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共 11091 条记录，以下是171-180 订阅

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SFDEM: A Single Flow Detection Enabled Method for DDoS Attacks in IoT with Feature Mapping

SFDEM: A Single Flow Detection Enabled Method for DDoS Attac...

引用

international conference on data Security and Privacy Protection (DSPP)

作者： Xie, Lixia Yuan, Bingdi Yang, Hongyu Zhang, Liang Civil Aviat Univ China Sch Comp Sci & Technol Tianjin Peoples R China Civil Aviat Univ China Sch Comp Sci & Technol Sch Safety Sci & Engn Tianjin Peoples R China Univ Arizona Sch Informat Tucson AZ USA

ISBN: (纸本)9798350303155

To address the slow response time of existing detection modules to modern multi-type Internet of Things DDoS(Distributed Denial of Service) attacks, their low feature differentiation, and poor detection performance, we proposed SFDEM, a single flow detection enabled method with feature mapping. Initially, SFDEM employs a queue to store previously arrived flow. The features of the end-queue flow are represented by the overall characteristics of the queue, which enables SFDEM to detect the single flow upon its arrival with low latency. Second, we design a multidimensional reconstruction encoder and introduce the derivative function of the hyperbolic tangent function to enhance the similarity of the same type of flow and the differentiation between different types of flow. Finally, SFDEM employs machine learning classifiers to detect DDoS attacks using the mapped features of the flow. On the CICDDoS2019 and the Bot-IoT datasets, the average metrics value of SFDEM is a maximum of 12.01% higher than other existing methods.

关键词： DDoS attack detection single flow feature mapping machine learning

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Nearest Neighbors Counterfactuals 10th

Nearest Neighbors Counterfactuals

引用

10th international conference on machine learning, Optimization, and data science, LOD 2024

作者： Magagnini, Marica Carrizosa, Emilio De Leone, Renato School of Science and Technology Università di Camerino Camerino62032 Italy Instituto de Matemáticas de la Universidad de Sevilla Seville41012 Spain

ISBN: (纸本)9783031824807

machine learning algorithms usually make decisions without providing any explanations on how they were reached. As a result, such models are not understood and trusted by the users. Counterfactuals are user-friendly explanations that provide valuable information to determine what should be changed in order to modify the outcome of a black box decision-making model without revealing the underlying algorithmic details. We present a novel technique to obtain counterfactual explanations by solving an optimization problem when a k-Nearest Neighborhood classifier is employed in a binary classification. Results from artificial and real datasets demonstrate the validity of the proposal. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Deep learning-based Mortality Prediction of COVID-19 Patients with Cardiovascular Disease 9

Deep Learning-based Mortality Prediction of COVID-19 Patient...

引用

9th IEEE international conference on data science in Cyberspace, DSC 2024

作者： Zheng, Huilin Liang, Yingyi Zhang, Daming Shi, Kai Dong, Luxi Xie, Xiaolan Guilin University of Technology Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology College of Computer Science and Engineering Guilin China

ISBN: (纸本)9798350391367

Coronavirus disease 2019 (COVID-19) is one of the most dangerous diseases in recorded human history due to its high contagiousness and rapid dissemination. Moreover, people who already have medical issues like cardiovascular disease (CVD) have a higher risk of getting seriously ill. Therefore, this study proposes a deep learning-based prediction model for the mortality of COVID-19 patients with CVD. The research substances are introduced as follows. First, we obtain the original dataset from the Kaggle website provided by the Mexican government, and extract our experimental data. After that, we develop a deep learning-based prediction model for the mortality of COVID-19 patients with CVD while applying a 5-fold cross-validation approach for the model training and validation. Finally, we evaluate the performance of the proposed model and also compare it with many common machine learning models. In our experimental results, the proposed model achieved higher performance than all of the other models. It facilitates decision-making and provides an accurate mortality prediction for COVID-19 patients with CVD. © 2024 IEEE.

关键词： COVID-19

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FedLRDP: Federated learning Framework with Local Random Differential Privacy

FedLRDP: Federated Learning Framework with Local Random Diff...

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international Joint conference on Neural Networks (IJCNN)

作者： Zhou, Runtian Dong, Anming Yu, Jiguo Ding, Qingyan Qilu Univ Technol Shandong Acad Sci Key Lab Comp Power Network & Informat Secur Minist EducShandong Comp Sci CtrNatl Supercomp Jinan 250014 Peoples R China Qilu Univ Technol Shandong Acad Sci Sch Comp Sci & Technol Jinan 250353 Peoples R China Qilu Univ Technol Shandong Acad Sci Big Data Inst Jinan 250014 Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Federated learning (FL) is a distributed machine learning framework enabling multiple clients to collaboratively train a shared ML model without sharing raw data. Despite its aim to safeguard data security and privacy, FL faces risks from advanced adversarial attacks like membership inference attack (MIA), potentially leaking sensitive information. To counter these threats, differential privacy (DP) methods add noise to shared model parameters. However, traditional DP methods struggle with balancing model accuracy, training efficiency, and privacy protection, often compromising one for the other. Additionally, uniform DP mechanisms may not cater to varying client privacy and performance needs. To tackle these challenges, we propose a federated learning framework based on localized random differential privacy (FedLRDP). This approach empowers each client to control noise levels based on their privacy requirements, enhancing model performance while preserving data privacy. We further optimize client-side loss functions to enhance model performance. Theoretical analysis establishes the convergence bounds for FedLRDP, demonstrating its superior convergence performance. Experimental results on MNIST reveal that FedLRDP improves model accuracy by 3.28% compared to traditional DP methods while mitigating MIA with a 72.48% lower attack success rate than FedAvg. Moreover, on EMNIST, FedLRDP boosts model accuracy by 7.31% compared to traditional DP methods, maintaining a 59.4% lower attack success rate than FedAvg. These findings underscore FedLRDP's efficacy in meeting client privacy needs while enhancing model performance.

关键词： Federated learning differential privacy privacy preservation convergence performance

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Communication-Efficient Personalized Federated learning on Non-IID data 19

Communication-Efficient Personalized Federated Learning on N...

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19th international conference on Mobility, Sensing and Networking (MSN)

作者： Li, Xiangqian Ma, Chunmei Huang, Baogui Li, Guangshun Qufu Normal Univ Qufu Shandong Peoples R China

ISBN: (纸本)9798350358261;9798350358278

In this paper, we explore the challenges associated with federated learning, a distributed machine learning paradigm that promotes collaborative model training while preserving the privacy of local client data. One significant hurdle is the non-IID nature of clients' data, alongside limited communication resources between clients and the cloud server. These statistical heterogeneity and communication resource limitations pose practical obstacles to the implementation of federated learning. To address these challenges, we propose a communication-efficient framework called GCPFL for personalized federated learning. Our framework empowers individual clients to train personalized models while substantially reducing communication costs. Specifically, each client compresses the gradient before uploading it and handles the effects of gradient compression through an error correction process. By uploading only the compressed gradients, the communication costs are significantly diminished. On the cloud server side, the received gradients are recovered into models, and similarity aggregation is performed on these models to facilitate collaboration among clients. Once the aggregated models are received, clients conduct local updates to acquire personalized models. Extensive experimental results illustrate that the GCPFL algorithm not only achieves high model accuracy but also substantially reduces communication costs compared to existing methods.

关键词： federated learning non-IID communication personalized

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When social networks meet payment: a security perspective 17

When social networks meet payment: a security perspective

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17th international conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Singh, Nivedita Alawami, Mohsen Ali Kim, Hyoungshick Sungkyunkwan University Computer Science and Engineering Korea Republic of Sungkyunkwan University Electrical and Computer Engineering Korea Republic of

ISBN: (纸本)9781665453486

In the big data arena, opportunities and challenges are mixed. The volume of data in the financial institution is proliferating, which imposes a challenge to big data analytics to ensure safety during each transaction. Moreover, as more and more social networking sites (SNS) are integrating an inbuilt online payment system into their domain, an exponential surge in financial scams is expected in the upcoming days. These scenarios are alarming, and with the rapid growth in the daily addition of new end users and their increasing time spent on SNS, the situations become more vulnerable. With the existing trend of data mobilizations and rapid increase in volume, variety, and velocity of data being produced, big data has a significant role in detecting fraud incidents in financial transactions. However, in the framework of present followed international standards, there is a voluntary compliance obligation on domestic governing bodies, which is a significant source for such voluminous financial frauds on SNS. In order to strengthen the enforcement of international standards to combat financial transactions on SNS, the paper proposes that domestic legislation should comply with international standards with the further addition of machine learning encircled by domestic banking legislation. Eventually, this could solve the security and privacy governance difficulties arising from these financial frauds over SNS. We believe that with our approach of three-layer security i.e. by international standards, domestic legislation, and machine learning, the finan-cial fraud arising due to the SNS payment system will be reduced to a larger extent. © 2023 IEEE.

关键词： machine learning

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machine learning-based Early Attack Detection Using Open RAN Intelligent Controller

Machine Learning-based Early Attack Detection Using Open RAN...

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IEEE international conference on Communications (IEEE ICC)

作者： Xavier, Bruno Missi Dzaferagic, Merim Collins, Diarmuid Comarela, Giovanni Martinello, Magnos Ruffini, Marco Univ Fed Espirito Santo Vitoria ES Brazil Trinity Coll Dublin Dublin Ireland

ISBN: (纸本)9781538674628

We design and demonstrate a method for early detection of Denial-of-Service attacks. The proposed approach takes advantage of the OpenRAN framework to collect measurements from the air interface (for attack detection) and to dynamically control the operation of the Radio Access Network (RAN). For that purpose, we developed our near-Real Time (RT) RAN Intelligent Controller (RIC) interface. We apply and analyze a wide range of machine learning algorithms to data traffic analysis that satisfy the accuracy and latency requirements set by the near-RT RIC. Our results show that the proposed framework is able to correctly classify genuine vs. malicious traffic with high accuracy (i.e., 95%) in a realistic testbed environment, allowing us to detect attacks already at the Distributed Unit (DU), before malicious traffic even enters the Centralized Unit (CU).

关键词： machine learning mobile networks 4G and 5G

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Incremental learning Algorithms for Broad learning System with Node and Input Addition

Incremental Learning Algorithms for Broad Learning System wi...

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2024 IEEE international conference on Systems, Man, and Cybernetics, SMC 2024

作者： Chen, Guang-Ze Jin, Junwei Sun, Hai-Wei Philip Chen, C.L. Faculty of Science and Technology University of Macau Department of Mathematics 99999 China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China School of Computer Science and Engineering South China University of Technology and Pazhou Lab Guangzhou510641 510335 China

ISBN: (纸本)9781665410205

The Broad learning System (BLS) has been established as an effective flat network alternative to Deep Neural Networks (DNNs), delivering high efficiency while achieving competitive accuracy. Despite its advantages, the incremental learning methods of BLS face challenges in stability and computation when expanding with new nodes or input. We introduce two novel incremental learning algorithms based on factorization updates for BLS that optimize node and input additions to overcome these limitations. Our node addition algorithm utilizes QR decomposition and Cholesky factorization, using the update of the Cholesky factor instead of pseudo-inverse computations. For input addition, we propose an iterative Cholesky factor update algorithm. Our algorithms demonstrate not only faster computation compared to the existing BLS but also improved testing accuracy on the MNIST or Fashion-MNIST dataset. This work presents a significant step forward in the practical application and scalability of BLS in various data-dense environments. © 2024 IEEE.

关键词： Adversarial machine learning

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Factor Analysis: Uncovering Factor Structure in the Indonesian Teacher Engagement Index (ITEI) Survey Results data 11

Factor Analysis: Uncovering Factor Structure in the Indonesi...

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11th international conference on Computer, Control, Informatics and its Applications, IC3INA 2024

作者： Cam, Grace Shirley Sasmoko Indrianti, Yasinta Manalu, Sonya Rapinta Moniaga, Jurike V. Data Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Primary Teacher Education Department Faculty Humanities Bina Nusantara University Jakarta Indonesia Entrepreneurship Department Podomoro University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331542313

This study aims to explore the latent structure of the Indonesian Teacher Engagement Index (ITEI) based on the ITEI survey results of primary school teachers in various regions of Indonesia. Exploration of the ITEI structure is obtained by identifying the main factors hidden in the ITEI survey data with machine learning. The machine learning method used is Exploratory factor analysis (EFA) with Principal Component Analysis (PCA). The ITEI dataset consists of 10,365 records, covering 44 items from 6 ITEI dimensions. The machine learning analysis yielded 4 main factors that became new dimensions, explaining the variation of 29 items. The four new factors found are summarized into the dimensions of Values and Ethics in Education, Personal and Social Development in Education, Character and Leadership in Education, and Social Competence and Justice in Education. This exploration was conducted to show the latent structure of the ITEI that matches the real situation and provide a deeper understanding of the main components that make up the teacher engagement index in Indonesia, so that conclusions can be drawn accurately from the survey results. The implications of these findings can be used to improve understanding and development of more effective education policies in the future. © 2024 IEEE.

关键词： Adversarial machine learning

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RICE-HDM: a remote sensing framework for monitoring rice high-temperature heat damage in the middle and lower reaches of the Yangtze River 24

RICE-HDM: a remote sensing framework for monitoring rice hig...

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3rd international conference on Frontiers of Artificial Intelligence and machine learning (FAIML)

作者： Liu, Li Yuan, Cian Chen, Yan Chen, Zixuan Ren, Dong Xiong, Biao China Three Gorges Univ Hubei Key Lab Intelligent Vis Based Monitoring Hy Yichang Hubei Peoples R China China Three Gorges Univ Coll Comp & Informat Technol Yichang Hubei Peoples R China Zhejiang Acad Surveying & Mapping Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9798400709777

In the middle and lower reaches of the Yangtze River Basin in China, a large amount of rice enters the heading and flowering stage during the hot summer following the plum rain season. The prolonged high temperature easily triggers widespread rice high-temperature heat damage, resulting in severe economic losses. This paper proposes a based remote sensing paddy rice high-temperature heat damage monitoring (RICE-HDM) framework, to monitor the high-temperature heat damage in Hubei, Hunan, Jiangxi, Jiangsu, and Anhui province, from 2003 to 2019. Firstly, based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) products and other data, we use Random Forest model to estimate the daily average temperatures in the study area. Secondly, a multi-platform data fusion and spatial interpolation module for processing daily average temperature images is proposed to construct a fully covered high-precision remote sensing dataset of daily average temperature. Finally, we utilize the produced daily average temperature dataset and rice information dataset obtained from our previous studies to estimate the proportion of rice heat damage areas from 2003 to 2019 of study area and to conduct disaster extent assessments, producing disaster distribution maps. The experimental results show that the proposed framework achieves quantitative and accurate monitoring of rice high-temperature heat damage, which is helpful for guiding real-time monitoring of the disaster and the assessment of the disaster.

关键词： High-temperature heat damage data fusion Paddy rice Remote sensing Random Forest

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