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检索条件"任意字段=14th ACM Conference on Computer and Communication Security"

共 1335 条记录，以下是21-30 订阅

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Building Secure and Decentralized Survey Methodology: A Comprehensive Solution with FIDO2 Authentication and Fluree 14

Building Secure and Decentralized Survey Methodology: A Comp...

引用

14th IEEE International conference on communication Systems and Network Technologies, CSNT 2025

作者： Balachandher, Jawahar Dongre, Chandana Bhattacharyya, Somanshu Hunashikatti, Rohit Sarasvathi, V. Department of Computer Science and Engineering PES University Karnataka Bangalore India

ISBN: (纸本)9798331531935

Traditional survey systems struggle with data immutability, privacy protection, and reliable storage. these limitations compromise transparency, security, and data integrity. this paper proposes a decentralized and secure survey platform to address these challenges. Fast Identity Online 2 (FIDO2) passwordless authentication strengthens user verification, backed by public key cryptography. Fluree, a permissioned blockchain based graph database, ensures decentralized storage and minimizes data tampering. Smart codes are designed based on user requirements, while integrated statistical analyses enhance data insights. this approach of integrating these technologies prioritizes transparency, security, and integrity in the data collection process while maintaining a seamless user experience. By fostering trust among stakeholders, it enables reliable, data driven decision-making. © 2025 IEEE.

关键词： Public key cryptography

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Decentralised Cloud Architecture: Blockchain Driven Integrity and Raptor Codes for Enhanced Data Transmission 14

Decentralised Cloud Architecture: Blockchain Driven Integrit...

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14th IEEE International conference on communication Systems and Network Technologies, CSNT 2025

作者： Preethi, M. Kale, Ayush Satheesh, Pooja Harshitha, R. Rohith Vaidya, K. Department of Computer Science PES University Bangalore India

ISBN: (纸本)9798331531935

Ensuring data integrity and security has become integral in the digital era. Blockchain, an immutable ledger can be leveraged to provide data integrity to the cloud system. Centralised cloud systems present challenges in data integrity, reliability and security due to single point of failure and control. the alternative decentralised approach distributes data across multiple nodes thus reducing reliance on one server. However, this can lead to issues like network congestion, difficulty verifying file integrity and origin of tampering. the proposed system uses a combination of decentralised cloud nodes, blockchain, and fountain codes to provide a robust, scalable and secure cloud solution. Blockchain is used to store hashes of the files and validate every operation ensuring transparency and integrity. To provide a robust data recovery and enhance reliability of communication amongst the nodes, fountain codes are utilised for data transmission. © 2025 IEEE.

关键词： Cloud computing architecture

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Intrusion Detection at the Edge Computing: A Deep Learning Approach Using the UNSW-NB15 Dataset 14

Intrusion Detection at the Edge Computing: A Deep Learning A...

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14th IEEE International conference on communication Systems and Network Technologies, CSNT 2025

作者： Kumar, Vipin Kumar, Vivek Bhadauria, Abhay Pratap Singh Dixit, Jay Kumar, Amit Haridwar India Department of Computer Engineering & Applications GLA University UP Mathura India

ISBN: (纸本)9798331531935

Edge computing has transformed technology by enabling seamless connections between IoT devices, but it also introduces significant security challenges. EC is crucial for providing minimal latency processing and reducing the load on centralized servers, ensuring timely and scalable intrusion detection for IoT environments. the findings underscore the potential of combining edge computing, IoT, and deep learning to address evolving security challenges in distributed systems. To mitigate these risks, efficient models for detecting malicious activities are crucial. this study investigates the application of edge computing, Internet of things (IoT), and deep learning to improve the performance of Intrusion Detection Systems (IDS). By leveraging the UNSW-NB15 dataset, the research assesses the capability of deep learning models to identify and mitigate cyber threats effectively. the results demonstrate better performance, showing their suitability for real-time security applications. these findings highlight the potential of intelligent intrusion detection systems powered by robust datasets and deep learning for addressing evolving security threats in edge computing environments. © 2025 IEEE.

关键词： Edge computing

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LIGHT-YOLOv11: An Efficient S mall Object Detection Model for UAV Images 14

LIGHT-YOLOv11: An Efficient S mall Object Detection Model fo...

引用

14th IEEE International conference on communication Systems and Network Technologies, CSNT 2025

作者： Dewangan, Bhimendra Srinivas, M. Computer Science and Information Security National Institute of Technology Warangal India Computer Science and Engineering National Institute of Technology Warangal India

ISBN: (纸本)9798331531935

Object recognition in UAV images is very challenging due to the frequent appearance of numerous small and truncated entities. Object detection is an essential component of computer vision for accurate object localization and identification in complex and dynamic aerial scenarios. We introduce LIGHT-YOLOv11, a modified object detection model that integrates architectural enhancements to address the specific challenges of aerial imagery. To improve feature extraction and emphasize essential regions, the network’s initial block has been restructured into a Focus Block. Additionally, a Transformer block has been integrated into the backbone, replacing two blocks in the final architecture, to optimize spatial and contextual understanding. With these adjustments, detection accuracy and processing efficiency are remarkably balanced, especially for small and closely spaced objects. In-depth analyses of the VisDrone2019-DET dataset show that LIGHT-YOLOv11 outperforms other popular techniques, producing impressive outcomes across a range of situations. © 2025 IEEE.

关键词： Target drones

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A Hardware Trojan Detection using Machine Learning Technique 14

A Hardware Trojan Detection using Machine Learning Technique

引用

14th IEEE International conference on communication Systems and Network Technologies, CSNT 2025

作者： Jaiswal, Yashasvi Sitaraman, S. Harihara VIT Bhopal University Bhopal India

ISBN: (纸本)9798331531935

Hardware Trojan is a major vulnerability in the security of the system. Various researchers have worked on the hardware Trojan detection and prevention techniques as solutions that are based on traditional methods such as side-channel analysis, logic testing, and IC fingerprinting. these methods focus on detecting anomalies in the system's behavior or physical characteristics. In this paper, we are giving insights of hardware trojan detection and prevention along with experimental study using several machine learning techniques. We used an open-source circuit dataset for the design phase known as Gate Level Netlist (GLN) that is available on the trust-hub data repository. In experiments we found that Hierarchical Machine learning Methods such as decision tree and random forest classified the hardware trojan infected and non-infected chips with 98-99% accuracy. © 2025 IEEE.

关键词： computer viruses

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Underpinning of Resource-Constrained IoT Networks: Analysis and Countermeasures for Attacks and Vulnerabilities 14

Underpinning of Resource-Constrained IoT Networks: Analysis ...

引用

14th IEEE International conference on communication Systems and Network Technologies, CSNT 2025

作者： Singh, Umeshwer Sood, Manu Bhadoria, Robin Singh Department of Computer Science Himachal Pradesh University Shimla India Department of Computer Science and Engineering National Institute of Technology Hamirpur Himachal Pradesh India

ISBN: (纸本)9798331531935

the Internet of things integrate various electronics devices on common platform as objects over the Internet. IoT devices have limited capabilities, such as low power, less memory, and low computational processing. Besides these constraints, IoT deals with a large amount of sensitive and personal data, making the IoT networks highly prone to attacks and various vulnerabilities. this paper summarizes the different possible attacks and vulnerabilities on resource-constrained IoT networks and the security requirements compromised. It also presents available countermeasures for these possible attacks. Furthermore, the paper discusses the simulation-based parameters for results on detection of such vulnerabilities and attacks in IoT networks. the paper presents protocols for resource-constrained IoT networks and maps different attacks with existing protocols. Finally, the paper summarizes the vulnerabilities in the IoT specially designed for resource-constrained environments and, discusses the potential impact of these vulnerabilities, and outlines countermeasures to address the same. © 2025 IEEE.

关键词： HTTP

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Deep Learning Techniques for Intrusion Detection Systems in Healthcare Environments 14

Deep Learning Techniques for Intrusion Detection Systems in ...

引用

14th IEEE International conference on communication Systems and Network Technologies, CSNT 2025

作者： Alam, Mahfooz Ashraf, Zubair Singh, Priya Pandey, Bishwajeet Rehman, Kaleemur Aldasheva, Laura Department of Master of Computer Applications G. L. Bajaj Institute of Technology and Management Greater Noida201306 India College of Computing and Information Technology University of Bisha Bisha67714 Saudi Arabia Department of Computer Science Institute of Engineering & Technology Dr. B. R. Ambedkar University Khandri Agra India Department of CSE-AI G. L. Bajaj Institute of Technology and Management Greater Noida201306 India Department of IS and CS Astana IT University Astana Kazakhstan

ISBN: (纸本)9798331531935

Nowadays, healthcare systems face ever-increasing cyber security threats due to the sensitive nature of patient data and the proliferation of IoT-enabled medical devices. Traditional Intrusion Detection Systems (IDS) struggle to adapt to healthcare environments' dynamic and heterogeneous data. Deep learning techniques, with their ability to analyze complex and high-dimensional data, have emerged as a promising approach to enhancing IDS performance. this paper explores the application of deep learning methods for intrusion detection in healthcare, focusing on their advantages, challenges, and practical implementations. Topics include neural network architectures, feature engineering for medical and network data, and real-time intrusion detection in resource-constrained environments. the goal is to provide a roadmap for leveraging deep learning to ensure robust and scalable security for healthcare systems. © 2025 IEEE.

关键词： Network intrusion

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Intelligent Anti-theft System with Automated Spray Control using ESP32 WROOM 14

Intelligent Anti-Theft System with Automated Spray Control u...

引用

14th IEEE International conference on communication Systems and Network Technologies, CSNT 2025

作者： Shanmugam, Geetha Balusamy, Deepa Elamparithi, S. Vetriselvan, R. Praamoth, Mourin S. Department of Computer Science and Engineering Nandha Engineering College Erode India

ISBN: (纸本)9798331531935

this project demonstrates a low-cost anti-theft solution built around the ESP32-WROOM microcontroller, with integrated chemical spray for security. the system is designed to detect unauthorized entry and respond with spraying disinfectant chemicals such as pepper spray, thus ensuring security as well as being effective against attackers. the ESP32-WROOM microcontroller is known for its Wi-Fi and Bluetooth capabilities, which plays a key role in providing real-time monitoring and control. Users can also access the system via mobile or web applications. In case of any abnormal situation, the user will be notified immediately. this hospitality provides a living environment for its users. therefore, they can move with peace of mind and even safeguard their valuable things from anywhere. this system also has a modular design that saves energy for easy installation and is suitable for residential, small business and commercial environments. © 2025 IEEE.

关键词： Disinfectants

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Proceedings of the acm workshop on survivable and self-regenerative systems (In association with 10th acm conference on computer communication security)

Proceedings of the ACM workshop on survivable and self-regen...

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Proceedings of the acm Workshop on Survivable and Self-Regenerative Systems (In Association with 10th acm conference on computer communications security)

ISBN: (纸本)1581137842

the proceedings contains 14 papers of the conference on Proceedings of the acm Workshop On Survivable and Self-Regenerative Systems (In Association With 10th acm conference On computer communication security). the topics include: a churn-resistant peer-to-peer web caching system;a holistic approach to service survivability;security analysis of SITAR intrusion tolerant system;continual repair for windows using the event log and self-regressive software components.

关键词： Internet

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Dynamic Forensic Analysis of CryptBot Malware 14

Dynamic Forensic Analysis of CryptBot Malware

引用

14th IEEE International conference on communication Systems and Network Technologies, CSNT 2025

作者： Rahman, Rizwan Ur Acharya, Abanteeka Deb, Sanjipan Panchal, Priyanshi Vellore Institute of Technology Bhopal Madhya Pradesh Bhopal India

ISBN: (纸本)9798331531935

CryptBot, an infostealer malware first identified in 2019, poses a serious threat by stealing sensitive data, including login credentials and cryptocurrency wallet details, and transmitting them to command-and-control servers. It spreads through phishing emails, malicious ads, and pirated software, using advanced evasion techniques to remain undetected. this study employs dynamic malware analysis to observe CryptBot’s real-time behavior, including its data exfiltration methods, persistence mechanisms, and evasion strategies. Key findings reveal tactics such as subprocess creation, process renaming, and temporary file usage to facilitate data theft. To mitigate such threats, a multi-layered security approach is essential, combining dynamic, static, and hybrid analysis methods with security measures like intrusion detection, encryption, and multi-factor authentication. Additionally, user education on phishing and software piracy is crucial in preventing infections. this research enhances understanding of CryptBot’s operations and proposes detection and mitigation strategies. Future work includes developing advanced sandbox environments and integrating machine learning to improve threat detection and response. © 2025 IEEE.

关键词： computer viruses

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