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检索条件"任意字段=17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025"

共 1312 条记录，以下是91-100 订阅

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Ranking-based Diversified Influence Maximization for Dynamic Social networks

Ranking-based Diversified Influence Maximization for Dynamic...

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international communication systems and networks and Workshops, comsnets

作者： Sunil Kumar Meena Shashank Sheshar Singh Kuldeep Singh Department of Computer Science University of Delhi New Delhi New Delhi India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Influence maximization (IM) is the task of selecting the most influential nodes in the network. IM achieves the goal of spreading information, influencing behaviour, or promoting sales of products. Existing studies in IM focus on maximizing the activated nodes in the static network and neglecting the communities. In this paper, we give a novel ranking-based algorithm that ranks the nodes based on their reachability and diversity. this paper proposes an objective function that considers both diversity and activated nodes to determine the diversity capability of the nodes. the proposed algorithm utilizes previous computation and integrates reachability centrality with the objective function to rank the nodes at a timestamp in a dynamic network. We execute the proposed and benchmark algorithms on five datasets. the experiments show that the suggested algorithm produces superior and competitive outcomes in comparison to the benchmark algorithms.

关键词： Social networking (online) communication systems Heuristic algorithms Benchmark testing Linear programming

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Towards Generating a Robust, Scalable and Dynamic Provenance Graph for Attack Investigation over Distributed Microservice Architecture

Towards Generating a Robust, Scalable and Dynamic Provenance...

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international communication systems and networks and Workshops, comsnets

作者： Utkalika Satpathy Harsh Borse Sandip Chakraborty Department of Computer Science and Engineering IIT Kharagpur India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

In recent years, detecting sophisticated attacks in distributed microservice environments has become increasingly challenging, mainly due to containerization, which adds another dimension of complexity for collecting the system logs and the lack of applications designed with known vulnerabilities for reproducibility and experimentation. this paper presents a framework called µProv for generating robust, scalable, and dynamic provenance graphs to aid in attack investigation over distributed microservice architectures. Our approach captures fine-grained, system-level interactions across microservices leveraging eBPF and constructs dynamic runtime provenance graphs representing the causal relationships between processes, files, and network activities. We integrate real-world attack scenarios with known vulnerabilities into our system to evaluate its effectiveness. through extensive empirical analysis, we demonstrate that µProv offers improved accuracy, scalability, and granularity compared to traditional logging methods.

关键词： Runtime Accuracy communication systems Scalability Memory management Microservice architectures Reproducibility of results Real-time systems Complexity theory

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mmWave Based User Identification using Gait Signatures

mmWave Based User Identification using Gait Signatures

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international communication systems and networks and Workshops, comsnets

作者： Deepraj Das Soumik Mandal Ashutosh Golande Argha Sen Sandip Chakraborty Department of Computer Science and Engineering Indian Institute of Technology Kharagpur

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

In this work, we present milliGait , a user identification using gait patterns captured by millimeter wave (mmWave) radar technology. milliGait takes into account the unique movement signatures of individuals to enable privacy secured accurate user identification. Using a Commercial-Off-the-self (COTS) mmWave radar we have collected mmWave features, particularly the range information and range-doppler profile features, extracted from the raw I/Q samples. To categorize people according to their leg movement patterns, we developed a Convolutional Neural Network (CNN) model using the mmWave features. the two features namely the range information and the range-doppler profile is fed into two concurrent CNN models (1D and 2D CNN models respectively). Finally a fully-connecyed classifier is employed to identify the subjects. We have evaluated milliGait with 3 different subjects with an authentication accuracy of over 80%.

关键词： Legged locomotion Privacy Accuracy communication systems Millimeter wave radar Feature extraction Sensors Convolutional neural networks Data mining Millimeter wave communication

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A Joint PSS Detection and Frequency Offset Estimation Algorithm for 5G NR communication

A Joint PSS Detection and Frequency Offset Estimation Algori...

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international communication systems and networks and Workshops, comsnets

作者： Srinivasan Kannan Sandeep Joshi Amrith Aathmaram Gayathri Srinidhi Rohit Pandey Department of Electrical & Electronics Engineering Birla Institute of Technology & Science Pilani Pilani 333031 India Hughes Systique and Bharti School of Telecommunication Technology & Management IIT Delhi Delhi India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

In this work, we propose a novel and robust algorithm for the combined detection of primary synchronization signal (PSS) and frequency offset correction for a fifth-generation (5G) new radio (NR) communication system. the proposed algorithm exploits the periodicity property inherent in the PSS of a 5G NR communication system and yields a low probability of false detection values in the presence of fading conditions and at low signal-to-noise ratio values such as -6 dB. the proposed method can be used to accurately detect the complete frequency offset compared to the typical methods that separately estimate integer and fractional frequency offsets. Further, through simulation, we demonstrate the performance and robustness of the proposed algorithm for 5G NR communication systems.

关键词： Fading channels communication systems 5G mobile communication Simulation Estimation Frequency estimation Robustness Synchronization Frequency synchronization Signal to noise ratio

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Learning-Based Sensing Mode Selection for ISAC in Intelligent Transportation systems

Learning-Based Sensing Mode Selection for ISAC in Intelligen...

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international communication systems and networks and Workshops, comsnets

作者： Roshan S. Sam Abhinaba Dey Sreenath Ramanath Lekha Wireless Solutions Bangalore India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Intelligent Transportation systems (ITS) encompass a broad range of technologies and strategies that aim to provide innovative services relating to different modes of transport and traffic management and enable users to be better informed and make safer, more coordinated, and smarter use of transport networks. One of the main pillars of 6G technology, Integrated Sensing and communication (ISAC), integrates communication and sensing capabilities into a single system. ISAC can significantly enhance ITS by improving the way vehicles and infrastructure interact, leading to more efficient, safe, and sustainable transportation networks. Moreover, Unmanned Aerial Vehicles (UAVs) can provide aerial data to complement ground-based sensors in ITS, leading to more comprehensive traffic analyses. the synergy between UAVs, ITS and ISAC has the potential to create smarter, safer, and more efficient transportation networks. In ISAC systems, various sensing modes can be utilized to enhance the performance of both sensing and communication functions. Each mode has its specific applications, advantages, and limitations. In this work, we propose a learning based sensing mode selection for ISAC in ITS. By applying learning-based approaches to sensing mode selection in ISAC, ITS can enhance the operational effectiveness across diverse environments.

关键词： 6G mobile communication Learning systems Surveillance Transportation Integrated sensing and communication Autonomous aerial vehicles Real-time systems 3GPP

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Identification of Phishing Mechanism of the Mastermind of Anomaly Community in the Social networks

Identification of Phishing Mechanism of the Mastermind of An...

引用

international communication systems and networks and Workshops, comsnets

作者： Sarjita Soo Nilanjana Saha Amrita Namtirtha Animesh Dutta NIT Durgapur CSE JIS Kalyani CSE

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Social media grows daily and raises malicious activity like spamming, phishing, cyberbullying, cyberstalking, etc. Anomaly community formation and phishing techniques are some of the most attractive among cyber fraudsters. Anomaly community and Phishing techniques are often led by a mastermind of that community. this anomaly community manipulates vulnerable individuals, using deceptive tactics on social platforms to lure them into harmful situations. this raises critical questions: How does the community, often operating anonymously, continue to expand its influence in social networks? To address these issues, the proposed algorithm, ACMP (Anomaly Community Detection and Phishing Mechanism), is designed to detect and mitigate these deceptive activities. It is designed using label propagation, eigenvector centrality, and phishing mechanisms. We have conducted two experiments on the Facebook dataset to evaluate the proposed method - the growth of phishing at each time step, and the growth of the mastermind’s connection strength. the experimental result reveals that the average percentage of growth improvement of phishing at each time-step is 11.35% from the ACMP.

关键词： Training Deep learning communication systems Phishing Unsolicited e-mail Cyberbullying Robustness Time complexity

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Leveraging Edge-cloud for Compute Efficient and Safer Autonomous Navigation

Leveraging Edge-cloud for Compute Efficient and Safer Autono...

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international communication systems and networks and Workshops, comsnets

作者： Prashanth P S Hrushikesh S Amrit Kumar Yuvraj Chowdary Makkena Praveen Tammana IIT Hyderabad

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Vision-based autonomous navigation in controlled environments like warehouses and agricultural fields, etc has allowed reducing the reliance on humans to do tedious jobs by employing autonomous vehicles. However, these vehicles heavily rely on computationally intensive ML tasks for image inference, significantly increasing CPU, GPU, and RAM utilization during onboard execution; thereby rapidly draining the battery. Additionally, updates/upgrades of onboard ML models with state-of-the-art models are limited and *** propose to keep the onboard device design simple by offloading compute-intensive ML tasks to the edge-cloud. By realizing this idea on the real testbed, we have observed that, there is 9% drop in CPU utilization, minimal GPU utilization, and 25% drop in RAM utilization. thus, the onboard battery’s power consumption is reduced by 37%, theoretically increasing the battery life by 200%.

关键词： Power demand communication systems Computational modeling Image edge detection Random access memory Graphics processing units Batteries Autonomous vehicles Autonomous robots

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Optimal Tree-Based Mechanisms for Differentially Private Approximate CDFs

Optimal Tree-Based Mechanisms for Differentially Private App...

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international communication systems and networks and Workshops, comsnets

作者： V. Arvind Rameshwar Anshoo Tandon Abhay Sharma IUDX Program Unit IISc Bengaluru India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

this paper considers the ε-differentially private (DP) release of an approximate cumulative distribution function (CDF) of the samples in a dataset. We assume that the true (approximate) CDF is obtained after lumping the data samples into a fixed number K of bins. In this work, we extend the well-known binary tree mechanism to the class of level-uniform tree-based mechanisms and identify ε-DP mechanisms that have a small ℓ 2 -error. We identify optimal or close-to-optimal tree structures when either of the parameters, which are the branching factors or the privacy budgets at each tree level, are given, and when the algorithm designer is free to choose both sets of parameters. Interestingly, when we allow the branching factors to take on real values, under certain mild restrictions, the optimal level-uniform tree-based mechanism is obtained by choosing equal branching factors independent of K, and equal privacy budgets at all levels.

关键词： Privacy Branching factor communication systems Binary trees Distribution functions

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XDATE: eXplainable Deep belief network-based Auto-encoder with exTended Garson Algorithm

XDATE: eXplainable Deep belief network-based Auto-encoder wi...

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international communication systems and networks and Workshops, comsnets

作者： Satyam Kumar Vadlamani Ravi IDRBT Hyderabad India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Deep neural networks (DNNs) have recently delivered impressive results across various tasks, yet understanding how they make decisions remains challenging. Feature importance has appeared as a key technique for interpreting both shallow and deep neural networks. In this paper, we introduce XDATE, a framework that extends the Garson algorithm to Deep Belief networks (DBNs) based on autoencoders (DBNA). Unlike traditional DNNs, DBNs offer a layered learning approach that captures hierarchical features more effectively, making them more suitable for complex tasks. XDATE allows us to assess feature importance across multiple hidden layers, providing a clearer understanding of the decision-making process. the XDATE framework demonstrates robustness by maintaining consistent feature importance metrics across different network depths in classification tasks. this stability, paired with efficient feature extraction, makes XDATE a valuable tool for interpreting DBNA. Our method effectiveness is validated on various classification problems and regression datasets, highlighting the importance of feature selection and the potential of XDATE in improving model interpretability in real-world application.

关键词： Measurement Deep learning communication systems Autoencoders Decision making Feature extraction Prediction algorithms Stability analysis Robustness Classification algorithms

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Quantum Convolutional Neural networks based Human Activity Recognition using CSI

Quantum Convolutional Neural Networks based Human Activity R...

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international communication systems and networks and Workshops, comsnets

作者： Syed Mohammed Danish Sudhir Kumar Department of Electrical Engineering Indian Institute of Technology Patna India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Human Activity Recognition (HAR) plays an important role in smart environments and Internet of things (IoT) applications, where accurate real-time monitoring can enhance automation and interaction. While Classical Convolutional Neural networks (CNNs) have been widely used for such tasks, they face limitations in scalability and feature extraction, especially when dealing with large datasets. To address these challenges, we propose a novel application of Quantum Convolutional Neural networks (QCNNs) specifically tailored for processing CSI data in HAR. Our method aims to combine the power of quantum computing with classical convolutional methods to extract complex patterns from CSI signals, thereby improving accuracy and maintaining computational efficiency. the challenge of high-dimensional CSI data is addressed by hybridizing classical and quantum layers in the architecture, which enables better feature extraction and learning capabilities. Results show that our QCNN achieves superior performance in comparison to traditional CNNs.

关键词： Quantum computing Accuracy Convolution Scalability Face recognition Feature extraction Real-time systems Human activity recognition Convolutional neural networks Internet of things

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