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Efficient Publication of Distributed and Overlapping Graph Data Under Differential Privacy

Tsinghua science and Technology 2022年第2期27卷 235-243页

作者： Xu Zheng Lizong Zhang Kaiyang Li Xi Zeng the School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China China Electronics Technology Cyber Security Co.Ltd. Chengdu 610000China

Graph data publication has been considered as an important step for data analysis and *** data,which provide knowledge on interactions among entities,can be locally generated and held by distributed data *** data are usually sensitive and private,because they may be related to owners’personal activities and can be hijacked by adversaries to conduct inference *** solutions either consider private graph data as centralized contents or disregard the overlapping of graphs in distributed ***,this work proposes a novel framework for distributed graph *** this framework,differential privacy is applied to justify the safety of the published *** includes four phases,i.e.,graph combination,plan construction sharing,data perturbation,and graph *** published graph selection is guided by one data coordinator,and each graph is perturbed carefully with the Laplace *** problem of graph selection is formulated and proven to be ***,a heuristic algorithm is proposed for *** correctness of the combined graph and the differential privacy on all edges are *** study also discusses a scenario without a data coordinator and proposes some insights into graph publication.

关键词： graph data distributed data publication differential privacy

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The Role of AI in Vehicle-to-Infrastructure Communication for Tolling Systems

The Role of AI in Vehicle-to-Infrastructure Communication fo...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Mahalakshmi, S. Baskaran, Nithiya Partheeban, Pachaivanna Krishnara, Kowsalyadevi Arjun, S. Chennai Institute of Technology Department of Computer Science and Engineering Kundrathur Chennai India Chennai Institute of Technology Department Cyber Security Kundrathur Chennai India Chennai Institute of Technology Department Civil EngDepartment Cyber Security Kundrathur Chennai India Sona Institute of Technology Department of Cyber Security Salem India

ISBN: (纸本)9798331535476

The number of vehicles people use is substantial;thus, surveillance must be accurate. License plates are detected and recognized by detecting static and real-time images captured by the camera using OCR technology, computer vision, and AI in the tool booth and other important roads. This model can be used for many things, such as automated services, increased security, determining whether a car is registered, and identifying and validating vehicles connected to traffic violations. Real-time investigations are part of the automated recognition of license plates and their application to other traffic control and highway surveillance aspects. To detect plates from the images and in real-time video, Use Easy OCR with the license plates to read the plate number, utilize tensor flow object detection to detect and recognize, and save the detected license plate for further analysis and search. © 2024 IEEE.

关键词： Highway traffic control

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TU-IR Apple Image Dataset: Benchmarking, Challenges, and Asymmetric Characterization for Bruise Detection in Application of Automatic Harvesting

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第1期2卷 105-124页

作者： Das, Dipak Hrishi Roy, Sourav Dey Saha, Priya Bhowmik, Mrinal Kanti Government Degree College Department of Information Technology Santirbazar 799144 India Department of Computer Science and Engineering Suryamaninagar 799022 India Department of Cyber Security and Digital Forensics Agartala799001 India

With the blooming interest in computer vision-based technologies for future automation of food producers, there is a need for incorporating an automatic bruise detection module in robotic apple harvesting because of decreasing accessibility and growing labor costs. Although numerous studies have been published for automatic quality inspection of fruit and other agricultural products, there is a lack of publicly available image-based datasets for quality inspection/automatic detection of bruises. Toward the aim of developing a bruise detection system for apple harvesting, especially at night time, this article describes the designing issues (i.e., protocol) and creation of a new infrared imaging-based dataset titled 'TU-IR Apple Image Dataset,' which contains 1375 infrared images of apples defining four major categories of bruises (i.e., fresh, slight, moderate, and severe). Along with the infrared images, ground truths (in the form of binary masks) and measurements of suspicious bruised regions are defined. This study also investigates the efficiency of infrared imaging technology for automatic bruise detection in apples by performing an analysis of temperature-based, intensity-based, texture-based, shape-based, and deep convolutional neural network-based features. The classification performance was evaluated using eight different feature sets. Based on the experimental results, considering the most outer-performed classifier, deep convolutional neural networks as a fixed feature extraction method were found to provide the highest prediction performance for discriminating between fresh and three categories of bruises in apples with an average accuracy, specificity, and sensitivity of 93.87%, 80.57%, and 92.02%, respectively. © 2023 IEEE.

关键词： Feature extraction

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Implementing Three Factors Authentication System (3FA) to Maintain Employee Attendance Record to Enhance security 3

Implementing Three Factors Authentication System (3FA) to Ma...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Johnson, Plezzant Columbus Kant, Shri Kumar, Sandeep Sharda University Department of Computer Science Engineering India Sharda University Center for Cyber Security and Cryptology India

ISBN: (纸本)9798350338607

Today as we all know that maintaining of employee credential have been some major issues around the world. Finding it more difficult to keep track of employees. With this, the company, institution, or government entity does not know who has entered their work-spaces to steal important information from their systems or databases. This serves as a deep wound for any company that does not keep track of its employees. This has been a major issue within so many Africans country, most especially Liberia. where so many companies, institutions, and governmental entities losing huge amounts of data. The means by which they do not track employee records have been with the traditional sign-in method, which has to do with the pen and paper or book sign in and sign out method, which can easily be stolen or damaged. This is one of the methods that employees use to not sign in when they get to work in order to steal data without anyone knowing that they were at work that day. Coming up with a method that will enable strong security and secure maintenance of employees records with less consuming and a strong level of three-factor authentication (3FA) will enable all employees to sign in with their three-level credentials, which have to do with something you know (knowledge), something you have (possession), or something you are (Inherence).The trustworthiness of identification is increased because this data cannot be copied or hacked. Furthermore, Three (3) Factor Authentication attempts to demonstrate, from a broader perspective, why it is more important to review your security procedures in order to prevent unauthorized persons from gaining access to your systems and obtaining data and information that you have not provided them with. Our system is going to allow user signing in with their user credential on your system after entering your username and password which authenticate you into the system, you as an employee to be verified and attendance to be mark you will use your Fa

关键词： Face recognition

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Strategies for Building and Maintaining Secure Web Applications 5

Strategies for Building and Maintaining Secure Web Applicati...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Sathiyapriyan, C. Manikandan, B. Gokul, S. Satheesh, Rewin Nivetha, S. Leelavathy, S. Aarupadai Veedu Institute of Technology Cyber Security Department of Computer Science and Engineering Chennai603104 India Aarupadai Veedu Institute of Technology Department of Computer Science and Engineering Chennai603104 India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai600119 India

ISBN: (纸本)9798350395235

Ensuring the security of web applications is paramount in safeguarding sensitive data and maintaining user trust. This study presents a comprehensive overview of strategies for building and maintaining secure web applications, acknowledging their inherent limitations and outlining potential areas for future work. The strategies discussed encompass fundamental aspects of web application security, including input validation and sanitization, robust authentication and authorization mechanisms, secure communication through HTTPS, timely security patching, implementation of security headers, monitoring and logging, client-side security measures, and vigilance in handling third-party components.. This research employed algorithms include Routing Algorithms, Middleware Execution Order Algorithms, Eloquent ORM Algorithms, Encryption and Hashing Algorithms, Session Handling Algorithms, Queue Management Algorithms, and Dependency Injection Algorithms. © 2024 IEEE.

关键词： Sensitive data

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A Traceable Privacy-Preserving Transaction Protocol With Evolutionary Threshold Authentication

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IEEE Internet of Things Journal 2025年第12期12卷 20787-20799页

作者： Xie, Ninghai Li, Jiguo Lin, Chao Zhang, Yichen Shen, Jian Fujian Normal University College of Computer and Cyber Security Fuzhou350117 China School of Mathematics and Statistics Key Laboratory of Analytical Mathematics and Applications Ministry of Education Fujian Fuzhou350117 China Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou350117 China Guangzhou University Guangdong Key Laboratory of Blockchain Security Guangzhou510006 China Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou310018 China

Decentralized payment systems, such as Bitcoin, enable immutable and transparent payments in a distributed manner. To address the issue of user privacy leakage due to transaction transparency, some efforts have enhanced privacy protection in decentralized payment systems. However, the lack of regulatory functions in these systems allows malicious users to engage in illegal activities. To balance user privacy protection and the regulation of malicious users, some efforts have attempted to introduce decentralized agencies. However, they have not considered the issue of agency corruption. In this paper, we propose a new traceable privacy-preserving transaction protocol, which tracks the addresses and transaction amounts of anonymous parties through decentralized institutions. To address the problem of committee corruption, we present a traceable privacy-preserving protocol with evolving threshold authentication based on a distributed random beacon (TPETA-to-DRB), enabling committee member updates. Furthermore, we prove that the protocol is secure under the random oracle model and conduct a comprehensive performance evaluation. © 2014 IEEE.

关键词： Authentication Protocol

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A Review of Intrusion Detection for Internet of Things Using Machine Learning

A Review of Intrusion Detection for Internet of Things Using...

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2024 International Conference on cybernation and Computation, cyberCOM 2024

作者： Paul, Ekwueme Chika Amrita Sharda School of Engineering & Technology Sharda University Computer Science & Engineering UP Greater Noida India Center for Cyber Security and Cryptology Computer Science & Engineering Sharda School of Engineering & Technology Sharda University UP Greater Noida India

ISBN: (纸本)9798331542344

As Internet of Things (IoT) networks proliferate across consumer and industrial sectors, safeguarding these interconnected cyber-physical systems is imperative. IoT ecosystems present abundant targets for malicious intrusions including denial-of-service, man-in-the-middle, crypto and access attacks due to their scale and dynamic topology. Such intrusions can severely disrupt sensing, monitoring and control functions, enabling catastrophic cascading failures. Consequently, IoT platforms require intelligent intrusion detection system (IDS) to identify anomalous behaviors and known attack patterns. Conventional signature-based detection relying on expert rules proves inadequate against evolving attacks. In contrast, data-driven machine learning (ML) algorithms offer versatile capabilities for securing IoT by modeling normal vs anomalous traffic and information flows based on supervised, semi-supervised or unsupervised learning. Critical analysis reveals ML-driven intrusion detection frameworks achieve high accuracy in controlled settings. However, challenges related to computational burdens, adversarial attacks, concept drift and lack of model standardization need resolution before widespread field deployment. As IoT ecosystems continue proliferating amidst escalating threats, our systematic review helps identify limitations and promising directions for future research on ML-powered IDSs. This paper presents a comprehensive analysis of adopting ML techniques like neural networks, random forest, support vector machines, ensemble models and deep learning architectures to engineer anomaly detection and misuse detection modules tailored for IoT environments. © 2024 IEEE.

关键词： Network intrusion

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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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CNN Based Model for Underwater Image Classification and Enhancement

CNN Based Model for Underwater Image Classification and Enha...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Jamandlamudi, Sahit Isravel, Deva Priya Dhas, Julia Punitha Malar Karunya Institute Of Technology And Sciences Division Of Data Science And Cyber Security Coimbatore India Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Coimbatore India

ISBN: (纸本)9798350371567

The challenge of enhancing underwater images is difficult because of several issues, including color distortion, inadequate illumination, and dispersion from suspended particles in the water. Conventional picture enhancing methods frequently find it difficult to resolve these problems. Thanks to its ability to learn intricate representations and pick up on minute features, deep learning has become a potent tool for picture improvement tasks in recent years. In this work, we suggest a novel deep learning-based method for improving underwater images. To automatically learn and improve the visual quality of underwater scenes, we use a convolutional neural network architecture that is specifically made to handle underwater photos. A sizable dataset of underwater photos is used to train the network, with an emphasis on contrast improvement and color correction. We compare our method with many state-of-the-art techniques and undertake extensive tests on a benchmark dataset to evaluate our approach. The outcomes show that our strategy performs much better than current approaches both in terms of subjective visual quality and objective criteria. Our technique improves visibility, minimizes noise, and successfully restores colors in underwater photos. By presenting a deep learning-based method that enhances the visual quality of underwater photographs, this work advances the field of underwater image processing and aids in marine research, underwater exploration, and surveillance. © 2024 IEEE.

关键词： Convolutional neural networks

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Anomaly-Based Detection of Log4j Attack in SDN

Anomaly-Based Detection of Log4j Attack in SDN

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Deshmukh, Pruthviraj Nitin Harish, R. Sangeetha, V. Praveen, K. TIFAC-CORE in Cyber Security Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Department of Computer Science and Information Systems BITS Pilani India

ISBN: (纸本)9789819613472

The widely used Java library Log4j logging framework has lately come under attack from a major vulnerability known as CVE-2021–44228, also known as Log4Shell. Through the use of malicious log messages, the vulnerability enables attackers to compromise vulnerable systems by executing remote code. A key component of SDN systems is the RYU controller, an open-source software-defined networking (SDN) controller that provides a wide range of functionalities. It can communicate and interact with SDN-capable switches and other network elements. It is used in SDN installations for managing, controlling, and supporting network automation and orchestration. This paper proposes a new anomaly-based Log4j detection approach in SDN networks. We have used multiple machine-learning classifiers to analyze and detect anomalies in network traffic generated by Log4j attacks to get the best achievable accuracy for real-time model development. The conducted experiment results that the proposed approach can give high accuracy up to 98% in detecting Log4j attack in SDN environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： E-learning

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