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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Khwaldeh, Sufian Mohit, Y. Khushwant, S. Department of Network and Information Security College of lnformation Technology University of Fujairah 1207 United Arab Emirates Department of Information Technology Faculty of Information Technology and Systems The University of Jordan Aqaba77110 Jordan Department of Mathematics University Institute of Sciences Chandigarh University Punjab Mohali140413 India Department of Computer Science & Engineering University Institute of Engineering and Technology M. D. University Rohtak124001 India

ISBN: (纸本)9798350394634

Cloud server data security makes extensive use of data centers and instantly accessible edge systems for possible transactions involving sizable and confidential data. These Edge Systems are chosen for rental based on their usefulness in situations where new privacy and security difficulties must be overcome. The use of Edge Systems is subject to dynamic mobility, which increases the danger of attacking threats. With this suggested approach, the Defensive Auto-updatable and Adaptable Bot Recommender approach (DAABRS) is used by the Cloud Center to identify the safeguarded Edge System. Cloud Data Centers with Edge Systems contain a variety of users and environments, making it simple for attackers to enter anonymously and compromise Edge System privacy. The most effective security measure against increasing risks and assaults for Edge Systems that rent more cloud storage is to use Stealth Mode Bots inside of data segments. In addition to anticipating and averting data breaches in server partitioning, tenant systems, edge systems, and containers by integrating stealth mode bots into each. Suggested use cases: Tenants, Edge Systems, or Containers for effective security-enhanced Stealth Mode Bots that are activated when a particular data segment is unplugged. Updatable and adaptive bots are used in the suggested strategy to counter Edge strategy assaults. Additionally, it uses Stealth Mode Bots to stop heavily attacked Data Containers, and machine learning will be used to update specifications to identify and prevent further assaults. © 2024 IEEE.

关键词： Containers

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Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

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computers, Materials & Continua 2022年第7期72卷 243-259页

作者： S.Karthik Robin Singh Bhadoria Jeong Gon Lee Arun Kumar Sivaraman Sovan Samanta A.Balasundaram Brijesh Kumar Chaurasia S.Ashokkumar Department of ECE College of Engineering and TechnologySRM Institute of Science and TechnologyVadapalaniChennai600026India Department of CSE Birla Institute of Applied Sciences(BIAS)BhimtalUttarakhand263136India Division of Applied Mathematics Wonkwang University460Iksan-DaeroIksan-SiJeonbuk54538Korea School of Computer Science and Engineering Vellore Institute of Technology(VIT)Chennai600127India Department of Mathematics Tamralipta MahavidyalayaWest Bengal721636India School of Computer Science and Engineering Center for Cyber Physical SystemsVellore Institute of Technology(VIT)Chennai600127India Indian Institute of Information Technology(IIIT) LucknowUttar Pradesh226002India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai602105India

Data is always a crucial issue of concern especially during its prediction and computation in digital *** paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data *** also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the *** paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and *** method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from *** proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown *** scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic *** also demonstrates the generative function forKalman-filer based prediction model and its *** paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration(CPE)for Python.

关键词： Bayesian learning model kalman filter machine learning data accuracy prediction

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Statistical Study of Thermal Analysis in Solar Air Heater System Using Inclined Discrete Rib 2

Statistical Study of Thermal Analysis in Solar Air Heater Sy...

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2nd IEEE International Conference on Emerging Trends in engineering and Medical Sciences, ICETEMS 2024

作者： Sathish, Thanikodi Saravanan, Rathinasamy Giri, Jayant Al-Qawasmi, Khaled Saveetha School of Engineering SIMATS Department of Mechanical Engineering Tamil Nadu India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Zarga University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah 21448 Saudi Arabia

ISBN: (纸本)9798331529352

Heat is transferred in a solar air heater (SAH) system between air and absorber portion. A solar collector that exhibits low air-to-absorbing plate transmission of heat will exhibit inadequate overall thermal performance. Employing artificial roughness that encourages heat transmission from the absorber plate might be an effective approach to improve it. This technique has been adopted by researchers progressively more in their work for an extensive amount of time, so further research into it is still possible. In an attempt to enhanced natural convection, artificial roughness has been added to the collector in this investigation. The most suitable improvements for collectors are presented here. Collectors can find models with an inclined, distinct rib structure. To investigate the thermal performance, turbulent airflow through the specifically roughened solar collector system has been generated using computational fluid dynamics (CFD) application in Fluent of ANSYS. A standard absorber plate collector employing CFD has been used to compare performance of the modified collectors. It has been established that enhancing the absorber plate collector's synthetic roughness characteristics enhanced an overall performance system of solar thermal collector. Eventually, the outcome of thermal performance in modified collector performed more effectively than the traditional collector. In the final analysis, the computer-simulated thermal efficiency became 2.27 when compared to an experimental investigation of 1000 W/m² heat flux. © 2024 IEEE.

关键词： Absorber Analysis Discrete rib Heat flow Performance Solar collector

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Impact of Silicon Dioxide (SiO₂) on the Properties of Kenaf and Jute Fiber Composites 2

Impact of Silicon Dioxide (SiO<sub>2</sub>) on the Propertie...

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2nd IEEE International Conference on Emerging Trends in engineering and Medical Sciences, ICETEMS 2024

作者： Sathish, Thanikodi Saravanan, Rathinasamy Giri, Jayant Al-Qawasmi, Khaled Saveetha School of Engineering SIMATS Department of Mechanical Engineering Tamil Nadu India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah 21448 Saudi Arabia

ISBN: (纸本)9798331529352

Natural based fiber products are increasing productively due to the current scenario for developing various products. Kenaf fiber and Jute fiber are used in some applications to make lower cost materials. In this study, hybrid composites kenaf and jute are incorporated with different combinations of silicon oxide nanoparticles to explore the mechanical and morphological properties. The composite is fabricated using the hand layup method and then the compression moulding method. Various weight fractions (0%, 2%, 4%, 6%) of SiO2 nanoparticles were added to the hybrid composite to find the performance of the composites. In mechanical properties, tensile strength and impact strength were calculated. In morphological properties, Fourier Transform Infrared Spectroscopy (FTIR) was calculated. The addition of SiO2 nanoparticles improved the tensile strength of the composites from the results of the tensile test. 52.4 MPa is the maximum tensile strength observed at 4% SiO2 nanoparticles added hybrid composite is a 30.4% increase compared to without SiO2 nanoparticle added hybrid composite. 4% SiO2 nanoparticle is 7.5 kJ/m2, which is a 50% improvement compared to 0% SiO2 and it is the maximum impact strength from the results. The best connections in the hybrid composite are 4% SiO2 nanoparticles which are spread evenly in it. It achieves the best connections between the composites to improve the strength by spreading evenly in all places to the hybrid composite. For good dispersion maintaining and maximizing valuable connections is 4% SiO2, which is the best nanoparticle content. The addition of SiO2 nanoparticles improves the strength and it is important for hybrid composites for lightweight and high-strength applications. © 2024 IEEE.

关键词： FTIR impact Jute fiber Kenaf fiber nanoparticle Silicon oxide tensile

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Image analysis and fine-tuned ResNet50 model for effective malware detection

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Procedia computer Science 2025年 260卷 1098-1106页

作者： Sanjeev Kumar Yudhishthira Sapru Jitendra Kumar Sugandha Sapru Cyber Security Technology Division Centre for Development of Advanced Computing Mohali India Indian Computer Emergency Response Team CERT-In Delhi India Department of Mathematics Bioinformatics and Computer Applications MANIT Bhopal India Department of Mathematics PGGC Panjab University Chandigarh India

With the rapid growth of connected assets to the Internet, malware attacks have increased exponentially, often causing substantial damage to these connected assets. Early detection of evolving malware attacks can avoid such damage to the Internet ecosystem. However, it requires an efficient and robust approach with little reliance on existing signature-based approaches, as these are no longer effective in detecting obfuscated and complex malware attacks. This study presents a new malware detection and classification approach using image analysis and a fine-tuned ResNet50 convolution neural network (CNN) model through transfer learning. The raw binary executable programs are transformed into grayscale images and resized to 224 × 224 × 1 image size to be fed to the deep CNN model for feature extraction. The pre-trained ResNet50 model is customized to extract the textural features by retaining the learned weight and bias from the source ImageNet data and making the last fully connected layer non-trainable. After that, a fully connected dense layer is added for dimension reduction of the feature map into N × 128, with N as the number of malware files. Two public datasets are used in experiments - MalImg and Microsoft BIG. This study uses comprehensive performance metrics to evaluate the model and obtained an accuracy of 98.50% for the MalImg dataset and 94.85% for the Microsoft BIG data set. The experiment results show that the proposed methodology can handle malware obfuscated by standard polymorphic techniques and is better than similar methods in the literature.

关键词： cybersecurity Convolution neural network ResNet Malware classification Transfer learning

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Federated learning with heterogeneous data and models based on global decision boundary distillation

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Journal of King Saud University computer and Information Sciences 2025年第5期37卷 1-13页

作者： Zhang, Kejun Wang, Jun Wang, Wenbin Zeng, Taiheng Li, Pengcheng Wang, Xunxi Zhang, Tingrui Department of Cyberspace Security Beijing Electronic Science and Technology Institute Beijing China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China School of Cyber Science and Technology University of Science and Technology of China Hefei China Faculty of Engineering The University of New South Wales Kensington Australia

Data heterogeneity and performance disparities among heterogeneous models are critical challenges in federated learning with heterogeneous data and models, which limit its practical applicability and degrade local model performance. To address these challenges, we propose Federated Learning with Heterogeneous Data and Models Based on Global Decision Boundary Distillation (Fed-GDBD). For data heterogeneity, Fed-GDBD employs local prototype clustering to effectively capture and condense private data distribution information, and incorporates irrelevant-class knowledge distillation during local supervised learning to explicitly model the posterior relationships among classes, thereby mitigating knowledge forgetting in local domains. To address model performance disparities, Fed-GDBD innovatively incorporates global decision boundary distillation. By maintaining an updated global decision boundary learner on the server, the approach optimizes local models from a global decision boundary perspective, reducing the impact of conflicting information. We provide a theoretical analysis of the convergence rate of Fed-GDBD under non-convex objectives. Extensive experiments on four datasets demonstrate that Fed-GDBD outperforms state-of-the-art federated learning methods across various statistical heterogeneity settings and better adaptability to challenging scenarios.

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AQROM:A quality of service aware routing optimization mechanism based on asynchronous advantage actor-critic in software-defined networks

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Digital Communications and Networks 2024年第5期10卷 1405-1414页

作者： Wei Zhou Xing Jiang Qingsong Luo Bingli Guo Xiang Sun Fengyuan Sun Lingyu Meng School of Information and Communication Guilin University of Electronic TechnologyGuilin541004China The 34th Research Institute of China Electronics Technology Group Corporation Guilin541004China Guangxi Key Laboratory of Optical Network and Optical Information Security Guilin541004China State Key Laboratory of Information Photonics and Optical Communications BeijingUniversity of Posts and TelecommunicationsBeijing100876China Department of Electrical and Computer Engineering University of New MexicoAlbuquerqueNM87131USA

In Software-Defined Networks(SDNs),determining how to efficiently achieve Quality of Service(QoS)-aware routing is challenging but critical for significantly improving the performance of a network,where the metrics of QoS can be defined as,for example,average latency,packet loss ratio,and *** SDN controller can use network statistics and a Deep Reinforcement Learning(DRL)method to resolve this *** this paper,we formulate dynamic routing in an SDN as a Markov decision process and propose a DRL algorithm called the Asynchronous Advantage Actor-Critic QoS-aware Routing Optimization Mechanism(AQROM)to determine routing strategies that balance the traffic loads in the *** can improve the QoS of the network and reduce the training time via dynamic routing strategy updates;that is,the reward function can be dynamically and promptly altered based on the optimization objective regardless of the network topology and traffic *** can be considered as one-step optimization and a black-box routing mechanism in high-dimensional input and output sets for both discrete and continuous states,and actions with respect to the operations in the *** simulations were conducted using OMNeT++and the results demonstrated that AQROM 1)achieved much faster and stable convergence than the Deep Deterministic Policy Gradient(DDPG)and Advantage Actor-Critic(A2C),2)incurred a lower packet loss ratio and latency than Open Shortest Path First(OSPF),DDPG,and A2C,and 3)resulted in higher and more stable throughput than OSPF,DDPG,and A2C.

关键词： Software-defined networks Asynchronous advantage actor-critic QoS-aware routing optimization mechanism

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Robust ANN Searching for Dynamic Vector Streams without Rebuilding Index

Robust ANN Searching for Dynamic Vector Streams without Rebu...

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computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Haowen Bian Lingwei Chao Jingxuan Su Wenhong Tian Ruini Xue School of Computer Science and Engineering (School of Cyber Security) University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

Graph-based approximate nearest neighbour (ANN) algorithms have dominated vector indexing recently. However, some of them do not support incremental vector addition, while most of them do not support in-place deletion. Therefore, additional rebuilding overhead is required to keep the index updated. We propose HSG (Hybrid Stride Graph) to enable the vector index to accept dynamic vector streams. Inspired by the K-ANN graph and NSG, HSG constructs the index with short and long edges simultaneously without any preprocessing or prior dataset knowledge. Finally, HSG is evaluated against three well-known datasets and the experimental results indicate that HSG can deliver acceptable QPS with an excellent recall guarantee.

关键词： Heuristic algorithms Big Data Approximation algorithms Vectors Indexes Artificial intelligence Streams Indexing

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An Optimized Chinese Filtering Model Using Value Scale Extended Text Vector

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computer Systems Science & engineering 2023年第11期47卷 1881-1899页

作者： Siyu Lu Ligao Cai Zhixin Liu Shan Liu Bo Yang Lirong Yin Mingzhe Liu Wenfeng Zheng School of Automation University of Electronic Science and Technology of ChinaChengdu610054China School of Life Science Shaoxing UniversityShaoxing312000China Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA70803USA College of Computer Science and Cyber Security Chengdu University of TechnologyChengdu610059China

With the development of Internet technology,the explosive growth of Internet information presentation has led to difficulty in filtering effective *** a model with high accuracy for text classification has become a critical problem to be solved by text filtering,especially for Chinese *** paper selected the manually calibrated Douban movie website comment data for ***,a text filtering model based on the BP neural network has been built;Second,based on the Term Frequency-Inverse Document Frequency(TF-IDF)vector space model and the doc2vec method,the text word frequency vector and the text semantic vector were obtained respectively,and the text word frequency vector was linearly reduced by the Principal Component Analysis(PCA)***,the text word frequency vector after dimensionality reduction and the text semantic vector were combined,add the text value degree,and the text synthesis vector was *** show that the model combined with text word frequency vector degree after dimensionality reduction,text semantic vector,and text value has reached the highest accuracy of 84.67%.

关键词： Chinese text filtering text vector word frequency vectors text semantic vectors value degree BP neural network TF-IDF doc2vec PCA

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UZNER: A Benchmark for Named Entity Recognition in Uzbek 12th

UZNER: A Benchmark for Named Entity Recognition in Uzbek

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12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Yusufu, Aizihaierjiang Jiang, Liu Ainiwaer, Abidan Teng, Chong Yusufu, Aizierguli Li, Fei Ji, Donghong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China School of Information Management Wuhan University Wuhan China School of Computer Science and Technology Xinjiang Normal University Urumqi China

ISBN: (纸本)9783031446924

Named entity recognition (NER) is a key task in natural language processing, and entity recognition can provide necessary semantic information for many downstream tasks. However, the performance of NER is often limited by the richness of language resources. For low-resource languages, NER usually performs poorly due to the lack of sufficient labeled data and pre-trained models. To address this issue, we manually constructed a large-scale, high-quality Uzbek NER corpus of Uzbek, and experimented with various NER methods. We improved state-of-the-art baseline models by introducing additional features and data translations. Data translation enables the model to learn richer syntactic structure and semantic information. Affix features provide knowledge at the morphological level and play an important role in identifying oversimplified low-frequency entity labels. Our data and models will be available to facilitate low-resource NER. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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