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Privacy Data Management Mechanism Based on Blockchain and Federated Learning

computers, Materials & Continua 2023年第1期74卷 37-53页

作者： Mingsen Mo Shan Ji Xiaowan Wang Ghulam Mohiuddin Yongjun Ren Engineering Research Center of Digital Forensics of Ministry of Education School of ComputerNanjing University of Information Science&TechnologyNanjing210044China Zhengde Polytechnic Nanjing211106China Xi’an University of Posts&Telecommunications Xi’an710061China Department of Cyber Security at VaporVM Abu Dhabi999041United Arab Emirates

Due to the extensive use of various intelligent terminals and the popularity of network social tools,a large amount of data in the field of medical *** to manage these massive data safely and reliably has become an important challenge for the medical network *** paper proposes a data management framework of medical network community based on Consortium Blockchain(CB)and Federated learning(FL),which realizes the data security sharing between medical institutions and research *** this framework,the data security sharing mechanism of medical network community based on smart contract and the data privacy protection mechanism based on FL and alliance chain are designed to ensure the security of data and the privacy of important data in medical network community,*** intelligent contract system based on Keyed-Homomorphic Public Key(KH-PKE)Encryption scheme is designed,so that medical data can be saved in the CB in the form of ciphertext,and the automatic sharing of data is *** knowledge mechanism is used to ensure the correctness of shared ***,the zero-knowledge mechanism introduces the dynamic group signature mechanism of chosen ciphertext attack(CCA)anonymity,which makes the scheme more efficient in computing and communication *** the end of this paper,the performance of the scheme is analyzed fromboth asymptotic and practical *** experimental comparative analysis,the scheme proposed in this paper is more effective and feasible.

关键词： Data management blockchain federated learning

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Guardian of the Ensembles: Introducing Pairwise Adversarially Robust Loss for Resisting Adversarial Attacks in DNN Ensembles

Guardian of the Ensembles: Introducing Pairwise Adversariall...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Shukla, Shubhi Dalui, Subhadeep Alam, Manaar Datta, Shubhajit Mondal, Arijit Mukhopadhyay, Debdeep Chakrabarti, Partha Pratim Centre for Computational and Data Sciences Iit Kharagpur India Iit Kharagpur Computer Science and Engineering Department India Center for Cyber Security New York University Abu Dhabi United Arab Emirates Iit Kharagpur Department of Artificial Intelligence India Iit Patna Computer Science and Engineering Department India

ISBN: (纸本)9798331510831

Adversarial attacks rely on transferability, where an adversarial example (AE) crafted on a surrogate classifier tends to mislead a target classifier. Recent ensemble methods demonstrate that AEs are less likely to mislead multiple classifiers in an ensemble. This paper proposes a new ensemble training using a Pairwise Adversarially Robust Loss (PARL) that by construction produces an ensemble of classifiers with diverse decision boundaries. PARL utilizes outputs and gradients of each layer with respect to network parameters in every classifier within the ensemble simultaneously. PARL is demonstrated to achieve higher robustness against black-box transfer attacks than previous ensemble methods as well as adversarial training without adversely affecting clean example accuracy. Extensive experiments using standard Resnet20, WideResnet28-10 classifiers demonstrate the robustness of PARL against state-of-the-art adversarial attacks. While maintaining similar clean accuracy and lesser training time, the proposed architecture has a 24.8% increase in robust accuracy (= 0.07) from the state-of-the art method. Code is available at: https://***/shubhishukla10/PARL © 2025 IEEE.

关键词： adversarial attack decision-boundary diversity defense ensemble models

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Secure speaker identification in open and closed environments modeled with symmetric comb filters

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Multimedia Tools and Applications 2025年第18期84卷 19147-19189页

作者： Shafik, Amira Monir, Mohamad El-Shafai, Walid Khalaf, Ashraf A. M. Nassar, M.M. El-Fishawy, Adel S. El-Din, M. A. Zein Dessouky, Moawad I. El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Laboratory Department of Computer Science Prince Sultan University Riyadh11586 Saudi Arabia Electrical Engineering Department Faculty of Engineering Minia University Minia61519 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Speech is a fundamental means of human interaction. Speaker Identification (SI) plays a crucial role in various applications, such as authentication systems, forensic investigation, and personal voice assistance. However, achieving robust and secure SI in both open and closed environments remains challenging. To address this issue, researchers have explored new techniques that enable computers to better understand and interact with humans. Smart systems leverage Artificial Neural Networks (ANNs) to mimic the human brain in identifying speakers. However, speech signals often suffer from interference, leading to signal degradation. The performance of a Speaker Identification System (SIS) is influenced by various environmental factors, such as noise and reverberation in open and closed environments, respectively. This research paper is concerned with the investigation of SI using Mel-Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients, with an ANN serving as the classifier. To tackle the challenges posed by environmental interference, we propose a novel approach that depends on symmetric comb filters for modeling. In closed environments, we study the effect of reverberation on speech signals, as it occurs due to multiple reflections. To address this issue, we model the reverberation effect with comb filters. We explore different domains, including time, Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST) domains for feature extraction to determine the best combination for SI in case of reverberation environments. Simulation results reveal that DWT outperforms other transforms, leading to a recognition rate of 93.75% at a Signal-to-Noise Ratio (SNR) of 15 dB. Additionally, we investigate the concept of cancelable SI to ensure user privacy, while maintaining high recognition rates. Our simulation results show a recognition rate of 97.5% at 0 dB using features extracted from speech signals and their DCTs. Fo

关键词： Speech enhancement

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A U-Shaped Multi-Scale Network for Defocus Blur Detection 17

A U-Shaped Multi-Scale Network for Defocus Blur Detection

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17th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2024

作者： Li, Longrui Huang, Liqing College of Computer and Cyber Security Fujian Normal University Fuzhou350117 China Digital Fujian Institute of Big Data Security Technology Fuzhou350117 China Fujian Provincial Engineering Research Center of Big Data Analysis and Application Fuzhou350117 China

ISBN: (纸本)9798331507398

Defocus blur detection (DBD) aims at accurately extract the in-focus sharp regions in natural images. Despite significant progress, the presence of uniform regions and gradient boundaries remain two of the most challenging problems. In this paper, we use a U-shaped multi-scale image input network for image DBD. The model is a U-shaped encoder-decoder structured network that incorporates inputs from multi-scale images to simultaneously extract features from images of different scales, and a feature fusion module (FFM) of the corresponding scales in the encoder part in order to better learn the features of these different scales. Experiments on two public datasets demonstrate the superior performance of the method in this paper in dealing with defocus blur detection. © 2024 IEEE.

关键词： Decoding

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Advance Public Bus Transport Management System: An Innovative Smart Bus Concept

Advance Public Bus Transport Management System: An Innovativ...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Patel, Keyurkumar Patel, Divya Chaudhary, Nishit Thakkar, Darsh Savaliya, Pratham Mistry, Armaan Rashtriya Raksha University Artificial Intelligence and Cyber Security Department of IT Gujarat Gandhinagar382305 India Karnavati University Unitedworld School of Computational Intelligence Department of Computer Science and Engineering Gujarat Gandhinagar382422 India

ISBN: (纸本)9798350324136

In the present era, public bus transportation services often lag behind in adopting modern technologies, resulting in numerous operational complexities. These issues encompass challenges such as poor passenger density control, operational inefficiencies, inadequate live tracking, limited real-Time capabilities, suboptimal resource management, route ambiguities, safety concerns, and more. This research paper introduces a comprehensive solution an Advance Public Bus Transport Management System (APBTMS) that aims to address these challenges for the benefit of both passengers and transport management authority. Our proposed system leverages various sensors and IoT (Internet of Things) modules deployed within buses to monitor various onboard activities and physical environmental factors. These enable the making of data-driven decisions in real-Time, Facilitating improved operational control. Key components of the system include GPS (Global Positioning System) tracking for real-Time bus location updates, passenger density/count through CV (computer Vision), and continuous monitoring of physical environmental factors such as temperature, humidity, sound magnitude, and vibration magnitude which facilitate accident/abnormalities detection, Fire detection and helps in continuously monitoring road quality. The application of CV technology, along with passenger count, can detect harm-causing objects inside a bus and can compare the faces of passengers with black-listed passengers (involved in previously reported crimes such as theft, and nuisance). Passengers benefit from these advancements through a dedicated mobile application. A User-friendly website is also offered to public bus transport management authority providing various useful functionalities. With the addition of safety features like real-Time speed detection and many other capabilities in the future, our proposed system can expand its benefits to school bus services and other public transportation services. © 2024

关键词： API (Application Programming Interface) AWS (Amazon Web Services) Cloud Computing CV (computer Vision) GPS (Global Positioning System) GSM (Global System for Mobile communication) IoT (Internet of Things) ML (Machine Learning)

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Multi-View Incongruity Learning for Multimodal Sarcasm Detection 31

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

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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

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Enhanced Phishing Detection: Integrating Random Forest Classifier and Domain Analysis for Proactive cybersecurity

Enhanced Phishing Detection: Integrating Random Forest Class...

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

作者： Dharini N E P Vishnu Sudarsan B.Praveen Thirugnanam D Sibi K Department of Computer Science and Engineering (Cyber Security) R.M.K. College of Engineering and Technology Puduvoyal Tamilnadu

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

This work provides a multimodal strategy for detecting phishing attack that combines URL structure analysis and machine learning methodologies. The process starts with Level 1 analysis, which includes parsing the URL components, accessing a list of known phishing sites, and evaluating domain-specific features to detect irregularities. Following Level 1 analysis, a decision is made whether to proceed to Level 2. Level 2 involves loading the URL into a machine learning model to detect specific patterns and features that signal phishing attempts. Our suggested technique combines Level 1 and Level 2 assessments to provide a complete assessment of the URL's legitimacy and possible phishing activities. Using the Random Forest Classifier, we achieved a maximum accuracy of 96% based on experimental verification which outperforms other popular classifiers including XGBoost, Decision Tree Classifiers, Logistic Regression, Bagging Classifier, AdaBoost Classifier, and Gradient Boosting Classifier. Performance parameters such as accuracy, recall, and F1-score were calculated and compared.

关键词： Uniform resource locators Logistic regression Accuracy Machine learning algorithms Phishing Feature extraction Real-time systems Decision trees Random forests Streams

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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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An IT/OT Converged cyber Range Framework Supporting Ensemble of Large Multimodal Models-Human Teaming and RAG Capability for Bigdata Information Retrieval - 6G Perspective

An IT/OT Converged Cyber Range Framework Supporting Ensemble...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Rahman, M. Abdur Islam, M. Saiful Islam, M. Repon Hossain, M. Shamim Al-Hazzaa, Selwa University of Prince Mugrin Cyber Security and Forensic Computing Department Medina Saudi Arabia UoL Worldwide University of London London United Kingdom Khulna University of Engineering & Technology Department of Computer Science and Engineering Khulna Bangladesh King Saud University Department of Software Engineering Riyadh Saudi Arabia Riyadh Saudi Arabia

ISBN: (纸本)9798350351255

In IT/OT converged enterprise networks, the management of multimodal big data has surpassed human capabilities, presenting critical challenges in business decision-making. This paper introduces an innovative IT/OT Converged cyber Range Framework, leveraging the power of 6G technology alongside an ensemble of large multimodal models (LMMs) equipped with Retrieval Augmented Generation (RAG) capabilities. Our proposed framework excels at parsing and retrieving information from diverse and massive multimodal datasets in near real-time, driven by the integration of LMMs and human interactions in natural language. The framework's advanced configuration with RAG achieved top performance with an average score of 80.5 across key metrics, including an 86 in answer relevancy and an 88 in faithfulness, while effectively reducing hallucinations to a score of 26. By combining 6G's connectivity with advanced LMM capabilities, we significantly improve the efficiency of automated big data processing and information retrieval, thereby transforming decision-making processes in IT/OT environments. The implementation of our framework, validated through real-life big data sources, and the encouraging test results showcase its effectiveness and transformative potential in managing and utilizing big data far beyond traditional human capabilities. © 2024 IEEE.

关键词： 6G technology Human-LMM Teaming IT/OT Converged Network Large Multimodal Models (LMMs) Multimedia Bigdata Retrieval Augmented Generation (RAG)

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Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

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computers, Materials & Continua 2023年第1期74卷 801-815页

作者： Yongjie Yang Shanshan Tu Raja Hashim Ali Hisham Alasmary Muhammad Waqas Muhammad Nouman Amjad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Information Security and Cybersecurity Unit King Khalid UniversityAbhaSaudi Arabia Computer Engineering Department College of Information TechnologyUniversity of Bahrain32038Bahrain School of Engineering Edith Cowan UniversityJoondalup PerthWA6027Australia School of Engineering University of Management and TechnologyLahorePakistan

With the recent developments in the Internet of Things(IoT),the amount of data collected has expanded tremendously,resulting in a higher demand for data storage,computational capacity,and real-time processing *** computing has traditionally played an important role in establishing ***,fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility,location awareness,heterogeneity,scalability,low latency,and geographic ***,IoT networks are vulnerable to unwanted assaults because of their open and shared *** a result,various fog computing-based security models that protect IoT networks have been developed.A distributed architecture based on an intrusion detection system(IDS)ensures that a dynamic,scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is *** this study,we examined the time-related aspects of network traffic *** presented an intrusion detection model based on a twolayered bidirectional long short-term memory(Bi-LSTM)with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark *** showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy,precision,recall and F1 score.

关键词： Fog computing intrusion detection bi-LSTM attention mechanism

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