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Deceiving Deep Learning-based Fraud SMS Detection Models through Adversarial Attacks

Deceiving Deep Learning-based Fraud SMS Detection Models thr...

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international IEEE conference on signal-image Technologies and internet-based System

作者： Ashish Bajaj Dinesh Kumar Vishwakarma Department of Information Technology Biometric Research Laboratory Delhi Technological University Bawana Road Delhi India

Short Messaging Service (SMS) is one of the most extensively utilized mobile applications for communication around the world. Smishing is the technique of delivering harmful SMS to users in which intruders send malicious SMS to the victim. This content may contain links that direct the user to websites that contain harmful software and user interfaces. Researchers have acquired outstanding accuracy scores in proposing SMS spam detectors utilizing transformer-based deep learning algorithms. Despite their superior performance in Natural Language Processing-related tasks, deep learning models are vulnerable to adversarial attacks that result in misclassification. A few words or characters are altered to create adversarial text, fooling the machine into making inaccurate predictions. This research aims to analyze the security weaknesses of the smishing detection method by employing advanced attack techniques to generate adversarial text. In this study, we conducted a comparative analysis of various transformer models, including BERT, DistilBERT and RoBERTa. These models were trained on the SMS spam dataset, which is often used for detecting fraudulent SMS messages. Through our analysis, we examined and evaluated the behavior of these models to know which model is more vulnerable or which is more robust. The prospective outcomes have been assessed by the computation of Attack Success Rates (ASR) for each model. The findings indicate the feasibility of circumventing automated spam SMS detection systems, hence highlighting potential implications for existing regulatory interventions.

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Transformer Fault Diagnosis Method based on Gramian Angular Field and Graph Convolution Network 8

Transformer Fault Diagnosis Method Based on Gramian Angular ...

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8th international conference on Energy System, Electricity, and Power, ESEP 2023

作者： Zhang, Chenyu Yang, Zhen Fang, Zhenggang Li, Yigang Li, Xiang Zhang, Yulong China Electric Power Research Institute Beijing100192 China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

Power transformers, being pivotal in electrical power systems, necessitate vigilant monitoring for operational sustainability and reliability. The traditional approach to such monitoring, Dissolved Gas Analysis (DGA), has been grounded in gas volume fraction ratio analysis-a technique that, while effective, is constrained by static thresholds and simplistic assumptions that often do not encapsulate the intricacies of fault progression. In light of these limitations, the exploration of Artificial Intelligence (AI) methodologies presents a compelling alternative, offering a dynamic and data-driven avenue for fault detection and diagnosis. This paper proposes a transformer fault diagnosis method based on the Gramian Angular Field (GAF) transformation and Graph Convolutional Networks (GCN). Firstly, the one-dimensional DGA fault samples are converted into three-dimensional DGA feature image samples using the Gramian Angular Field transformation method. Then, a dataset expansion is performed on the image samples using a GAN-based data augmentation method to meet the data volume input requirements of the deep learning model. Subsequently, a graph convolutional network based on GCN layers and a Self-Attention Graph Pooling (SAGPool) mechanism is constructed. This network aggregates neighbor nodes according to the node features and topological structure of the three-dimensional DGA graph signal to adaptively mine fault features and diagnose fault types. The experimental results show that the method proposed in this paper can serve as an effective transformer fault diagnosis method. It is more accurate compared to the traditional gas ratio method for diagnosis, and its advantages are more significant compared to other traditional fault diagnosis deep learning methods. © 2024 SPIE.

关键词： Failure analysis

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Enhanced Data Hiding based on First, Middle, and Last Order Pixel in Spatial Domain images 10

Enhanced Data Hiding Based on First, Middle, and Last Order ...

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10th international conference on signal Processing and Communication, ICSC 2025

作者： Hidayat, Rahmad Chanda D'Layla, Adifa Widyadhani De La Croix, Ntivuguruzwa Jean Ahmad, Tohari Institut Teknologi Sepuluh Nopember Surabaya Department of Informatics Indonesia Stain Sultan Abdurrahman Kepulauan Riau Indonesia Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia University of Rwanda Kigali Rwanda Intitut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia

ISBN: (纸本)9798331511890

The rapid advancement of internet technology has revolutionized global information sharing, enabling faster and more efficient digital communication. However, the increased reliance on online platforms for exchanging sensitive information raises significant concerns about data security, as unauthorized access and misuse of transmitted data pose severe risks, including identity theft and financial fraud. Steganography has emerged as a promising technique for securing data by embedding it imperceptibly into digital media. This study introduces a novel steganographic method that utilizes the difference between pixels in the cover image to embed secret messages while maintaining high image quality. The proposed scheme divides image pixels into blocks of 9. It calculates the difference between pixels with each block's first, middle, or last pixel to ensure minimal distortion and superior visual quality post-embedding. Experimental results validate the effectiveness of the method, achieving consistently higher PSNR (Peak signal-to-Noise Ratio) values compared to a baseline approach, with a PSNR of 64.9205 dB for a 10 KB payload and similarly superior results for payloads of 20 KB and 40 KB. © 2025 IEEE.

关键词： Steganography

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2023 international conference on Information and Communication technology for Sustainable Development, ICICT4SD 2023 - Proceedings

2023 International Conference on Information and Communicati...

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2nd international conference on Information and Communication technology for Sustainable Development, ICICT4SD 2023

ISBN: (纸本)9798350358667

The proceedings contain 92 papers. The topics discussed include: an effective technique to automatically detect and neutralize rogue DHCP server;enhancing multi-class skin lesion classification with modified EfficientNets: advancing early detection of skin cancer;an investigation on rf propagation in the middle of body area networks in virtual reality;design a smart helmet as a quick responder safety solution for the bikers;securing vaccination data using self-sovereign identity, Hyperledger fabric and zero trust model;ensemble deep learning approach for ECG-based cardiac disease detection: signal and image analysis;advancing al-based assistive systems for visually impaired people: multi-class object detection and currency classification;a multilevel depression detection from twitter using fine-tuned RoBERTa;and comparative analysis of BanglaT5 and pointer generator network for Bengali abstractive story summarization.

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Design and Manufacturing of an Efficient Low-Cost Fire Surveillance System based on GSM Communications and IoT technology 3rd

Design and Manufacturing of an Efficient Low-Cost Fire Surve...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Marhoon, Hamzah M. Basil, Noorulden Elias, Bashar Bahaa Qas Department of Systems Engineering College of Information Engineering Al-Nahrain University Jadriya Baghdad Iraq Department of Electrical Engineering College of Engineering Mustansiriyah University Baghdad Iraq Department of Information and Communication Engineering College of Information Engineering Al-Nahrain University Jadriya Baghdad Iraq

ISBN: (纸本)9789819708918

In addition to recent technological advancements, a noticeable trend has emerged toward the development of smart cities and homes. By integrating intelligent home control components with existing household appliances, it aims to significantly reduce the use of human effort. This integration has led to decreased electricity consumption, enhanced home security against accidents and theft, and an overall improvement in residents’ comfort levels. This paper proposes the utilization of internet of Things (IoT) technology for the design of an intelligent home fire alarm system. The suggested system is comprised of an Arduino microcontroller, a NodeMCU microcontroller, a GSM shield, a flame sensor, a buzzer, and a servo motor. Given the limitations and relatively low accuracy of flame sensors, the LM35 temperature sensor is employed in conjunction with it. Upon detecting a flame and a subsequent temperature rise beyond 50 °C, the Arduino Uno will promptly dispatch an SMS warning to the homeowner. Simultaneously, a logical "1" signal will be transmitted to the NodeMCU microcontroller. This microcontroller will then utilize IoT technology to exhibit the warning message on the pre-programmed graphical user interface (GUI). To conclude the alert sequence, the Arduino Uno microcontroller will activate the servo motor. This activation serves the purpose of initiating the opening of the exit door and triggering the alarm. Rigorous practical testing has been conducted on the implemented system, yielding consistently positive outcomes and successful results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： internet of things

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Channel Estimation Algorithm for IM/DD-OFDM/OQAM-PON System in Industrial internet based on Compressed Sensing

Channel Estimation Algorithm for IM/DD-OFDM/OQAM-PON System ...

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IEEE international conference on Communications (ICC)

作者： Liang, Siyuan Wang, Chunting Cao, Haotong Feng, Jie Sun, Wenle Xian Univ Posts & Telecommun Xian Peoples R China Nanjing Univ Posts & Telecommun Jiangsu Key Lab Wireless Commun Nanjing Peoples R China

ISBN: (纸本)9781665426718

High-quality and low-latency communication in the three major application scenarios of 5G mobile communication technology is a guide for future technological development. The Industrial internet needs to deal with the data transmission tasks of large-traffic mobile bandwidth and ultra-low *** Frequency Division Multiplexing/Offset Quadrature Amplitude Modulation (OFDM/OQAM) passive optical network (PON) systems are affected by inherent imaginary interference (IMI), part of which is generated by the chromatic dispersion (CD) of optical fiber systems. Another part is produced by polarization mode dispersion (PMD). This paper proposes a channel estimation (CE) algorithm based on compressed sensing (CS), where the signal exhibits sparsity through sparse representation, and the signal reconstruction algorithm of compressed sensing adopts the orthogonal matching pursuit algorithm (OMP). The channel transfer function (TF) can effectively reduce the IMI and make the signal accuracy of the receiving end higher. Simulation results show that CS-CE algorithm can improve the system performance, which is better than the traditional LS method. Compared to existing LS methods, the CS algorithm can accomplish a 20% improvement. The algorithm can reduce the bit error rate of the system and improve the reliability of the system.

关键词： OFDM/OQAM Passive Optical network(PON) Channel estimation(CE) Compressed Sensing (CS)

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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2025 IEEE international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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3D Visual Cable Intelligent Control System based on Multidimensional Information Perception of internet of Things 2

3D Visual Cable Intelligent Control System Based on Multidim...

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2nd IEEE international conference on Artificial Intelligence of Things and Crowdsensing, AIoTCs 2022

作者： Zhao, Jiong Zhang, Junbing Li, Hao Lei, Guoqiang Zhao, Jun Taiyuan Power Supply Company of State Grid Shanxi Electric Power Company Shanxi Taiyuan China

ISBN: (纸本)9798350334104

With the development of society, cable plays an increasingly important role in people's daily life. Multidimensional information perception technology, as a new era information technology, has been applied to all walks of life. This paper aims at real-time monitoring of cables based on 3D visualization of the internet of Things. Firstly, the theoretical basis and idea of intelligent control are introduced. Secondly, the overall scheme design of the system, the selection of key hardware and the data acquisition and transmission modules are described. Finally, starting from the communication mode, a variety of signal fusion methods are proposed to better realize the comprehensive utilization of multi-source information. The feasibility and effectiveness of the proposed technology in practical application are verified through simulation. The verification results show that, The control time of the 3D visualization cable intelligent control system based on the multi-dimensional information perception of the internet of Things is within 6 seconds, the temperature of the intelligent control cable fluctuates around 25 °C, and the monitoring image can achieve high-definition image quality, which shows that the technology can meet the user's intelligent control needs. © 2022 IEEE.

关键词： Cables

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Real-Time Implementation of IoT-based Home Automation System on Arduino Platform

Real-Time Implementation of IoT-Based Home Automation System...

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international conference on VLSI, signal Processing, Power Electronics, IoT, Communication and Embedded systems, VSPICE 2023

作者： Kumar, Bittu Kumar, Brajesh Kumar, Dammala Shiva Dasari, Kiran Kumbhare, Vijay Rao Varma, Ashwini Kumar Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Hyderabad500075 India Department of Electronics and Communication Engineering Government Engineering College Bihar Jamui India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad500043 India Department of Mechatronics Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9789819746569

The future of home automatic systems is increasing exponentially for a variety of aspects that can minimize the physical effort of an individual. The internet of Things (IoT) is expanding drastically every day, starting with small devices and moving up to larger ones. The use of IoT-based devices can share the data and complete the assigned tasks while people are engaged in other activities. The primary goal of this paper is to develop an IoT-based smart house automation system that can enable users to access and manage devices and appliances from a distance using an Android-based smartphone app. Further, in order to be more precise, IoT devices can be used to create a low-cost, expandable, reliable, and flexible wireless smart home automation system. It enables users to control various devices from distant locations using an intuitive user interface and simple installation. This paper presents the creation of a framework for smart home automation. Additionally, it combines wireless networking for customers to use a smartphone from anywhere in their home to remotely operate a range of electrical appliances such as bulbs, tube lights, fans, etc. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smart homes

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ISAR IMAGING OF IFDS ECHO based ON GPU PARALLEL PROCESSING

ISAR IMAGING OF IFDS ECHO BASED ON GPU PARALLEL PROCESSING

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IET international Radar conference 2023, IRC 2023

作者： Lei, Junfeng Liu, Tao Tian, Biao School of Electronics and Communication Engineering Shenzhen Campus of Sun Yat-sen University Shenzhen China

ISBN: (纸本)9781839539954

The Intermediate Frequency Direct Sampling (IFDS) mode in Inverse Synthetic Aperture Radar (ISAR) imaging preserves the original information of the echo signal and radar system characteristics. However, the massive data accompanying this mode imposes considerable computational burdens on subsequent steps. As a result, traditional ISAR imaging algorithms and implementations face challenges in achieving fast, real-time imaging in IFDS mode. This paper presents the design and implementation of an IFDS echo ISAR imaging system based on parallel computing using Graphic Processing Unit (GPU). The efficiency and effectiveness of the proposed system were validated on the simulated data. Significant performance improvements compared to traditional CPU-bound ISAR imaging systems were demonstrated in the results. These improvements were achieved with minimal impact on the final quality of the two-dimensional ISAR image. © The Institution of Engineering & technology 2023.

关键词： Radar imaging

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