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PrivBox: Privacy-Preserving Deep Packet Inspection with Dual Double-masking Obfuscated Rule Generation

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wu, Pengfei Ning, Jianting Huang, Xinyi Chen, Rongmao Zhang, Kai Liang, Kaitai Wuhan University School of Cyber Science and Engineering Wuhan China Singapore Management University School of Computing and Information Systems Singapore City University of Macau Faculty of Data Science Macau China Fujian Normal University College of Computer and Cyber Security Fuzhou China Jinan University College of Cyber Security Guangzhou China National University of Defense Technology College of Computer Changsha China Shanghai University of Electric Power College of Computer Science and Technology Shanghai China Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Netherlands

Many network middleboxes have been deployed to perform deep packet inspection (DPI) over packet payloads. However, such middleboxes cannot accomplish their tasks when the traffic is encrypted. BlindBox (SIGCOMM 2015) provided the first solution for performing DPI over encrypted traffic. To improve its efficiency, a later proposal PrivDPI (CCS 2019) introduced a practical technique to generate encrypted rules. However, a recent proposal P2DPI (ASIACCS 2021) showed that the rule generator in PrivDPI can comprise the user's privacy. In this paper, we present a new attack on P2DPI and show that the privacy of its endpoints can still be compromised by the rule generator. We comprehensively analyze the vulnerability of prior studies and present PrivBox, a new DPI system that achieves the same privacy guarantee as BlindBox while maintaining practical efficiency. This is based on a new technique called dual double-masking obfuscated rule generation. For a ruleset of 3,000, PrivBox achieves connection establishment time on the endpoint side comparable to PrivDPI and supports up to 4,672 token encryptions per second, which is sufficient for a number of real-world applications. Overall, our experiment demonstrates that PrivBox is practical and well-suited for short, frequently established sessions, especially when token repeating is common. ©2004-2012 IEEE.

关键词： Differential privacy

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Implementing DevOps Practices in CPPS Using Microservices and GitOps

Implementing DevOps Practices in CPPS Using Microservices an...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： István Koren Felix Rinker Kristof Meixner Moritz Kröger Michael Zeng Chair of Process and Data Science RWTH Aachen University Germany CDL SQI Institute of Information Systems Engineering Technische Universität Wien Austria Chair for Laser Technology RWTH Aachen University Germany Laboratory for Machine Tools and Production Engineering RWTH Aachen University Germany

As digitalization shapes Industry 4.0, integrating software engineering practices into the manufacturing domain becomes a key focus. DevOps is such a practice and methodology that enhances collaboration and automates processes in software development and IT operations. In previous work, we discussed challenges and opportunities of applying the DevOps paradigm in Cyber-Physical Production systems (CPPS) engineering, which are complex systems that integrate physical components with computer-controlled operations. This paper instantiates our DevOps4CPPS framework, spotlighting the application of GitOps and microservices in a CPPS environment. A real-world case study from laser ultra short pulse manufacturing illustrates how the approach effectively addresses the inherent challenges of CPPS, boosting agility and operational performance, while reducing downtime. The paper provides guidance to researchers and practitioners on applying DevOps practices to CPPS, and suggests future research directions.

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IoT for Water Quality Categorization

IoT for Water Quality Categorization

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Hermantoro Sastrohartono Andreas Wahyu Krisdiarto Arief Ika Uktoro Reza Rahutomo Teddy Suparyanto Bens Pardamean Agricultural Engineering Department Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results.

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Towards Fast Personalized Semi-Supervised Federated Learning in Edge Networks: Algorithm Design and Theoretical Guarantee

arXiv

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arXiv 2023年

作者： Wang, Shuai Xu, Yanqing Yuan, Yanli Quek, Tony Q.S. The Information Systems Technology and Design Singapore University of Technology and Design 487372 Singapore The Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of The Shenzhen Research Institute of Big Data School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China

Recent years have witnessed a huge demand for artificial intelligence and machine learning applications in wireless edge networks to assist individuals with real-time services. Owing to the practical setting and privacy preservation of federated learning (FL), it is a suitable and appealing distributed learning paradigm to deploy these applications at the network edge. Despite the many successful efforts made to apply FL to wireless edge networks, the adopted algorithms mostly follow the same spirit as FedAvg, thereby heavily suffering from the practical challenges of label deficiency and device heterogeneity. These challenges not only decelerate the model training in FL but also downgrade the application performance. In this paper, we focus on the algorithm design and address these challenges by investigating the personalized semi-supervised FL problem and proposing an effective algorithm, namely FedCPSL. In particular, the techniques of pseudo-labeling, and interpolation-based model personalization are judiciously combined to provide a new problem formulation for personalized semi-supervised FL. The proposed FedCPSL algorithm adopts novel strategies, including adaptive client variance reduction, local momentum, and normalized global aggregation, to combat the challenge of device heterogeneity and boost algorithm convergence. Moreover, the convergence property of FedCPSL is thoroughly analyzed and shows that FedCPSL is resilient to both statistical and system heterogeneity, obtaining a sublinear convergence rate. Experimental results on image classification tasks are also presented to demonstrate that the proposed approach outperforms its counterparts in terms of both convergence speed and application performance. Copyright © 2023, The Authors. All rights reserved.

关键词： information services

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Fractional discrete neural networks with variable order: solvability, finite time stability and synchronization

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The European Physical Journal Special Topics 2024年 1-14页

作者： Hioual, Amel Alomari, Saleh Al-Tarawneh, Hassan Ouannas, Adel Grassi, Giuseppe Laboratory of Dynamical Systems and Control University of Oum El-Bouaghi Oum El Bouaghi Algeria Faculty of Science and Information Technology Software Engineering Jadara University Irbid Jordan Department of Data Sciences and Artificial Intelligence Al-Ahliyya Amman University Amman Jordan Department of Mathematics and Computer Science University of Oum EL-Bouaghi Oum El Bouaghi Algeria Dipartimento Ingegneria Innovazione Universita del Salento Lecce Italy

Research in the field of dynamic behaviors in neural networks with variable-order differences is currently a thriving area, marked by various significant discoveries. However, when it comes to discrete-time neural networks featuring fractional variable-order nonlocal and nonsingular kernels, there has been limited exploration. This paper stands as one of the initial contributions to this subject, focusing primarily on the topics of stability and synchronization in finite-time within discrete neural networks. The research employs the nabla ABC variable-order difference operator, with a primary approach involving the investigation of a novel Gronwall inequality using the Atangana-Baleanu difference variable-order sum operator. This analysis leads to the development of a uniqueness theorem and a criterion for the stability in finite-time of variable-order discrete neural networks. Furthermore, the requirements stemming from this type of stability and the novel Gronwall inequality serve as the foundation for establishing the conditions necessary for achieving finite-time synchronization in these networks, employing a specific control using state feedback method. Finally, the study utilizes numerical solutions to validate the obtained results.

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AI-Driven Mobile Healthcare: Societal Impact, Workforce Adaptation, and Enhanced Predictive Modeling for Diabetes Risk Assessment

AI-Driven Mobile Healthcare: Societal Impact, Workforce Adap...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Shreyas Rajendra Hole Vinothkumar Kolluru Ranita Gangul Sagar Kolekar Advaitha Naidu Chintakunta Sudeep Mungara Computer Science & Engineering (AI& ML) Dayananda Sagar University Bangalore India Department of data Science Stevens Institute of Technology Hoboken NJ U.S.A. Computer Science & Engineering Delaware State University Bangalore India Department of Civil Engineering Symbiosis Institute of Technology (SIT) Symbiosis International (Deemed University) Pune India Department of Computer Science University of North Carolina at Charlotte Charlotte NC U.S.A. Masters in Information Systems Stevens Institute of Technology Hoboken NJ U.S.A.

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

With advancements in artificial intelligence, it is natural for the health industry to focus on IOT devices. AI could promote a paradigm shift in how doctors and patients interact through immediate updates, predictive features, and remote access. The focus of this study is the outreach and employment aspects revolving around AI-enabled health care, with special attention to mHealth solutions. We searched through published data and came across 112 relevant studies identifying keywords such as accessibility, empowerment, and workforce. One of the most interesting findings was the fact that AI targeting mobile applications has the possibility of increasing access to health services by approximately 60 percent for rural areas, as well as increasing the patient cooperative rate by approximately 45%, where a predictive health tracking device has been utilized. We proposed two new measures: the Physical Activity and Nutrition Index (PANI) and Health Status Index (HSI), allowing for better diabetes risk predictions with a 0.82 AUC-ROC and better generalizability than traditional baselines, with an improvement of 12%. When identifying the best available ML models, including Logistic Regression, Random Forest, XGBoost, LightGBM, and Bayesian Neural Networks, we found that the hybrid Random Forest + Linear SVC model had the best performance with recall and accuracy of 0.77 and 0.72, respectively. The ability of the model to demonstrate both interpretability and specificity was evident. It was also observed that synthetic data augmentation (SMOTE) increased the recall for minority class predictions by 18% but did not reduce specificity.

关键词： Adaptation models Uncertainty Neural networks Medical services Predictive models Diabetes Bayes methods Risk management Indexes Random forests

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Facial Expression Recognition in the Wild using Artificial Rabbits Optimizer based Residual Neural Network

Facial Expression Recognition in the Wild using Artificial R...

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Advances in Computation, Communication and information Technology (ICAICCIT), International Conference on

作者： P S Metkewar N Uma Rajesh Kumar Dhanaraj Balasubramanian Prabhu Kavin Malathy Sathyamoorthy Symbiosis Institute of Computer Studies and Research (SICSR) Symbiosis International (Deemed University) (SIU) Pune Maharashtra India New Horizon College of Engineering Bengaluru Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chengalpattu India Department of Information Technology KPR Institute of Engineering and Technology Coimbatore India

In affective computing, emotion acknowledgement in the wild is a much-studied area. Although there have been advancements, the difficulty of emotion acknowledgement in the wild due to head movement, face deformation, illumination fluctuation, etc. remains an open subject. To improve the model's simplification ability and, by extension, its performance across a variety of learning tasks, it is crucial that a wide variety of features be extracted by the deep neural network. Interest in facial expression detection in natural settings has grown in recent years despite the difficulty of obtaining discriminative and informative characteristics from partially obscured photos. In the first stage of this paper's pre-processing, the noisy pictures are enhanced by Contrast Limited Adaptive Histogram Equalization (CLAHE). After that, Residual Neural Network (ResNet) features extraction is used, and the same method serves as an emotion classifier. An iterative technique called stochastic gradient descent (SGD) is utilized to refine the ResNet model's objective function. Finally, the Artificial Rabbits Optimization Algorithm (ARO) is used to choose the best value for, thereby enhancing the reliability of the categorization. The suggested method is effective, as evidenced by experimental findings on three popular facial expression recognition in-the-wild datasets: AffectNet, AFEW dataset, and RAF-DB, where it accomplishes state-of-the-art presentation with 96% accuracy and improves upon existing models by roughly 5% to 8%.

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Deep Learning Methods for Anatomical Landmark Detection in Video Capsule Endoscopy Images

Deep Learning Methods for Anatomical Landmark Detection in V...

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Future Technologies Conference, FTC 2020

作者： Adewole, Sodiq Yeghyayan, Michelle Hyatt, Dylan Ehsan, Lubaina Jablonski, James Copland, Andrew Syed, Sana Brown, Donald Department of Systems and Information Engineering University of Virginia CharlottesvilleVA United States School of Data Science University of Virginia CharlottesvilleVA United States Department of Pediatrics School of Medicine University of Virginia CharlottesvilleVA United States

ISBN: (纸本)9783030631277

Video capsule endoscope (VCE) is an emerging technology that allows examination of the entire gastrointestinal (GI) tract with minimal invasion. While traditional endoscopywith biopsy procedures are the gold standard for diagnosis of most GI diseases, they are limited by how far the scope can be advanced in the tract and are also invasive. VCE allows gastroenterologists to investigate GI tract abnormalities in detail with visualization of all parts of the GI tract. It captures continuous real time images as it is propelled in the GI tract by gut motility. Even though VCE allows for thorough examination, reviewing and analyzing up to eight hours of images (compiled as videos) is tedious and not cost effective. In order to pave way for automation of VCE-based GI disease diagnosis, detecting the location of the capsule would allow for a more focused analysis as well as abnormality detection in each region of the GI tract. In this paper, we compared four deep Convolutional Neural Network models for feature extraction and detection of the anatomical part within the GI tract captured by VCE images. Our results showed that VGG-Net has superior performance with the highest average accuracy, precision, recall and, F1-score compared to other state of the art architectures: GoogLeNet, AlexNet and, ResNet. © 2021, Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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Prediction of Paddy Plant Height with Vermicompost Fertilizer Treatment on Tidal Land using ANFIS Method 6

Prediction of Paddy Plant Height with Vermicompost Fertilize...

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6th International Conference on Informatics and Computing, ICIC 2021

作者： Rahman, Abdul Ermatita Budianta, Dedik Abdiansah Multi Data Palembang University Department of Informatics Palembang Indonesia Sriwijaya University Department of Information Systems Palembang Indonesia Sriwijaya University Department of Soil Science Palembang Indonesia Sriwijaya University Department of Informatics Engineering Palembang Indonesia

ISBN: (纸本)9781665421553

The main problem in tidal land is high soil acidity, and the availability of nutrients in the soil is relatively low. Utilization of local resource vermicompost is used to improve soil conditions in tidal lands in order to increase crop yields. The parameter of paddy plant height has a very high correlation with paddy yields. This study aims to implement the ANFIS method to predict paddy plant height based on the treatment of vermicompost organic fertilizer. The dataset used for ANFIS training was taken directly from the observation data on the height of the paddy plant and the results of soil laboratory tests. The ANFIS process consists of 5 inputs consisting of fertilizer treatment, pH, N, P, K, and one output, namely paddy plant height. The results obtained from the training data process are that there are 486 rules and the error rate using MAPE is 3.53%, or the accuracy level of the prediction results is 96.47%. © 2021 IEEE.

关键词： Forecasting

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CancerAI: A Deep Learning Framework for Ovarian Cancer Prediction

CancerAI: A Deep Learning Framework for Ovarian Cancer Predi...

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Electronics and Sustainable Communication systems (ICESC), 2020 International Conference on

作者： G. Revathy P. Uma Maheswari S. Madhavi Vijay Anand P R. Vajubunnisa Begum H. Jasmin Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India Department of Computer science and Business systems K. Ramakrishnan college of Engineering Trichy India Department of Information Technology Vel tech Multi tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Artificial Intelligence and Data Science CMR Institute of Technology Bengaluru India Department of Electronics and Communication Science Justice Basheer Ahmed Sayeed College for Women Affiliated by University of Madras Chennai India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Ovarian cancer is the fifth most common cause of death from cancer in women. This is mainly because it is often diagnosed at a late stage, as the earliest symptoms are unclear and inconsistent. Existing diagnostic techniques, such as biomarkers, biopsies, and imaging tests, have notable drawbacks such as reliance on subjective interpretation, inconsistency across different observers, and time-consuming testing procedures. This research study introduces an innovative deep learning framework that employs a convolutional neural network (CNN) algorithm to accurately predict and diagnose ovarian cancer, thereby overcoming the existing constraints. The CNN was trained using a dataset of histopathology images. The dataset was partitioned into test and training subsets to enhance the model's performance. The algorithm demonstrated an impressive accuracy rate of 96%, successfully detecting 97.12% of malignant instances and precisely categorizing 95.02% of healthy cells. This method effectively mitigates the challenges related to human expert evaluation, including elevated rates of misclassification and variability across different observers, while also reducing the time required for analysis. The results underscore the capability of this CNN-based technique to offer a more precise, effective, and dependable strategy for forecasting and detecting ovarian cancer. Subsequent investigations will prioritize the integration of new breakthroughs in deep learning to further amplify the efficacy of the suggested approach.

关键词： Deep learning Training Accuracy Histopathology Transfer learning Observers Prediction algorithms Partitioning algorithms Convolutional neural networks Ovarian cancer

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