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10th Symposium on Novel Optoelectronic Detection Technology and Applications

作者： Luo, Haibo Zhou, Rang Su, Yifei Huang, Jiajie Li, Wanpeng School of Computer Science and Cyber Security Chengdu University of Technology Chengdu China Department of Computing Science University of Aberdeen Aberdeen United Kingdom

ISBN: (数字)9781510688155

ISBN: (纸本)9781510688148

Free-rider attacks in federated learning enable attackers to disguise their participation in the training process, enabling them to obtain the final aggregated model without contributing any data or computational resources. These attacks pose a significant threat, particularly in sensitive applications where data is scarce and the model holds considerable commercial value. This paper provides both theoretical and experimental analysis of free-rider attacks on federated learning systems. We establish formal guarantees demonstrating that such attacks can successfully converge to the aggregated models generated by legitimate participants. While server-level defenses can detect some forms of these attacks, we investigate more sophisticated evasion techniques, such as random perturbations to free-rider parameters, that make detection more difficult. Through a series of experiments in both independent and identically distributed (IID) as well as non-IID settings, we demonstrate the efficacy of these advanced strategies. Finally, we propose recommendations to mitigate free-rider attacks in real-world federated learning applications, with a particular emphasis on protecting data integrity and ensuring the security of high-value models. © 2025 SPIE.

关键词： Federated learning

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RCDS:a right-confirmable data-sharing model based on symbol mapping coding and blockchain

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Frontiers of Information Technology & Electronic engineering 2023年第8期24卷 1194-1213页

作者： Liang WANG Shunjiu HUANG Lina ZUO Jun LI Wenyuan LIU School of Cyber Security and Computer Hebei UniversityBaoding071000China Xiong’an Intelligent City Innovation Federation Xiong’an071700China School of Information Science and Engineering Yanshan UniversityQinhuangdao066000China

The problem of data right confirmation is a long-term bottleneck in data *** methods for confirming data rights lack credibility owing to poor supervision,and work only with specific data types because of their technical *** emergence of blockchain is followed by some new data-sharing models that may provide improved data ***,few of these models perform well enough in confirming data rights because the data access could not be fully under the control of the blockchain *** view of this,we propose a right-confirmable data-sharing model named RCDS that features symbol mapping coding(SMC)and *** SMC,each party encodes its digital identity into the byte sequence of the shared data by generating a unique symbol mapping table,whereby declaration of data rights can be content-independent for any type and any volume of *** blockchain,all data-sharing participants jointly supervise the delivery and the access to shared data,so that granting of data rights can be openly *** evaluation results show that RCDS is effective and practical in data-sharing applications that are conscientious about data right confirmation.

关键词： Data right confirmation Symbol mapping coding Blockchain Data sharing Traitor tracing Access control

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Framework for Cloud-Based Data Management in Autonomous Vehicles

Framework for Cloud-Based Data Management in Autonomous Vehi...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Pallapothu, Harshith Gopi Krishna, G. Sunil, Digvijay Teja Yadav, S. Nagasundari, S. Pes University Dept. of Computer Science & Engineering Bengaluru India Research Center for Information Security Forensics and Cyber Resilience Pes University Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331529833

The increasing adoption of autonomous vehicles has driven the need for robust data management solutions that support real-time operations and ensure vehicle safety and efficiency. This work introduces a cloud-based framework for management of sensor data from autonomous vehicles, focusing on optimizing payload transmission rates. The framework leverages AWS IoT Core and AWS IoT Analytics to ensure efficient data flow from vehicle sensors to cloud storage. This framework demonstrates its potential for scalable deployment in real-world autonomous vehicle networks, contributing to the evolution of connected vehicle technologies and intelligent transport systems. This framework ensures reliable data transmission up to 250 payloads per second, beyond which data loss occurs. © 2025 IEEE.

关键词： Cloud platforms

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Fortifying Smart Grids: A Holistic Assessment Strategy against cyber Attacks and Physical Threats for Intelligent Electronic Devices

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computers, Materials & Continua 2024年第8期80卷 2579-2609页

作者： Yangrong Chen June Li Yu Xia Ruiwen Zhang Lingling Li Xiaoyu Li Lin Ge School of Computer Science Zhengzhou University of AeronauticsZhengzhou450046China Henan Xin’an Century Technology Co. Ltd.Henan Data Security Engineering Technology Research CenterZhengzhou450046China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationSchool of Cyber Science and EngineeringWuhan UniversityWuhan430072China

Intelligent electronic devices(IEDs)are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control *** the context of the heightened security challenges within smart grids,IEDs pose significant risks due to inherent hardware and software vulner-abilities,as well as the openness and vulnerability of communication *** grid security,distinct from traditional internet security,mainly relies on monitoring network security events at the platform layer,lacking an effective assessment mechanism for ***,we incorporate considerations for both cyber-attacks and physical faults,presenting security assessment indicators and methods specifically tailored for ***,we outline the security monitoring technology for IEDs,considering the necessary data sources for their security ***,we classify IEDs and establish a comprehensive security monitoring index system,incorporating factors such as running states,network traffic,and abnormal *** index system contains 18 indicators in 3 ***,we elucidate quantitative methods for various indicators and propose a hybrid security assessment method known as GRCW-hybrid,combining grey relational analysis(GRA),analytic hierarchy process(AHP),and entropy weight method(EWM).According to the proposed assessment method,the security risk level of IEDs can be graded into 6 levels,namely 0,1,2,3,4,and *** higher the level,the greater the security ***,we assess and simulate 15 scenarios in 3 categories,which are based on monitoring indicators and real-world situations encountered by *** results show that calculated security risk level based on the proposed assessment method are consistent with actual ***,the reasonableness and effectiveness of the proposed index system and assessment method are validated.

关键词： Smart grid intelligent electronic device security assessment abnormal behaviors network traffic running states

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Performance Analysis of Energy and Secure Efficient Routing for Wireless Sensor Networks

Performance Analysis of Energy and Secure Efficient Routing ...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Hussein, Zainab Mohammed Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Wireless Sensor Networks (WSNs) are essential for various applications, but their architecture makes them vulnerable to attacks. While traditional security methods like authentication and encryption offer some protection, they often fall short against specific node attacks. This paper introduces the Secure Energy Aware Routing (SEAR) strategy, which uses trust-based assessments to exclude malicious nodes while optimizing energy consumption. SEAR factors in node trust, hop time, and residual energy to ensure reliable data transmission through trustworthy nodes. MATLAB simulations on networks of varying sizes (100-500) compare SEAR with existing methods (EAD and EASER), demonstrating SEAR's superior performance. Results show significant improvements in packet delivery ratio (13-16%), network throughput (19-24 %), energy consumption (32-36%), packet loss (16-190/0), and end-to-end latency (21-26%), proving SEAR's effectiveness in enhancing WSN security, energy efficiency, and performance. © 2024 IEEE.

关键词： MATLAB

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Extraction of Actionable Threat Intelligence from Dark Web Data 5

Extraction of Actionable Threat Intelligence from Dark Web D...

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5th International Conference on Control, Communication and Computing, ICCC 2023

作者： Varghese, Varsha Mahalakshmi, S. Senthilkumar, K.B. ER&DCI Institute of Technology Cyber Forensics and Information Security Department of Computer Science & Engineering Thiruvananthapuram India Cyber Security Group Centre for Development of Advanced Computing Thiruvananthapuram India

ISBN: (纸本)9798350334128

Darknet has emerged as an excellent platform for cybercriminals to conduct various illicit activities such as operating a fully functional crypto-currency-based marketplace or maintaining a highly anonymous communication channel. However, it also acts as a source of cyber Threat Intelligence which is the information about the techniques, tactics, or motives of an emerging threat. The life cycle of a Darknet-based cyber Threat Intelligence solution mainly consists of the Collection, Processing, Analysis, and Production of Data from the Darknet. The Collection and Processing stages deal with accumulating information from various dark web pages and transforming the data into some format using a big data system. The Analysis part is integral to the Threat Intelligence schema since it constitutes the extraction of named entities like Organization, Offensive Activity, Tools, etc., and the determination of relationships between these entities. The production stage is like data mining where some knowledge is extracted that can be relevant to an organization under consideration. Such knowledge can mitigate risks and disrupt any targeted cyber-attack campaigns, making cyber Threat Intelligence actionable. In this research, an open-source-intelligence toolset is used to scan and collect data from the dark web forums through crawling and scrapping. The collected data is then ingested into a state-of-the-art NLP model that extracts actionable threat intelligence using Named Entity Recognition. The experimental results indicate that the model could identify HackerIds, tools, software, organizations, and other entities in the discussions of dark web forums with better efficiency and accuracy. This could be used to identify the source of a data leak, the release of new malware, evidence of a new exploit, and other offensive activities. © 2023 IEEE.

关键词： Extraction

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Internet of Things Intrusion Detection System Based on Convolutional Neural Network

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computers, Materials & Continua 2023年第4期75卷 2119-2135页

作者： Jie Yin Yuxuan Shi Wen Deng Chang Yin Tiannan Wang Yuchen Song Tianyao Li Yicheng Li Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjingChina Engineering Research Center of Electronic Data Forensics Analysis NanjingChina Key Laboratory of Digital Forensics Department of Public Security of Jiangsu ProvinceNanjingChina

In recent years, the Internet of Things (IoT) technology has developedby leaps and bounds. However, the large and heterogeneous networkstructure of IoT brings high management costs. In particular, the low costof IoT devices exposes them to more serious security concerns. First, aconvolutional neural network intrusion detection system for IoT devices isproposed. After cleaning and preprocessing the NSL-KDD dataset, this paperuses feature engineering methods to select appropriate features. Then, basedon the combination of DCNN and machine learning, this paper designs acloud-based loss function, which adopts a regularization method to preventoverfitting. The model consists of one input layer, two convolutional layers,two pooling layers and three fully connected layers and one output ***, a framework that can fully consider the user’s privacy protection isproposed. The framework can only exchange model parameters or intermediateresults without exchanging local individuals or sample data. This paperfurther builds a global model based on virtual fusion data, so as to achievea balance between data privacy protection and data sharing computing. Theperformance indicators such as accuracy, precision, recall, F1 score, and AUCof the model are verified by simulation. The results show that the model ishelpful in solving the problem that the IoT intrusion detection system cannotachieve high precision and low cost at the same time.

关键词： Internet of things intrusion detection system convolutional neural network federated learning

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Enhanced Deep Learning Approaches for Robust Darknet Traffic Classification 3

Enhanced Deep Learning Approaches for Robust Darknet Traffic...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Etyang, Felix Pavithran, Pramod Mwendwa, Gideon Mandela, Ngaira Hillary, Musiime School of Engineering Cochin University of Science & Technology Department of Computer Science Cochin India School of Cyber Security and Digital Forensics National Forensics Sciences University Gandhinagar India School of Cyber Security and Digital Forensics National Forensics Sciences University Jinja Uganda

ISBN: (纸本)9798331518592

The classification of darknet traffic remains a pivotal task in modern cybersecurity efforts, necessitating advanced analytical techniques to address malicious activities pre-emptively. This study introduces and compares the efficacy of several deep learning models for classifying darknet traffic, including a Feedforward Neural Network (FFNN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and a novel deep learning model designed to enhance classification performance. Utilizing the CIC-Darknet2020 dataset, we implement a comprehensive evaluation using accuracy, precision, recall, and F1 score metrics. The results reveal that the proposed model surpasses traditional architectures, significantly improving classification accuracy and reliability, thereby contributing valuable insights to cybersecurity. © 2024 IEEE.

关键词： Adversarial machine learning

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A Review on Crop Yield Prediction using Deep Learning 8

A Review on Crop Yield Prediction using Deep Learning

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Verma, Sheily Singh, Shubham CUC-Ulster University Department of Computer Science/ Cyber Security Doha Qatar Era Smith Technology Noida India

ISBN: (纸本)9798350386578

The integration of cutting-edge technology into agricultural practices has catalyzed a transformative shift in global agriculture. This research study focuses on the revolutionary impact of deep learning approaches on crop yield prediction, a critical aspect of sustainable agriculture amid increasing global food demands. Traditional methods for crop yield estimation are examined initially, revealing limitations in accuracy and adaptability to diverse agroclimatic conditions. Subsequently, the rise of deep learning is explored, highlighting its ability to autonomously extract complex patterns from vast agricultural datasets. This research study provides a comprehensive classification and analysis of deep learning architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and attention mechanisms, in the context of agricultural yield prediction. This study discusses about the incorporation of sensor-based information, satellite imagery, and climate data into deep learning models, emphasizing the synergy among the data sources to enhance the prediction accuracy. Additionally, this research study addresses the challenges related to data quality, model interpretability, and scalability, proposing solutions and future research directions. By illuminating the transformative role of deep learning in crop yield prediction, this research study equips researchers, practitioners, and policymakers with valuable insights to advance sustainable agriculture and ensure global food security amidst changing climatic patterns and demographic trends. © 2024 IEEE.

关键词： crop yield prediction Recurrent Neural Networks (RNNs) Root Mean Square Error (RMSE)

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Blockchain-Assisted Fine-Grained Deduplication and Integrity Auditing for Outsourced Large-Scale Data in Cloud Storage

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IEEE Internet of Things Journal 2025年第12期12卷 21662-21678页

作者： Liu, Bingyun Zhang, Xiaojun Yang, Xingchun Zhang, Yuan Xue, Jingting Zhou, Rang Southwest Petroleum University School of Computer Science and Software Engineering Engineering Research Center for Intelligent Oil and Gas Exploration Chengdu610500 China Sichuan Police College Intelligent Policing Key Laboratory of SichuanProvince Luzhou646000 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu610054 China Chengdu University of Technology School of Computer Science and Cyber Security Chengdu610059 China

Cloud computing has emerged as a promising mode for storaging vast quantities of big data, which is vulnerable to potential security threats, making it urgent to ensure data confidentiality and integrity auditing. In addition, as a large number of duplicate data files exist in cloud storage, data deduplication is significant to improve storage efficiency. In this paper, we propose a blockchain-assisted fine-grained deduplication and integrity auditing scheme for outsourced large-scale data in cloud storage, achieving internal deduplication and cross-user external deduplication for ciphertexts and authentication tags. By constructing sparse summation ciphertext tree, the scheme implements proofs of ownership protocol through vector commitment, and guarantees the retrieval of distributed deduplication data blocks by designing the reconstruction matrix and bloom filter. The scheme exploits blockchain and smart contracts to ensure transparent integrity auditing without a third-party auditor (TPA), thereby avoiding malicious auditing biases. security analysis and performance evaluation demonstrate the feasibility of the scheme for deploying in cloud storage systems. © 2014 IEEE.

关键词： Data integrity

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