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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Garg, Khushi Jattu, Mahee Hariharasitaraman, S. Subramanian, R. Raja Division of Cyber Security and Digital Forensics VIT Bhopal University Madhya Pradesh India School of Computing Science and Engineering Vit Bhopal University Madhya Pradesh India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu India

ISBN: (纸本)9798350361186

The emergence of Web 3.0 has paved the way for a new generation of the internet that enables users to view, publish, and own content independently. This innovation is being driven by a combination of blockchain, semantic communication, edge computing, and artificial intelligence, which together create value networks that facilitate participatory decision-making. Blockchain, in particular, offers security services by recording content in a decentralized and transparent manner. Additionally, it is compatible with decentralized wireless edge computing designs that can accurately express the intended meanings of contents without requiring a lot of resources. Building on this foundation, this research proposes a paradigm for unified blockchain-semantic ecosystems for Web 3.0 with wireless edge intelligence. Specifically, we present a semantic proof technique built on Oracle that enables Web 3.0's off-chain and on-chain interactions. To improve interaction efficiency and support Web 3.0, Oracle has developed an adaptable Deep Reinforcement Learning-based sharding technique. Our work also introduces a blockchain-based semantic exchange architecture that tokenizes semantic data into non-fungible tokens (NFT) for semantic exchange in this system. We employ Zero-Knowledge Proof to trade real semantic information without publicly posting it before accepting payment, in contrast to traditional NFT marketplaces. This makes it possible to trade fairly and privately. Overall, our proposed framework can precisely perform decentralized semantic sharing and information transfer, further achieving the advantages of semantic extraction and communication in Web 3.0. © 2024 IEEE.

关键词： Edge computing

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FTGraph: A Flexible Tree-Based Graph Store on Persistent Memory for Large-Scale Dynamic Graphs

FTGraph: A Flexible Tree-Based Graph Store on Persistent Mem...

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2024 IEEE International Conference on Cluster Computing, CLUSTER 2024

作者： Sun, Gan Zhou, Jiang Li, Bo Gu, Xiaoyan Wang, Weiping He, Shuibing Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Cyberspace Security Defense Beijing China College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798350358711

Traditional in-memory graph systems often suffer from scalability due to the limited capacity and volatility of DRAM. Emerging non-volatile memory (NVM) provides an opportunity to achieve highly scalable and high-performance graph stores for its large capacity and persistence characteristics. However, directly deploying current in-memory graph storage systems on NVM would cause significant inefficiencies in NVM access, as their graph organization designed for DRAM may incur higher write amplification, crash inconsistency and costly concurrency control overhead in NVM for write-intensive work-loads. In this paper, we propose FTGraph, a Flexible Tree-based Graph storage system, for both efficient dynamical graph updates and analysis. To achieve this goal, we introduce a novel degree-aware suffix bit tree to effectively manage vertices and edges of the graph, enabling adaptability to real-world power-law degree distributions while significantly reducing NVM writes. Based on it, we adopt two optimization methods, logical vertex ID translation and sequential storage, for vertices with very high degrees within the tree to enhance graph analysis operations. We further integrate 8B NVM atomic writes with optimistic version-based concurrency control through a dual bitmap design to ensure low-overhead, log-free crash consistency and reduce read-writer contention. Experimental results show that FTGraph achieves up to 21.2× higher update performance and up to 85.4 × higher analysis performance, compared with state-of-the-art dynamic graph systems implemented on NVMs. © 2024 IEEE.

关键词： Concurrency control

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Natural Language Interface for Data Visualization: Harnessing the Power of Large Language Models

Natural Language Interface for Data Visualization: Harnessin...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Buchepalli Praneeth Ashutosh Kumar Singh Jyothika K Raju Mohana B Suma Computer Science and Engineering RV College of Engineering Bengaluru India Computer Science and Engineering (Cyber Security) RV College of Engineering Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Democratizing data analysis is the need of the hour, empowering a wider range of users within corporations to engage with data and uncover valuable insights. Because of the inherent ambiguity of natural language and the frequent lack of quality and clarity in user queries, implementing functioning Natural Language Interfaces (NLIs) has proven to be a challenging task. These problems make it difficult for existing language models to understand user intent effectively. This study introduces a novel data visualization tool that leverages the power of Large Language Models (LLMs) to empower corporations and research institutions in extracting meaningful insights from vast datasets. LLMs interpret user input and generate goals, which LIDA, a library developed by Microsoft, uses to create a wide range of visualizations with libraries like matplotlib, seaborn, and plotly, based on these goals and user instructions. This system bridges the gap between Natural Language Interfaces(NLIs) and data visualization by enabling users to submit prompts in natural language. Users can formulate questions in natural language, eliminating the need for complex code or data manipulation expertise. This work compares and contrasts the performance of open source models Mistral 7B instruct-v3, LLaMa3 8B, and proprietary model, gpt 3.5-turbo across several case studies as the graphs generated are dependent completely on the interpretation power of LLMs.

关键词： Visualization Data analysis Large language models Computational modeling Natural languages Data visualization Libraries Robustness Data models Data mining

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EVADE: Targeted Adversarial False Data Injection Attacks for State Estimation in Smart Grid

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2025年第3期10卷 534-546页

作者： Tian, Jiwei Shen, Chao Wang, Buhong Ren, Chao Xia, Xiaofang Dong, Runze Cheng, Tianhao Xi’an Jiaotong University Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an710049 China Air Force Engineering University Air Traffic Control and Navigation College Xi’an710051 China Air Force Engineering University Information and Navigation College Xi’an710043 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore Xidian University School of Computer Science and Technology Xi’an710126 China

Although conventional false data injection attacks can circumvent the detection of bad data detection (BDD) in sustainable power grid cyber physical systems, they are easily detected by well-trained deep learning-based detectors. Still, state estimation models with deep leaning-based detectors are not secure due to the vulnerabilities and fragility of deep learning models. Using the related laws of conventional false data injection attacks and adversarial sample attacks, this paper proposes the targEted adVersarial fAlse Data injEction (EVADE) strategy to explore targeted adversarial false data injection attacks for state estimation in Smart Grid. The proposed EVADE attack strategy selects key state variables based on adversarial saliency maps to improve the attack efficiency and perturbs as few state variables as possible to reduce the attack cost. In this way, the EVADE attack strategy can bypass the detection of BDD and neural attack detection (NAD) methods (that is, maintaining deep stealthy) with a high success rate and achieve the attack target simultaneously. Experimental results demonstrate the effectiveness of the proposed strategy, posing serious and pressing concerns for sustainable cyber physical power system security. © 2016 IEEE.

关键词： cyber attacks

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Senspy: An Integrated Network Monitoring and Anomaly Detection System Using Machine Learning

Senspy: An Integrated Network Monitoring and Anomaly Detecti...

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Space, Aerospace and Defence Conference (SPACE), IEEE

作者： Bandaru Jnyanadeep Nikunj Mittal Srivishnu P N Varshith Y Mohana Minal Moharir Ashok Kumar A R Computer Science and Engineering (Cyber Security) RV College of Engineering® Bengaluru India Computer Science and Engineering RV College of Engineering® Bengaluru India

ISBN: (数字)9798350367386

ISBN: (纸本)9798350367393

In the evolving cybersecurity landscape, the surge in cyber threats necessitates innovative defence mechanisms. Proposed work explores the integration of ML methods into Intrusion Detection System (IDS) Senspy tool. Senspy, an adaptive IDS software incorporates ML techniques like k-Nearest Neighbors (KNN), Random Forest and Logistic Regression, to enhance its detection capabilities of cyber threats. It's beneficial for recognizing outliers or unusual patterns in network traffic. By employing ML techniques, Senspy excels in detecting and identifying various cyber-attacks, surpassing traditional IDS capabilities. The system demonstrates robust performance, particularly with Random Forest algorithm achieving over accuracy and an F1 score of 99.6%.

关键词： Logistic regression Machine learning algorithms Accuracy Telecommunication traffic Nearest neighbor methods Prediction algorithms Windows

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Real Time Smart Forest Fire Detection and Monitoring System Using IoT and Machine Learning

Real Time Smart Forest Fire Detection and Monitoring System ...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Khushal A Kishore A Tejas Neswi Mohana K Badarinath Computer Science and Engineering (Cyber Security) RV College of Engineering® Bengaluru India Computer Science and Engineering RV College of Engineering® Bengaluru India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

According to the National Interagency Fire Center (NIFC) forest fires pose a major threat to our planet, causing severe ecological, economic, and human loss, with an estimated 7 million acres of forest burned annually in the United States alone. To overcome this, in this paper proposed a cutting-edge solution that integrates Internet of Things (IoT), remote sensing, and machine learning technology. By deploying a network of sensors throughout a certain forest area to monitor temperature, humidity, and smoke levels, we can collect real-time data on forest conditions. This data is transmitted to a centralized system with an accuracy rate of 92% in predicting potential fire outbreaks. This approach provides early detection of forest fires, allowing for rapid response and mitigation efforts. Proposed system ensures swift communication between the monitoring station, authorities, and firefighting teams, reducing response times. Rigorous testing, including over multiple simulations, has validated the system's effectiveness, positioning it as a major tool in forest fire management and potentially reducing fire damage by 30%.

关键词： Temperature sensors Vibrations Wildlife Forestry Machine learning Real-time systems Sensor systems Internet of Things Time factors Monitoring

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A Novel Framework for Scalable Video Transmission Based on the Outage Probability Analysis of MIMO Coding 4

A Novel Framework for Scalable Video Transmission Based on t...

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4th Information Communication Technologies Conference, ICTC 2023

作者： Zhang, Yongjian Wang, Dongyu Zhang, Zhiqiang Wang, Shiguo University of International Relations School of Cyber Science and Engineering Beijing China North China University of Technology Department of Electronic Engineering Beijing China Shanxi Police College Department of Cyber Security Taiyuan China University of Science and Technology Computer and Communication Engineering Institute Changsha Changsha China

ISBN: (纸本)9781665462587

This work presents a radical approach for space-time codings, which could realize the goal of unequal error protection (UEP) transmitting for scalable video. This analysis leads us to develop a hybrid coding framework that combines vertical bell-labs layered space-time (V-BLAST) to ensure high-data-rate for the less crucial enhancement layer (EL) and Alamouti coding to ensure high reliability for the base layer (BL). According to those simulation findings, the suggested hybrid delivery method performs substantially better based on peak signal-to-noise ratio than either a pure special multiplexing or a pure spatial diversity delivery scheme. © 2023 IEEE.

关键词： MIMO systems

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VHASR: A Multimodal Speech Recognition System With Vision Hotwords

VHASR: A Multimodal Speech Recognition System With Vision Ho...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Hu, Jiliang Li, Zuchao Wang, Ping Ai, Haojun Zhang, Lefei Zhao, Hai Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China School of Information Management Wuhan University Wuhan China Department of Computer Science and Engineering Shanghai Jiao Tong University China

ISBN: (纸本)9798891761643

The image-based multimodal automatic speech recognition (ASR) model enhances speech recognition performance by incorporating audio-related image. However, some works suggest that introducing image information to model does not help improving ASR performance. In this paper, we propose a novel approach effectively utilizing audio-related image information and set up VHASR, a multimodal speech recognition system that uses vision as hotwords to strengthen the model's speech recognition capability. Our system utilizes a dual-stream architecture, which firstly transcribes the text on the two streams separately, and then combines the outputs. We evaluate the proposed model on four datasets: Flickr8k, ADE20k, COCO, and OpenImages. The experimental results show that VHASR can effectively utilize key information in images to enhance the model's speech recognition ability. Its performance not only surpasses unimodal ASR, but also achieves SOTA among existing image-based multimodal ASR. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Multiclass Brain Tumor Detection using Convolutional Neural Network and Conventional Machine Learning 2

Multiclass Brain Tumor Detection using Convolutional Neural ...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Vidhya, K. Vamsi Krishna, B. Haritha, M. Rajasekaran, Arun Sekar Roopa Chandrika, R. Velusamy, Jayaraj Karunya Institute of Technology and Sciences Division of Data Science and Cyber Security School of Computer Science and Technology Coimbatore India QIS College of Engineering and Technology Department of AIMLDS Andhra Pradesh India QIS College of Engineering and Technology Department of Mechanical Engineering Andhra Pradesh India SR University Department of Electronics and Communication Engineering Telangana India Karpagam Academy of Higher Education Department of Cyber Security Coimbatore India Nehru Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798331534950

Brain tumor (BT) affect the brain and reduce the lifespan of humans. Therefore, diagnosis and treatment of illnesses in its early stage is required. The objective of this paper is to evaluate the effectiveness of the Convolutional Neural Network (CNN) and Conventional Machine Learning (CML) strategies for identifying multiple classes of brain tumor. In the research, a comparison is made between these two systems for the picture classification of brain tumor. The first step is the collection and preprocessing of images of several classes of brain tumor. The CNN and CML models are trained using the preprocessed dataset. CML makes use of traditional machine learning (ML) algorithms for the purpose of feature extraction and classification, whereas CNN relies on convolutional layers to automatically extract significant features from the images it is fed as input. Precision, accuracy, F1 score, and recall are the criteria that are utilized to evaluate methods. In terms of accuracy and overall performance, CNN is superior to CML. CNN provides a more precise classification of a variety of tumor classifications. Based on these findings, convolutional neural networks (CNN) are likely superior than machine learning for the identification of multiclass brain tumor. CNNs are superior because of their ability to directly understand intricate patterns and characteristics from photographs. This work demonstrates the usefulness of CNNs for multiclass classification in brain tumor detection systems because of their high accuracy and efficiency. © 2024 IEEE.

关键词： Convolutional neural networks

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Helping Language Models Learn More: Multi-Dimensional Task Prompt for Few-shot Tuning

Helping Language Models Learn More: Multi-Dimensional Task P...

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2023 IEEE International Conference on Systems, Man, and cybernetics, SMC 2023

作者： Weng, Jinta Zhang, Jiarui Hu, Yue Fa, Daidong Xu, Xiaofeng Huang, Heyan School of Cyber Security University of Chinese Academy of Sciences China School of Computer and Information Qiannan Normal University for Nationalities Duyun China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China School of Computer Science and Technology Southeast Academy of Information Technology Beijing Institute of Technology China

ISBN: (纸本)9798350337020

Large language models (LLMs) can be used as accessible and intelligent chatbots by constructing natural language queries and directly inputting the prompt into the large language model. However, different prompt' constructions often lead to uncertainty in the answers and thus make it hard to utilize the specific knowledge of LLMs (like ChatGPT). To alleviate this, we use an interpretable structure to explain the prompt learning principle in LLMs, which certificates that the effectiveness of language models is determined by position changes of the task's related tokens. Therefore, we propose MTPrompt, a multi-dimensional task prompt learning method consisting based on task-related object, summary, and task description information. By automatically building and searching for appropriate prompts, our proposed MTPrompt achieves the best results on few-shot samples setting and five different datasets. In addition, we demonstrate the effectiveness and stability of our method in different experimental settings and ablation experiments. In interaction with large language models, embedding more task-related information into prompts will make it easier to stimulate knowledge embedded in large language models. © 2023 IEEE.

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