocation QoS records for training and optimization, which causes privacy breaches. While some federated learning-based methods consider user privacy in a distributed way, they either directly upload local trained parameters or use simple encryption for global aggregation at the central server, thus failing to truly protect user privacy. Secondly, existing federated learning-based methods neglect distributed user-service topology and latent behavior-attribute correlations, compromising QoS prediction accuracy. To address these limitations, we propose a novel framework named Privacy-Enhanced Federated Expanded Graph Learning (PE-FGL) for secure QoS prediction. It first conducts user-service expansion on the invocation graph with advanced privacy-preserving techniques, upgrading first-order local QoS invocations to high-order interaction relationships. Then, it extracts hybrid features from the expanded invocation graph via deep learning and graph residual learning. Finally, a two-layer secure mechanism of federated parameters aggregation is designed to enable collaborative learning among users through local parameter segmentation and global aggregation, achieving effective and secure QoS prediction. Extensive experiments on WS-DREAM demonstrate effective QoS prediction across multiple metrics while preserving privacy in user-service invocations. © 2025 IEEE.

关键词： Federated learning

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Surface Defect Detection Algorithm of Hot-Rolled Strip Based on Improved YOLOv7

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第4期51卷 345-354页

作者： Shen, Lijia Cui, Wenhua Tao, Ye Shi, Tianwei Liao, Jinzhen School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China

To enhance the capability of classifying and localizing defects on the surface of hot-rolled strips, this paper proposed an algorithm based on YOLOv7 to improve defect detection. The BI-SPPFCSPC structure was incorporated into the feature pyramid in this algorithm, enabling enhanced extraction of features from small objects and improved accuracy in network model positioning. Additionally, a small object detection layer was introduced to enhance shallow feature capture. Then the CARAFE sampling operator was used for up-sampling to reduce the feature loss problem of small objects. Finally, the WIoU served as the loss function for network model training to expedite convergence. The NEU-DET dataset was utilized for ablation and comparison tests. The findings indicated that the enhanced YOLOv7 model's mAP value had increased to 80.7%. The detection impact was much enhanced in comparison to other traditional models, and the frequency of false and missing detections was also decreased. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Surface defects

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When Software Security Meets Large Language Models:A Survey

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 317-334页

作者： Xiaogang Zhu Wei Zhou Qing-Long Han Wanlun Ma Sheng Wen Yang Xiang IEEE the School of Computer and Mathematical Sciences the University of Adelaide the School of Science Computing and Engineering Technologies Swinburne University of Technology

Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, software testing and analysis are two of the critical methods, which significantly benefit from the advancements in deep learning technologies. Due to the successful use of deep learning in software security, recently,researchers have explored the potential of using large language models(LLMs) in this area. In this paper, we systematically review the results focusing on LLMs in software security. We analyze the topics of fuzzing, unit test, program repair, bug reproduction, data-driven bug detection, and bug triage. We deconstruct these techniques into several stages and analyze how LLMs can be used in the stages. We also discuss the future directions of using LLMs in software security, including the future directions for the existing use of LLMs and extensions from conventional deep learning research.

关键词： Large language models (LLMs) software analysis software security software testing

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Existence and uniqueness of mean field equilibrium in continuous bandit game

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science China(Information sciences) 2025年第3期68卷 395-396页

作者： Xiong WANG Yuqing LI Riheng JIA School of Computer Science and Technology Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan University Wuhan University Shenzhen Research Institute School of Computer Science and Technology Zhejiang Normal University

Multiarmed bandit(MAB) models are widely used for sequential decision-making in uncertain environments, such as resource allocation in computer communication systems.A critical challenge in interactive multiagent systems with bandit feedback is to explore and understand the equilibrium state to ensure stable and tractable system performance.

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