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检索条件"任意字段=2019 International Conference on Applied Machine Learning and Data Science, ICAMLDS 2019"

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Anti-DDoS Attacks Strategy of SDN data Plane with data Augmentation Based on P4 25

Anti-DDoS Attacks Strategy of SDN Data Plane with Data Augme...

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25th IEEE international conferences on High Performance Computing and Communications, 9th international conference on data science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Zhang, Wenxiu Jing, Shan Zhao, Chuan School of Information Science and Engineering University of Jinan Jinan China Quan Cheng Laboratory Jinan China

ISBN: (纸本)9798350330014

As Software Defined Networking (SDN) becomes increasingly prevalent, the risk of Distributed Denial of Service (DDoS) attacks targeting SDN also grows. Specifically, the SDN data plane, consisting of simple forwarding devices, is more susceptible to DDoS attacks launched by malicious actors. These attacks can impose tremendous load on the SDN, potentially leading to its complete collapse in severe cases. The traditional SDN architecture based on the OpenFlow Protocol falls short in meeting the requirements for programming the data plane. To address this challenge and improve the flexibility of SDN architectures, the Programming Protocol-Independent Packet Processors (P4) has emerged. However, existing solutions, primarily based on Statistical learning, machine learning, and Deep learning, require further enhancement in terms of accuracy, complexity, and latency. In this study, we utilize the P4 language to implement a programmable data plane and propose the P4-CACNN model for detecting and defending against DDoS attacks on the data plane. Firstly, we employ an appropriate attention mechanism to enhance the processing of incoming traffic. Subsequently, the data is fed into a discriminator consisting of a Convolutional Neural Network (CNN) to classify whether it constitutes attack traffic. Experimental results demonstrate that our proposed model achieves a remarkable 98.99% accuracy in detecting DDoS attacks on the SDN data plane, while maintaining low latency. © 2023 IEEE.

关键词： Denial-of-service attack

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Improving Network Security with Hybrid Model for DDoS Attack Detection 3

Improving Network Security with Hybrid Model for DDoS Attack...

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3rd IEEE international conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Dandotiya, Monika Singh Makwana, Rajni Ranjan Madhav Institue of Technology & Science Department of Computer Science & Engineering MP Gwalior India

ISBN: (纸本)9798350354348

One of the most devastating forms of attack in the modern day is cyber-attacks. DDoS (Distributed Denial of Service) attacks are one kind of cyberattack. It is a kind of cyberattack in which the perpetrator takes over a network or a system and disables it, either temporarily or permanently, preventing its intended users from accessing it. Put simply, it's an attack where the target computer is bombarded with needless requests in an effort to overload it, crash it, and prevent people from accessing that network or machine. So, the most important thing to do to guard against DDoS attacks is to separate network data as soon as possible. In this study, we provide a new approach to intrusion detection algorithms known as CNN-GRU Attention. It combines Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), & a self-attention mechanism to improve algorithm accuracy. The model's structure is intended to effectively represent the geographical and temporal aspects of network flows. The experimental findings highlight the effectiveness of deep learning in cybersecurity applications, outperforming standard models. The experimental outcome showed that suggested hybrid model outperformed the competition on CICDDoS2019 data set, suggesting that it might be useful for detecting and categorising DDoS attack traffic. We evaluated the proposed system compared to state-of-the-art models. We are pleased to report that the accuracy of our work was 99.6%. © 2024 IEEE.

关键词： Network intrusion

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Prediction of High-Resolution Atmospheric CO2 Concentration from OCO-2 using machine learning 23

Prediction of High-Resolution Atmospheric CO2 Concentration ...

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6th ACM India Joint international conference on data science and Management of data, CODS-COMAD 2023

作者： Pais, Spurthy Maria Bhattacharjee, Shrutilipi Madasamy, Anand Kumar Department of Information Technology National Institute of Technology KarnatakaSurathkal Mangaluru India

ISBN: (纸本)9781450397988

Carbon Dioxide (CO2) is a greenhouse gas (GHG) emitted by human anthropogenic activities. The satellite measurement of atmospheric column-averaged CO2 concentration (XCO2) provides an excellent opportunity to understand the global carbon cycle for a large comprehensive temporal range. Orbiting Carbon Observatory-2 (OCO-2) satellite provides highly accurate data with a spatial resolution of approximately 3 km2. However, OCO-2 measures one location on the Earth's surface almost fortnightly. Also, the clouds and aerosols cause missing data. In this work, the OCO-2 measurements, along with Open-Source data Inventory for Anthropogenic CO2 (ODIAC) emission estimate, are considered. A spatial upscaling, followed by different machine learning methods are used to predict high-resolution, continuous mapping of XCO2. The prediction models are evaluated using the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) for Germany, considering a temporal range of November 2018 to December 2019. The least error is attained by monthly model, achieving a MAE and RMSE of 0.707 ppm and 1.187 ppm, respectively, using the extremely randomized trees (ERT) method. The predictions are externally validated using Total Carbon Column Observing Network (TCCON) ground-based measurements as well. © 2023 ACM.

关键词： Carbon dioxide

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Quadric Hypersurface Intersection for Manifold learning in Feature Space 25

Quadric Hypersurface Intersection for Manifold Learning in F...

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international conference on Artificial Intelligence and Statistics

作者： Pavutnitskiy, Fedor Klochkov, Artem Ivanov, Sergei O. Vialov, Viktor Abramov, Evgeny Zaikovskii, Anatolii Borovitskiy, Viacheslav Petiushko, Aleksandr HSE Univ Moscow Russia St Petersburg State Univ St Petersburg Russia Lomonosov MSU Moscow Russia Russian Acad Sci St Petersburg Dept Steklov Math Inst St Petersburg Russia

The knowledge that data lies close to a particular submanifold of the ambient Euclidean space may be useful in a number of ways. For instance, one may want to automatically mark any point far away from the submanifold as an outlier or to use the geometry to come up with a better distance metric. Manifold learning problems are often posed in a very high dimension, e.g. for spaces of images or spaces of words. Today, with deep representation learning on the rise in areas such as computer vision and natural language processing, many problems of this kind may be transformed into problems of moderately high dimension, typically of the order of hundreds. Motivated by this, we propose a manifold learning technique suitable for moderately high dimension and large datasets. The manifold is learned from the training data in the form of an intersection of quadric hypersurfaces-simple but expressive objects. At test time, this manifold can be used to introduce a computationally efficient outlier score for arbitrary new data points and to improve a given similarity metric by incorporating the learned geometric structure into it.

关键词： Large dataset

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machine learning analysis using PyLEnM package of Chernobyl long-term groundwater monitoring data: first experience and opportunities 17

Machine learning analysis using PyLEnM package of Chernobyl ...

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17th international conference Monitoring of Geological Processes and Ecological Condition of the Environment, Monitoring 2023

作者： Bugai, D. Kireev, S. Faybishenko, B. Institute of Geological Sciences Ukraine Ecocenter Chernobyl Ukraine Lawrence Berkeley National Laboratory BerkeleyCA United States

ISBN: (纸本)9789462824874

The PyLEnM framework based on unsupervised machine learning (ML) algorithms was applied to analyse Chernobyl groundwater monitoring data set comprising of 74 monitoring wells within the 10-km zone of Chernobyl nuclear power plant for the period from 1990 to 2019. Concentrations of 90Sr and 137Cs in groundwater were studied depending on the groundwater water level, surface deposition of fallout radionuclides, monitoring borehole design characteristics and distance from the source of release. No strong positive correlation of radionuclide groundwater concentrations with land surface contamination was determined, suggesting that a larger set of attributes is needed to explain variability of groundwater data. Time trends of 90Sr in groundwater and results of ML analyses are consistent with the conceptual model assuming a gradual release from dissolving fuel particles and its downward transport through vadose zone to groundwater. The results for 137Cs can likely be explained by a combination of facilitated transport and fixation to clay minerals in the topsoil layer. The conducted pilot computational analysis of the Chernobyl dataset offers further opportunities for in-depth research on the application of AI/ML-guided analyses in order to improve conceptual understanding of radionuclide migration process and draw lessons from radiation monitoring experience. © 2023 17th international conference Monitoring of Geological Processes and Ecological Condition of the Environment, Monitoring 2023. All rights reserved.

关键词： Groundwater

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Deep learning-based Real-Time Detection of Emphysema in Chest Radiographs 2

Deep Learning-based Real-Time Detection of Emphysema in Ches...

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2nd international conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Ramalingam, Ramadoss Chinnaiyan, Vimala Department of Electronics and Communication Engineering College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chengalpattu603203 India

ISBN: (纸本)9798350340235

In 2019, chronic obstructive pulmonary disease (COPD) led to 3.23 million deaths on a global scale, positioning it as the third most prevalent cause of mortality. Emphysema and chronic bronchitis are termed the World Health Organization (WHO) uses to describe COPD. The typical depiction of emphysema involves the damage to the tiny air sacs located at the ends of the lung airways. Chronic bronchitis causes airway irritation, coughing, and phlegm. Deep learning was applied to multiple CNN architectures (AlexNet, ResNet 50, ResNet 101, and DenseNet 201) to extract features from chest X-rays. Chest x-rays were classified as having or not having emphysema using machine learning classifiers, including nearest neighbor classifiers (kNN), kernel approximation classifiers (SVM kernel and logistic regression), neural network classifiers (NN), decision tree classifiers, Naive Bayes classifiers, discriminant analysis, and support vector machines (SVM). Densenet201+ SVM achieved 93.1% total validation accuracy across all 7548 images with an AUC of 0.9781, and test accuracy is 100% with an AUC of 1.0 across all 1510 images. Therefore, DenseNet 201+ SVM is the highest of the four methods across all 7548 images. © 2023 IEEE.

关键词： Chest x-ray Convolutional Neural Network (CNN) Deeplearning Emphysema machine learning classifiers

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Prediction of Flight Fares Using machine learning 13

Prediction of Flight Fares Using Machine Learning

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13th international conference on Cloud Computing, data science and Engineering, Confluence 2023

作者： Tuli, Manya Singh, Leena Tripathi, Sudhanshu Malik, Nidhi Amity University Aset Dept. of Cse Noida India Amity University Dept. of It and Engg. Tashkent Uzbekistan The NorthCap University Deptt. of Cse Gurgaon India

ISBN: (纸本)9781665462631

The airline industry has become a very organized sector given the magnitude of travel undertaken by the population these days. However, the airline ticket pricing is a fluid parameter because of its dependency on various factors. The variation in ticket prices has led to customers traveling with different fares even on the same flight. This results in revenue losses for the airline industry due to operation of under booked flights and high customer dissatisfaction. The prediction of flight prices at an early stage would help airlines strategize their operations and gather the necessary resources impacting a specific market segment level for a route. Thus, this paper aims to propose a model which can capture the variability between different factors affecting the cost of a flight ticket and arrive at the model with least mean absolute error for estimating the prices of a trip. The dataset used in this paper has been provided with prices of flight tickets for various airlines between March to June 2019. From the algorithms implemented on the dataset, an ensemble of the Artificial Neural Networks, XGBoost Regressor, and Light Gradient Boosting machine Regression models gives the least mean absolute error of 1226.6989. © 2023 IEEE.

关键词： Air transportation

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Wheat, Rice and Corn Yield Prediction for Jammu District Using machine learning Techniques 7th

Wheat, Rice and Corn Yield Prediction for Jammu District Usi...

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7th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2023

作者： Gandotra, Sakshi Chhikara, Rita Dhull, Anuradha The NorthCap University Gurugram India

ISBN: (纸本)9789819940394

Timely prediction of agricultural produce has always been a significant estimator for various stakeholders in agricultural economy. High computing capacity coupled with machine learning techniques has led humans to harness the potential of data sets to address challenges in agriculture sector. In pursuit to identify the most appropriate machine learning model for wheat, rice and corn yield prediction for Jammu district of J&K, we present the performance of machine learning models of Long Short-Term Memory, Gated Recurrent Unit, Bi directional long short-term memory and classic DNN along with the baseline models of Support Vector Regression, Random Forest Regressor and Ensemble method of boosting (adaboost). Environmental data set comprising temperature, rainfall, humidity, solar radiation, basic sunshine hours for the span of year 2009–2019 has been employed for this study. Yield and environment data set have been procured from Directorate of Agriculture and Sher—e—Kashmir—Agricultural University of science and Technology (SKAUST) Jammu respectively. Results show the long short-term memory outperforms the other evaluated models with low values for loss metrics of RMSE, MAE, MAPE of 0.30, 0.20 and 0.23 respectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Extreme learning machine Algorithm Based on IPSO for Prediction of Blood Glucose Concentration in Patients with Type 1 Diabetes 7

Extreme Learning Machine Algorithm Based on IPSO for Predict...

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7th international conference on Computational Intelligence and Applications, ICCIA 2022

作者： Yu, Liling Zhang, Gangping Xu, Binfeng Liu, Wenping Guo, Jingyu Chen, Yufang Institution of Medical Instruments Guangdong Food and Drug Vocational College Guangzhou China

ISBN: (纸本)9781665495844

Aiming at the complexity and instability of blood glucose data of diabetic patients, this paper introduces an extreme learning machine algorithm (ELM) based on improved particle swarm optimization (IPSO) into the prediction of blood glucose concentration in patients with type I diabetes. First, the blood glucose concentration time series of diabetes patients collected by dynamic blood glucose monitoring is smoothed and normalized to improve the smoothness of the blood glucose data sequence and weaken the randomness of the original blood glucose data sequence. Then, the extreme learning machine is optimized, the improved particle swarm optimization algorithm is introduced to select the appropriate parameters required by the extreme learning machine, and the optimized algorithm is applied to the prediction of blood glucose concentration of diabetic patients. The experimental results show that the extreme learning machine algorithm based on improved particle swarm optimization has higher accuracy for short-term blood glucose concentration prediction of patients. © 2022 IEEE.

关键词： Forecasting

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FIFA: MAKING FAIRNESS MORE GENERALIZABLE IN CLASSIFIERS TRAINED ON IMBALANCED data 11

FIFA: MAKING FAIRNESS MORE GENERALIZABLE IN CLASSIFIERS TRAI...

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11th international conference on learning Representations, ICLR 2023

作者： Deng, Zhun Zhang, Jiayao Zhang, Linjun Ye, Ting Coley, Yates Su, Weijie J. Zou, James Columbia University United States University of Pennsylvania United States Rutgers University United States University of Washington United States Kaiser Permanente Washington Health Research Institute University of Washington United States Stanford University United States

Algorithmic fairness plays an important role in machine learning and imposing fairness constraints during learning is a common approach. However, many datasets are imbalanced in certain label classes (e.g. "healthy") and sensitive subgroups (e.g. "older patients"). Empirically, this imbalance leads to a lack of generalizability not only of classification, but also of fairness properties, especially in over-parameterized models. For example, fairness-aware training may ensure equalized odds (EO) on the training data, but EO is far from being satisfied on new users. In this paper, we propose a theoretically-principled, yet Flexible approach that is Imbalance-Fairness-Aware (FIFA). Specifically, FIFA encourages both classification and fairness generalization and can be flexibly combined with many existing fair learning methods with logits-based losses. While our main focus is on EO, FIFA can be directly applied to achieve equalized opportunity (EqOpt);and under certain conditions, it can also be applied to other fairness notions. We demonstrate the power of FIFA by combining it with a popular fair classification algorithm, and the resulting algorithm achieves significantly better fairness generalization on several real-world datasets. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Classification (of information)

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