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Emerging Research in Computational Science (ICERCS), International Conference on

作者： S. Jothi Lakshmi S.P. Sasirekha N. Mekala R. Reshma V. Indu Nair S.G. Bhagya Department of Information Technology Nehru Institute of Technology Coimbatore India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India Department of Computer Science and Engineering(Cyber Security) Dhanalakshmi Srinivasan College of Engineering Coimbatore India Department of Artificial Intelligence and Data Science SNS College of Engineering Coimbatore India Department of Artificial Intelligence and Data Science Bapuji Institute of Engineering and Technology Davangere

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

This paper presents an object detection method using YOLO technique (You Only Look Once) based deep learning algorithm to help visually impaired people in their daily life. A Cobotic Spectacle is a cutting-edge product designed specifically for visually impaired individuals. The system seamlessly integrates face motion recognition, object identification and text detection functionalities using YOLO Algorithm. The real time object detection method uses YOLO and convolution neural networks (CNN) for single forward propagation from end to end. The proposed system utilizes YOLOv8, a state-of-the-art real-time object detection algorithm, which is trained on the Common Object in Context (COCO) dataset to accurately identify objects in front of a person. With a high degree of accuracy, the system then conveys the identified object information through a synthesized voice output, thereby enhancing visual perception and providing an immersive *** face motion recognition module promotes improved social interaction, while object identification increases environmental awareness. This integrated solution combines edge processing and cloud-based analysis for portability, real-time processing, and computational efficiency. A Cobotic Spectacle empowers visually impaired individuals, providing a state-of-the-art smart spectacle system that enhances visual perception, promotes independence, and improves their overall quality of life.

关键词： YOLO Text recognition Scientific computing Face recognition Neural networks Text detection Speech recognition Real-time systems Object recognition Visual perception

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Large System Analysis on Uplink Transmission of Backhaul-Aware UAV-Assisted Network 12th

Large System Analysis on Uplink Transmission of Backhaul-Awa...

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12th International Conference on computer engineering and Networks, CENet 2022

作者： Xue, Yishi Wu, Pingchuan Lan, Haoliang Hong, Lei Department of Computer Information and Cyber Security Jiangsu Police Institute Nanjing China Engineering Research Center of Electronic Data Forensics Analysis Jiangsu Nanjing China Key Laboratory of Digital Forensics Department of Public Security of Jiangsu Province Nanjing210031 China

ISBN: (纸本)9789811969003

With the development of wireless communication networks and basic technologies of unmanned aerial vehicles (UAVs), UAVs have gradually become a major research hotspot in the field of wireless communications. UAVs can assist networks to expand their coverage and to increase capacity, and becomes a promising solution for the next-generation networks. However, UAV-assisted networks also face many challenges, such as, limited spectrum resources and backhaul capacity. In this paper, the effect of spectrum resource allocation on the achievable rate of the system is studied in the uplink data collection scenario where a multi-antenna UAV assists base station (BS) to collect data of user equipments (UEs). This paper considers the UAV is connected to the BS through a wireless backhaul link, and the backhaul link shares spectrum resources with the access network. By optimizing the allocation factor of spectrum resources between the wireless backhaul link of the UAV and the access network, the uplink achievable rate maximization problem is firstly formulated. Then, the approximation problem of the original optimization problem is proposed by using the large-dimensional random matrix theory, and the optimal solution is proposed. Finally, the simulation results verify the accuracy of the theoretical derivation and the validity of the optimal solution of the approximation problem. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Resource allocation

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Enhancing Anomaly Detection in Smart Water Metering Networks with LSTM-Autoencoder and Data Augmentation Techniques

Enhancing Anomaly Detection in Smart Water Metering Networks...

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International Multidisciplinary Information technology and engineering Conference (IMITEC)

作者： Maria Nelago Kanyama Fungai Bhunu Shava Attlee M Gamundani Andreas Hartmann Department of Computer Science Namibia University of Science and Technology Windhoek Namibia Department of Cyber Security Namibia University of Science and Technology Windhoek Namibia Hydrology Institute of Groundwater Management Dresden Germany

ISBN: (数字)9798350387988

ISBN: (纸本)9798350387995

Machine Learning (ML) based anomaly detection in Smart Water Metering Networks (SWMNs) and traditional water metering networks faces significant challenges, including small dataset sizes, unlabeled data, and class imbalance. These issues often lead to high false detection rates, model overfitting, and increased model complexity. This paper aims to enhance anomaly detection in SWMNs using a ML-based Long Short-Term Memory Autoencoder (LSTM-AE) augmented with data augmentation techniques. The dataset, collected from Location A in Windhoek, Namibia, comprised of only 72 monthly water consumption data points per household, which is insufficient for effectively training ML models. To address this limitation, we employed two interpolation-based data augmentation techniques: Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) and Cubic Spline, to generate synthetic data instances to expand the dataset. These techniques were selected based on the patterns and trends of water consumption data and were compared for their accuracy. The original and augmented datasets were then used to train the LSTM-AE model. The results demonstrate that, despite the limited data available in the water sector, data augmentation techniques can significantly enrich datasets, leading to substantial performance improvement with augmented data. The proposed approach greatly enhanced the accuracy of the LSTM-AE model in reconstructing data from small datasets. This study underscores the potential of data augmentation to overcome data scarcity in the water sector and enhance anomaly detection in SWMNs.

关键词： Training Accuracy Autoencoders Data augmentation Data models Polynomials Splines (mathematics) Anomaly detection Synthetic data Overfitting

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SecSCS: A User-Centric Secure Smart Camera System Based on Blockchain

SecSCS: A User-Centric Secure Smart Camera System Based on B...

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International Conference on Distributed Computing Systems

作者： Xinyuan Qian Hongwei Li Haoyong Wang Guowen Xu Shengmin Xu Ju Ren School of Computer Science and Engineering University of Electronic Science and Technology of China Chongqing Key Laboratory of Automated Reasoning and Cognition Department of Computer Science City University of Hong Kong College of Computer and Cyber Security Fujian Normal University Department of Computer Science and Techology Tsinghua University

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Smart cameras have gained immense popularity in commercial markets for their safety and security capabilities. Yet, the prevalent design of these intelligent camera systems often compels users to cede control of their data to poten-tially untrusted service providers, such as cloud services. This relinquishment can lead to unauthorized data access by these intermediaries, posing significant security and privacy risks. The conventional solutions have been to employ privacy-enhancing technologies to bypass these intermediaries, but at the cost of increased overhead for video streaming and sharing. In our study, we introduce SecSCS, a user-centric, blockchain-based secure camera system that incorporates essential features like video streaming, sharing, deletion, and permission restoration. SecSCS integrates a blockchain-enabled user login protocol with a secure device pairing mechanism that combines visual authorization with blockchain to flexibly manage the device ownership. We utilize blockchain to provide integrity protection for the video clips stored remotely, ensuring the video data remains tamper-proof. Furthermore, we present a video frame compression and a fast video encryption method aimed at boosting the efficiency of smart camera systems. Our evaluations show that, in comparison to the leading decentralized scheme, CaCTUs, SecSCS improves the computational and communication overhead for live streaming by a factor of 12.58 and 11.29, respectively, at a frame rate of 24 fps and a resolution of 720p.

关键词： Authorization Visualization Data privacy Protocols technology Smart cameras Streaming media

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Enhancing Arabic Speaker Identification through Lip Movement Analysis and Deep Representation Learning 24

Enhancing Arabic Speaker Identification through Lip Movement...

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2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Ashour, Mohammed Imad Abbas, Abbas Saadi Ahmed, Saadaldeen Rashid Haydar, Noor Ghaffoori, Ali Jassim Hussain, Abadal-Salam T. Kurdi, Neesrin Ali Al-Sarem, Mohammed Tawfeq, Jamal Fadhil Department of Petroleum Engineering College of Engineering University of Baghdad Iraq Computer Science Department Bayan University Erbil Kurdistan Iraq Artificial Intelligence Engineering Department College of Engineering Al-Ayen University Thi-Qar Iraq Department of Cyber security Techniques Al Hikma University College Baghdad Iraq Electrical Power Techniques Al-Ma'moon University College Al-Washash Baghdad Iraq Department of Medical Instrumentation Techniques Engineering Technical Engineering College Al-Kitab University Altun Kupri Kirkuk Iraq College Of Computer Science and Engineering Taibah University Medina Saudi Arabia Department of Medical Instrumentation Technical Engineering Medical Technical College Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798400716928

Speaker identification plays a vital role in various real-life applications, including personal safety systems and human-computer interaction. This study presents a novel approach to Arabic speaker identification by integrating lip movement analysis with deep learning techniques. Using the "ArabCeleb"dataset, we conducted data preprocessing and employed a deep learning architecture to extract features from both audio and visual modalities. Our method achieved an impressive speaker identification accuracy of 98.6%. This research contributes to the advancement of speaker identification technology, particularly in Arabic, and provides valuable insights for multimodal speaker recognition. By incorporating lip movement analysis and deep representation learning, our approach enhances the accuracy and reliability of Arabic speaker identification systems. Further research in this direction holds great potential for improving speaker recognition in diverse linguistic contexts. © 2024 ACM.

关键词： Reliability analysis

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Moye: A Wallbreaker for Monolithic Firmware

Moye: A Wallbreaker for Monolithic Firmware

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International Conference on Software engineering (ICSE)

作者： Jintao Huang Kai Yang Gaosheng Wang Zhiqiang Shi Zhiwen Pan Shichao Lv Limin Sun Beijing Key Laboratory of IOT Information Security Technology Institute of Information Engineering CAS School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Computer Electronics and Information Guangxi University Nanning China

ISBN: (数字)9798331505691

ISBN: (纸本)9798331505707

As embedded devices become increasingly popular, monolithic firmware, known for its execution efficiency and simplicity, is widely used in resource-constrained devices. Different from ordinary firmware, the monolithic firmware image is packed without the file that indicates its format, which challenges the reverse engineering of monolithic firmware. Function identification is the prerequisite of monolithic firmware's analysis. Prior works on function identification are less effectiveness when applied to monolithic firmware due to their heavy reliance on file formats. In this paper, we propose Moye, a novel method to identify functions in monolithic firmware. We leverage the important insight that the use of registers must conform to some constraints. In particular, our approach segments the firmware, locate code sections and output the instructions. We use a masked language model to learn hiding relationships among the instructions to identify the function boundaries. We evaluate Moye using 1,318 monolithic firmware images, including 48 samples collected from widely used devices. The evaluation demonstrates that our approach significantly outperforms current works, achieving a precision greater than 98 % and a recall rate greater than 97 % across most datasets, showing robustness to complicated compilation options.

关键词： Image segmentation Codes Reverse engineering Robustness Registers Microprogramming Software engineering

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TMAS:A transaction misbehavior analysis scheme for blockchain

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Blockchain(Research and Applications) 2024年第3期5卷 42-52页

作者： Shiyong Huang Xiaohan Hao Yani Sun Chenhuang Wu Huimin Li Wei Ren Kim-Kwang Raymond Choo School of Computer Science China University of GeosciencesWuhan430074China Artificial Intelligence Thrust Information HubHong Kong University of Science and TechnologyGuangzhou511400China Yunnan Key Laboratory of Blockchain Application Technology Yunnan Innovation Institute of Beihang UniversityKunming650233China Fujian Key Laboratory of Financial Information Processing Putian UniversityPutian351100China Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX 78249-0631USA

Blockchain-based cryptocurrencies,such as Bitcoins,are increasingly ***,the decentralized and anonymous nature of these currencies can also be(ab)used for nefarious activities such as money laundering,thus reinforcing the importance of designing tools to effectively detect malicious transaction *** this paper,we propose TMAS,a transaction misbehavior analysis scheme for blockchain-based ***,the proposed system includes ten features in the transaction graph,two heuristic money laundering models,and an analysis method for account linkage,which identifies accounts that are distinct but controlled by an identical *** evaluate the effectiveness of our proposed indicators and models,we analyze 100 million transactions and compute transaction features,and are able to identify a number of suspicious ***,the proposed methods can be applied to other cryptocurrencies,such as token-based cryptocurrencies(e.g.,Bitcoins)and account-based cryptocurrencies(e.g.,Ethereum).

关键词： Blockchain Anti-money laundering Bitcoin Cyptocurrency

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An Efficient and Explainable Machine Learning Framework for Alzheimer's Disease Detection

An Efficient and Explainable Machine Learning Framework for ...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Mohammad Emad Arafah Rejwan Bin Sulaiman Mohammad Ahmad Md. Simul Hasan Talukder Department of Computer Science University of Petra Amman Jordan Lecturer of AI and Cyber Security Northumbria University Newcastle upon Tyne UK Lecturer of Computer Science University of Bedfordshire Luton UK Electrical and Electronic Engineering Dhaka University of Engineering & Technology Gazipur Bangladesh

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Alzheimer's disease detection is of paramount importance as it is a global health problem in terms of early detection, interventions, and management. The study applies prominent machine learning classification to Alzheimer's disease by integrating longitudinal and cross-sectional data analysis. With careful correlation analysis, we derived the following important predictive features: ‘M/F’, ‘Age’, ‘Education’, ‘SES’, ‘CDR’, ‘MMSE’, ‘eTIV’, ‘nWBV’, ‘ASF’, and ‘Hand’. In this study, the entire dataset was divide into training (80%) and testing (20%) sets, with eight machine learning models implemented on it for classification: CatBoost-CB, Logistic Regression-LR, Extreme Gradient Boosting-XGB, Support Vector Machine-SVM, Random Forest-RF, K-Nearest Neighbors-KNN, Decision Tree-DT, and Gradient Boosting-GB. Our results demonstrated superior performance from ensemble models, with RF, XGB, GB, DT, and CB achieving 98.1% accuracy, 98.2% precision, and 98.1% recall. These models showed exceptional performance with a Kappa score of 0.95, indicating strong reliability in classification. We have also used LIME explainability to enhance model transparency, providing insight into the contribution of different features in support of the interpretability of the model. In this study, we stress the power of machine learning for early detection of Alzheimer's disease, and provide future research directions on improving the predictive accuracy and transparency of predictive models.

关键词： Training Radio frequency Accuracy Feature extraction Vectors Reliability Alzheimer's disease Stress Regression tree analysis Testing

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Secure Cloud Computing: Mitigating Network Vulnerabilities

Secure Cloud Computing: Mitigating Network Vulnerabilities

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International Conference on Computing and Networking technology (ICCNT)

作者： Santhosh Kumar Gopal Abdul Sajid Mohammed Madhusudhan Dasari Sreeramulu Dhamodharan Srinivasan Technology Lead Charlotte NC USA Dept of School of Computer and Information Sciences University of the Cumberlands Kentucky USA Dept of Cyber Security American Express Department of Computer and Communication Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

As businesses increasingly depend on these cloud-based services, secure cloud computing is vital. There is no free way with the cloud network, and our information is susceptible to vulnerability. Mitigation strategies to address these network vulnerabilities in cloud computing and analysis of the man-in-the-middle attack and Distributed Denial of Service (DDoS) attack are discussed in this paper. For instance: enhance network architecture, encryption, and security mechanisms like intrusion detection systems and firewalls. With these steps, organizations can further increase their cloud network security and protect data with zero breaches.

关键词： Access control Cloud computing Protocols Prevention and mitigation Authentication Telecommunication traffic Software Encryption Web sites Protection

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An Efficient Post-quantum Identity-Based Signature

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Chinese Journal of Electronics 2021年第2期30卷 238-248页

作者： YANG Zhichao DUNG H.Duong WILLY Susilo YANG Guomin LI Chao CHEN Rongmao College of Liberal Arts and Sciences National University of Defence Technology Department of Information Security Naval University of Engineering School of Computing and Information Technology University of Wollongong College of Computer National University of Defence Technology

Digital signature is one of the most important cryptography primitives. Recently, more and more works have been done to construct signatures over lattice problems to keep them secure in the quantum age. Among them, a ring-based signature scheme named Dilithium is the most efficient one and a candidate in the third round of the National Institute of Standards and technology's post-quantum cryptography project. To make those schemes work well in large network, we constructed the first ring-based Identity-based signature(IBS) scheme for light-weight authentication. The construction in this paper relies on the transformations introduced by Bellare et al. in Journal of Cryptology(Vol.22, No.1, pp.1–61, 2009)and its security can be proved under the hardness of ringlearning with errors problem in the random oracle *** to better trapdoor and polynomial ring setting,our proposed scheme are much better than the previous ones in terms of both computation and communication complexities.

关键词： construct signatures important cryptography primitives ring-based Identity-based signature scheme communication complexity efficient post-quantum Identity-based signature polynomial ring setting ring-based signature scheme polynomials quantum age digital signatures quantum cryptography lattice problems digital signature public key cryptography technology's post ring-learning

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