检索结果-内蒙古大学图书馆

Green5G: Enhancing Capacity and Coverage in Device-to-Device Communication

computers, Materials & Continua 2021年第5期67卷 1933-1950页

作者： Abdul Rehman Javed Rabia Abid Bakhtawar Aslam Hafiza Ammara Khalid Mohammad Zubair Khan Omar H.Alhazmi Muhammad Rizwan Department of Cyber Security Air UniversityIslamabadPakistan Department of Computer Science Kinnaird College for WomenLahore54000Pakistan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinahSaudi Arabia

With the popularity of green computing and the huge usage of networks,there is an acute need for expansion of the 5G network.5G is used where energy efficiency is the highest priority,and it can play a pinnacle role in helping every industry to hit *** in the 5G network,conventional performance guides,such as network capacity and coverage are still major issues and need *** to Device communication(D2D)communication technology plays an important role to improve the capacity and coverage of 5G technology using different *** issue of energy utilization in the IoT based system is a significant exploration *** optimizationin D2D communication is an important *** need to resolve this issue for increasing system *** IoT speaks to the issue of lessening energy utilization of IoT gadgets which accomplishes a supportable climate for IoT *** this paper,we improve the capacity and coverage of 5G technology using Multiple Inputs Multiple Outputs(MU-MIMO).MUMIMO increases the capacity of 5G in D2D *** also present all the problems faced by 5G technology and proposed architecture to enhance system performance.

关键词： Green computing 5G energy efficiency resource optimization device to device communication multiple input multiple output

来源：评论

学校读者我要写书评

暂无评论

Enhancing IoT Data security: Integrating Elliptic Galois Cryptography with Matrix XOR Steganography and Adaptive Firefly Optimization 8

Enhancing IoT Data Security: Integrating Elliptic Galois Cry...

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Anuradha, K. Alam, Irfan Nair, Deveendra Menon Narayanan Kiran, Ajmeera Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu India Faculty of Information and Communication Technology Kampar Malaysia Telangana India Scai Vit Bhopal University Cyber Security and Digital Forensics Division Kothari Kalan Sehore India Mlr Institute of Technology Department of Computer Science and Engineering Hdyerabad India

ISBN: (纸本)9798350376425

Securing the uninterrupted data flow is crucial in the fast-advancing Internet of Things (IoT) market. This work investigates the incorporation of elliptic Galois cryptography and matrix XOR coding steganography to improve data security. The suggested solution employs the elliptic Galois cryptography protocol to encrypt sensitive data from diverse medical records. The private information is embedded within images through the matrix XOR encoding steganography algorithm. The Adaptive Firefly optimization approach is used to improve the efficiency of utilizing cover blocks for steganography. The effectiveness of the proposed technique is evaluated using various modern approaches. We analyze the efficiency and security of the proposed approach by comparing it with a previous hybrid method employing the New York State Department of Health dataset. The results unequivocally validate that our approach offers enhanced safety in Internet of Things (IoT) applications. © 2024 IEEE.

关键词： Steganography

来源：评论

学校读者我要写书评

暂无评论

Adversarial learning for Mirai botnet detection based on long short-term memory and XGBoost

引用

International Journal of Cognitive Computing in engineering 2024年第1期5卷 153-160页

作者： Vajrobol, Vajratiya Gupta, Brij B. Gaurav, Akshat Chuang, Huan-Ming Institute of Informatics and Communication University of Delhi India International Center for AI and Cyber Security Research and Innovations. Asia University Taiwan Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Ronin Institute MontclairNJ United States Department of Information Management National Yunlin University of Science and Technology Yunlin Taiwan Symbiosis International University Pune India Department of Electrical and Computer Engineering Lebanese American University Beirut1102 Lebanon Dehradun India

In today's world, where digital threats are on the rise, one particularly concerning threat is the Mirai botnet. This malware is designed to infect and command a collection of Internet of Things (IoT) devices. The use of Mirai attacks has intensified in recent times, thus threatening the smooth operation of numerous devices that are connected to a network. Such attacks carry adverse consequences that include interference with services or the leakage of confidential information. To fight this growing threat, smart and flexible detection techniques are required to counter the new methods cyber attackers use. The aim of this research is to develop a resilient defense against Mirai botnet attacks. The Long Short Term Memory term (LSTM) and XGBoost combined have the best performance of 97.7% accuracy score. With this combination, the aim is to strengthen our cyber defenses against sophisticated and dynamically operating Mirai botnets to further enhance the security of our digital world. © 2024

关键词： Botnet

来源：评论

学校读者我要写书评

暂无评论

GAN-Loc: Empowering Indoor Localization for Unknown Areas via Generative Fingerprint Map 21

GAN-Loc: Empowering Indoor Localization for Unknown Areas vi...

引用

21st Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2024

作者： Yoon, JinYi You, Yeawon Kang, Dayeon Kim, Jeewoon Lee, HyungJune Department of Computer Science Virginia Tech BlacksburgVA United States Department of Cyber Security Ewha Womans University Seoul Korea Republic of Manning College of Information & Computer Sciences University of Massachusetts AmherstMA United States Department of Computer Science University of California IrvineCA United States Department of Computer Science and Engineering Ewha Womans University Seoul Korea Republic of

ISBN: (纸本)9798331519186

As indoor localization becomes a necessity to provide intelligent location-based services for in-building users, fingerprint-based positioning has been widely adopted in numerous Wi-Fi-equipped devices. However, its reliance on extensive offline site surveys limits its application in unexplored environments without prior fingerprint sampling. To address the challenge, we propose GAN-Loc, a novel generative fingerprint-based framework. We extract the underlying correlation between location and radio signal features, empowering indoor localization in un/under-explored areas, including unknown data points, newly deployed APs, or unexplored sites. It involves: 1) decomposing into a signal feature map for each AP perspective;2) learning with a set of points and their associated signal strength data;3) generating and integrating synthetic radio fingerprints;and 4) employing them into some existing localization algorithms. We evaluated GAN-Loc with extensive real-world RSSI measurements in seven different indoor places. GAN-Loc achieves the localization accuracy of up to 1.21m, 1.29m, and 1.28m for Wi-Fi, Zigbee, and BLE, respectively, compared to the accuracy of 1.47m, 1.58m, and 1.22m using an ideal ground-truth map, which is unachievable without site survey in unknown sites. © 2024 IEEE.

关键词： Gallium nitride

来源：评论

学校读者我要写书评

暂无评论

GLIMMER: Incorporating Graph and Lexical Features in Unsupervised Multi-Document Summarization 27

GLIMMER: Incorporating Graph and Lexical Features in Unsuper...

引用

27th European Conference on Artificial Intelligence, ECAI 2024

作者： Liu, Ran Liu, Ming Yu, Min Jiang, Jianguo Li, Gang Zhang, Dan Li, Jingyuan Meng, Xiang Huang, Weiqing Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China School of Information Technology Deakin University Australia Department of Computer Science Beijing Technology and Business University China

ISBN: (纸本)9781643685489

Pre-trained language models are increasingly being used in multi-document summarization tasks. However, these models need large-scale corpora for pre-training and are domain-dependent. Other non-neural unsupervised summarization approaches mostly rely on key sentence extraction, which can lead to information loss. To address these challenges, we propose a lightweight yet effective unsupervised approach called GLIMMER: a Graph and LexIcal features based unsupervised Multi-docuMEnt summaRization approach. It first constructs a sentence graph from the source documents, then automatically identifies semantic clusters by mining low-level features from raw texts, thereby improving intra-cluster correlation and the fluency of generated sentences. Finally, it summarizes clusters into natural sentences. Experiments conducted on Multi-News, Multi-Xscience and DUC-2004 demonstrate that our approach outperforms existing unsupervised approaches. Furthermore, it surpasses state-of-the-art pre-trained multi-document summarization models (e.g. PEGASUS and PRIMERA) under zero-shot settings in terms of ROUGE scores. Additionally, human evaluations indicate that summaries generated by GLIMMER achieve high readability and informativeness scores. © 2024 The Authors.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Designing of Creator's Information Hub using Machine Learning 2

Designing of Creator's Information Hub using Machine Learnin...

引用

2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Gabhane, Diksha Gawande, Ujwalla Gaikwad, Sanjana Gajarlewar, Swati Bhawalkar, Rita Yeshwantrao Chavan College of Engineering Dept. of Computer Science & Design Nagpur India Yeshwantrao Chavan College of Engineering Dept. of Information Technology Nagpur India NCPharmacy Wanadongri Nagpur India G H Raisoni College of Engineering and Management Dept. of Cyber Security and Data Science Pune India

ISBN: (纸本)9798331528713

The proliferation of educational content on platforms like YouTube has transformed digital learning, yet it presents challenges in effective creation, dissemination, and impact assessment. This paper proposes a strategic initiative to collaborate with 6,000 active educational YouTubers to enhance global educational outreach. Central to this initiative is the compilation and analysis of a comprehensive dataset containing vital information about content creators and their audiences. Leveraging advanced machine learning algorithms, the analysis aims to identify collaboration opportunities, optimize engagement strategies, enhance content relevance and impact, facilitate knowledge sharing, and measure outcomes. The problem statement highlights fragmentation in educational content, lack of data-driven insights, and suboptimal collaboration opportunities. Addressing these challenges requires a structured approach to connect creators, leverage data analytics, and establish a collaborative ecosystem for enriching educational experiences worldwide. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Multi-View Incongruity Learning for Multimodal Sarcasm Detection 31

Multi-View Incongruity Learning for Multimodal Sarcasm Detec...

引用

31st International Conference on Computational Linguistics, COLING 2025

作者： Guo, Diandian Cao, Cong Yuan, Fangfang Liu, Yanbing Zeng, Guangjie Yu, Xiaoyan Peng, Hao Yu, Philip S. Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science University of Illinois Chicago United States

ISBN: (纸本)9798891761964

Multimodal sarcasm detection (MSD) is essential for various downstream tasks. Existing MSD methods tend to rely on spurious correlations. These methods often mistakenly prioritize non-essential features yet still make correct predictions, demonstrating poor generalizability beyond training environments. Regarding this phenomenon, this paper undertakes several initiatives. Firstly, we identify two primary causes that lead to the reliance of spurious correlations. Secondly, we address these challenges by proposing a novel method that integrate Multimodal Incongruities via Contrastive Learning (MICL) for multimodal sarcasm detection. Specifically, we first leverage incongruity to drive multi-view learning from three views: token-patch, entity-object, and sentiment. Then, we introduce extensive data augmentation to mitigate the biased learning of the textual modality. Additionally, we construct a test set, SPMSD, which consists potential spurious correlations to evaluate the the model's generalizability. Experimental results demonstrate the superiority of MICL on benchmark datasets, along with the analyses showcasing MICL's advancement in mitigating the effect of spurious correlation. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need 38

Unlearnable 3D Point Clouds: Class-wise Transformation Is Al...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Xianlong Li, Minghui Liu, Wei Zhang, Hangtao Hu, Shengshan Zhang, Yechao Zhou, Ziqi Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples;(ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, i.e., even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at https://***/CGCL-codes/UnlearnablePC. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Quantum Resistance Group Key Management for IoTs 2

Quantum Resistance Group Key Management for IoTs

引用

2nd International Conference on Emerging Trends in Electrical, Control, and Telecommunication engineering, ETECTE 2023

作者： Samiullah, Fouzia Gan, Ming-Lee Akleylek, Sedat Aun, Yichiet Universiti Tunku Abdul Rahman Faculty of Information and Communication Technology Kampar Perak31900 Malaysia Ondokuz Mayis University Cyber Security and Information Technologies Research and Development Centre and Department of Computer Engineering Samsun Turkey University of Tartu Chair of Security and Theoretical Computer Science Estonia

ISBN: (纸本)9798350305654

Group communication plays a vital role in our interconnected society, facilitating efficient and secure data exchange among multiple endpoints. The issue pertains to the intricate nature that emerges within the limited settings of the Internet of Things and sensor networks. GKM plays a crucial role in maintaining security in group communication schemes. Existing GKM schemes commonly rely on public-key cryptography, which may be susceptible to potential threats posed by quantum computers. This study investigated the usage of post-quantum GKM schemes in the realm of IoTs. To enhance communication security and efficiency in the era of quantum computing, we made significant progress in implementing a post-quantum GKM scheme. Specifically, we have chosen the SABER Key Encapsulation Mechanism, which is one of the four finalists in the National Institute for Standards and Technology (NIST) process. We also present preliminary simulation results for our scheme. © 2023 IEEE.

关键词： Public key cryptography

来源：评论

学校读者我要写书评

暂无评论

Real-Time Detection of AI-Generated Deepfake Audio: A Novel Approach

Real-Time Detection of AI-Generated Deepfake Audio: A Novel ...

引用

International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Prathamesh Chiddarwar Computer Science Engineering (Specialization in Cyber Security and Forensics) MIT World Peace University Pune India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

The rise of AI-generated deepfake audio presents a significant challenge in audio forensics, as traditional detection methods are inadequate against sophisticated manipulations. This study proposes a novel approach combining deep learning algorithms with signal processing techniques to detect deepfake audio in real-time. By leveraging voice pattern recognition and anomaly detection, the method achieves high accuracy and low latency. Experimental results show a significant improvement in detection performance, with the integrated system outperforming traditional methods. The proposed solution demonstrates potential for enhancing security and digital forensics by enabling the swift identification of deepfake audio.

关键词： Deep learning Deepfakes Accuracy Government Signal processing algorithms Signal processing Real-time systems Pattern recognition security Low latency communication

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：