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

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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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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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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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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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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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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An Early Warning Model of Telecommunication Network Fraud Based on User Portrait

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computers, Materials & Continua 2023年第4期75卷 1561-1576页

作者： Wen Deng Guangjun Liang Chenfei Yu Kefan Yao Chengrui Wang Xuan Zhang Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjingChina Engineering Research Center of Electronic Data Forensics Analysis NanjingChina Department of Public Security of Jiangsu Province Key Laboratory of Digital ForensicsNanjingChina

With the frequent occurrence of telecommunications and networkfraud crimes in recent years, new frauds have emerged one after another whichhas caused huge losses to the people. However, due to the lack of an effectivepreventive mechanism, the police are often in a passive position. Usingtechnologies such as web crawlers, feature engineering, deep learning, andartificial intelligence, this paper proposes a user portrait fraudwarning schemebased on Weibo public data. First, we perform preliminary screening andcleaning based on the keyword “defrauded” to obtain valid fraudulent userIdentity Documents (IDs). The basic information and account information ofthese users is user-labeled to achieve the purpose of distinguishing the typesof fraud. Secondly, through feature engineering technologies such as avatarrecognition, Artificial Intelligence (AI) sentiment analysis, data screening,and follower blogger type analysis, these pictures and texts will be abstractedinto user preferences and personality characteristics which integrate multidimensionalinformation to build user portraits. Third, deep neural networktraining is performed on the cube. 80% percent of the data is predicted basedon the N-way K-shot problem and used to train the model, and the remaining20% is used for model accuracy evaluation. Experiments have shown thatFew-short learning has higher accuracy compared with Long Short TermMemory (LSTM), Recurrent Neural Networks (RNN) and ConvolutionalNeural Network (CNN). On this basis, this paper develops a WeChat smallprogram for early warning of telecommunications network fraud based onuser portraits. When the user enters some personal information on the frontend, the back-end database can perform correlation analysis by itself, so as tomatch the most likely fraud types and give relevant early warning *** fraud warning model is highly scaleable. The data of other Applications(APPs) can be extended to further improve the efficiency of anti-fraud whichhas e

关键词： Crawler user portrait feature engineering deep learning small program development

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Novel Architecture of security Orchestration, Automation and Response in Internet of Blended Environment

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computers, Materials & Continua 2022年第10期73卷 199-223页

作者： Minkyung Lee Julian Jang-Jaccard Jin Kwak ISAA Lab Department of Cyber SecurityAjou UniversitySuwon16499Korea Department of Computer Science and Information Technology Massey UniversityAuckland0745New Zealand Department of Cyber Security Ajou UniversitySuwon16499Korea

New technologies that take advantage of the emergence of massive Internet of Things(IoT)and a hyper-connected network environment have rapidly increased in recent *** technologies are used in diverse environments,such as smart factories,digital healthcare,and smart grids,with increased security *** intend to operate security Orchestration,Automation and Response(SOAR)in various environments through new concept definitions as the need to detect and respond automatically to rapidly increasing security incidents without the intervention of security personnel has *** facilitate the understanding of the security concern involved in this newly emerging area,we offer the definition of Internet of Blended Environment(IoBE)where various convergence environments are interconnected and the data analyzed in *** define Blended Threat(BT)as a security threat that exploits security vulnerabilities through various attack surfaces in the *** propose a novel SOAR-CUBE architecture to respond to security incidents with minimal human intervention by automating the BT response *** security Orchestration,Automation,and Response(SOAR)part of our architecture is used to link heterogeneous security technologies and the threat intelligence function that collects threat data and performs a correlation analysis of the *** is operated under Collaborative Units of Blended Environment(CUBE)which facilitates dynamic exchanges of data according to the environment applied to the IoBE by distributing and deploying security technologies for each BT type and dynamically combining them according to the cyber kill chain stage to minimize the damage and respond efficiently to BT.

关键词： Blended threat(BT) collaborative units for blended environment(CUBE) internet of blended environment(IoBE) security orchestration,automation and response(SOAR)

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