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PHISHING DETECTION USING machine learning ALGORIthMS 4

PHISHING DETECTION USING MACHINE LEARNING ALGORITHMS

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4th international conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Mohammed, Moulana Prasanth, K. Koteswara Subhash, S. Venkata Sai Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur India

ISBN: (纸本)9781665401180

Phishing attacks are one of the most serious hazards to individuals and businesses in today's digital world. Many attacks are performed each month with the goal of convincing consumers that they are visiting a reputable website or online application in order to obtain account information. A strategy is provided for detecting these types of assaults by modifying existing Document Object Model (DOM) comparison tools such as Proportional Distance, false positive and false negative, and the favicon image recognition algorithm. there may be a different impact, misclassifying a phishing website than misclassifying a legitimate website if the proportional distance approach is used. To solve this, the proportional distance method is used. © 2022 IEEE

关键词： Websites

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machine learning methods application for outliers detection in epileptic EEG 4

Machine learning methods application for outliers detection ...

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4th international conference "Neurotechnologies and Neurointerfaces", CNN 2022

作者： Koymov, Matvey Grubov, Vadim Maksimenko, Vladimir Utashev, Nikita Andrikov, Denis Kurkin, Semen Baltic Center for Artificial Intelligence and Neurotechnology Immanuel Kant Baltic Federal University Kaliningrad Russia Neuroscience and Cognitive Technology Laboratory Innopolis University Kazan Russia Natl. Med. and Surg. Ctr. Named after N. I. Pirogov Ministry of Healthcare of the Russian Federation Moscow Russia Research and Production Company 'Immersmed' Moscow Russia

In this paper possibility of machine learning meth-ods applications for epileptic activity detection in records of electroencephalography is studied for further application in medical decision-support system. © 2... 详细信息

ISBN: (数字)9781665463294

ISBN: (纸本)9781665463294

关键词： machine learning

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Personalized Fuzzy Federated Prompt Tuning for Re-Identification 4

Personalized Fuzzy Federated Prompt Tuning for Re-Identifica...

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4th IEEE international conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Zhao, Hongwei Zhang, Weishan Sun, Haoyun Zhao, Yikang Liu, Yuru Wang, Ziyu College of Computer Science and Technology Qingdao China

ISBN: (纸本)9798350349252

Contrastive Language-Image Pre-training (CLIP) can not be directly applied in re-identification (ReID) tasks because multiple images may be associated with the same ID description in a batch, which is not applicable for instance contrastive learning in CLIP and brings performance degradation. Additionally, ReID data involves sensitive personal information. Data security and privacy protection mechanism is necessary for model training with multi-party ReID data. Federated learning facilitates collaborative training across multiple parties while preserving data privacy. However, when data is non-independent and non-identically distributed (non-IID), the performance of some clients may decrease. To address these challenges, a Personalized Fuzzy Federated Prompt Tuning method (PFFPT) is proposed in this paper for ReID. PFFPT constructs ID-specific learnable prompt tokens at each client, while the global server performs fuzzy federated clustering and dynamic weight aggregation based on local visual feature embeddings. Furthermore, an ID-contrastive Loss is proposed in local client training to tackle the ID-image matching issue. Comprehensive experiments were conducted on two vehicle ReID datasets and eight person ReID datasets. Compared to the traditional FedAVG, PFFPT achieved Rank-1 improvements of more than 10.87 and 10.48 on person ReID and vehicle ReID tasks, respectively, which demonstrates the effectiveness of the proposed method. © 2024 IEEE.

关键词： Federated learning

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4th international Engineering Research Symposium, INERS 2022

4th International Engineering Research Symposium, INERS 2022

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4th international Engineering Research Symposium, INERS 2022

the proceedings contain 12 papers. the topics discussed include: from signal to image: an effective preprocessing to enable deep learning-based classification of ECG;prediction of software quality with machine learning-based ensemble methods;a research on resource allocation algorithms in content of edge, fog and cloud;investigation of the effect of metakaolin substitution on physicomechanical properties of fly ash-based geopolymer mortars;effect of absorption determining methods of superabsorbent polymers in cementitious environments on the fresh properties;conceptual comparison of fuzzy set extensions considering indeterminacy;uplink application circuit for satellite communications;operational issues and proposed solutions in wastewater treatment plants in the western Black Sea basin of Turkey;determination of surface properties of different formwork release agents on strength concrete;comparison of different compressor technologies for refrigerated display cabinet: experimental study;and effect of testing temperature on the impact-sliding wear behavior of a 316L austenitic stainless steel.

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Winter wheat phenology classification using stacking ensemble learning algorithm based on Sentinel-1A SAR images 5

Winter wheat phenology classification using stacking ensembl...

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5th international conference on Geoscience and Remote Sensing Mapping, ICGRSM 2023

作者： Duan, Haoxin Yang, Huijin Zhao, Jianhui Li, Ning School of Computer and Information Engineering Henan University Kaifeng China Henan Province Engineering Research Center of Spatial Information Processing Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (纸本)9781510672789

Crop monitoring and phenology estimation based on the satellite systems have become an important research area due to high demand on crops. Synthetic Aperture Radar (SAR) is a kind of microwave remote sensing equipment, which has the advantage of all-weather and all-day, and can realize large-scale and periodic crop phenological monitoring. Besides, thanks to the high temporal resolution of new generation space-based sensors, it has been possible to monitor growth cycle of crops by classification algorithms. A stacking ensemble learning algorithm using time series Sentinel-1A SAR images for winter wheat phenology classification was proposed in this paper based on multiple machine learning models, including Random Forest (RF), Support Vector machine (SVM), K-nearest Neighbor(K-NN), Naive Bayes (NB) and BP Neural Network (BP) models. the experimental results showed that, comparing with each single model, the stacking ensemble learning algorithm proposed in this paper had the optimal performance, with the highest overall recognition accuracy of 81.40%, demonstrating its effectiveness and application potential for winter wheat phenology identification. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Synthetic aperture radar

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A Data-Driven Approach to Forecasting Bangladesh Next-Generation Economy 1

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4th international conference on machine Intelligence and Signal Processing, MISP 2022

作者： Shohug, Md. Mahfuj Hasan Bitto, Abu Kowshir Rubi, Maksuda Akter Bijoy, Md. Hasan Imam Rahaman, Ashikur Department of Software Engineering Daffodil International University Dhaka1216 Bangladesh Department of General Educational Development Daffodil International University Dhaka1216 Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka1216 Bangladesh

ISBN: (数字)9789819900473

ISBN: (纸本)9789819900466

Bangladesh is the most populous country on the planet. We recognized Bangladesh as an independent sovereign country, yet despite its large population, Bangladesh lags far behind the world’s wealthiest nations. In the developed world, especially in terms of economics, a country’s Gross Domestic Product (GDP) is the monetary value of all completed labor and products produced within its borders during a period. It is concerned with the overall measurement of all financial transactions. In this research, we use a machine learning time series ARIMA model to correctly estimate the Bangladesh GDP Growth Rate with the order of (0, 1, 1). the auto ARIMA function generates a minimal AIC value, which is used to verify the model. the SARIMAX (0, 1,1, 6) model can predict the GDP with 87.51% accuracy. We implement this machine learning time series ARIMA model on the web application;GDP indicator. Users may find the GDP growth rate for Bangladesh in the future here, as well as watch our country’s upcoming economy. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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A web application to demonstrate the properties of Label Propagation algorithms 29

A web application to demonstrate the properties of Label Pro...

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Demonstration Track of the 29th international conference on Cooperative Information Systems, CoopIS-D 2023

作者： Ceravolo, Paolo Maghool, Samira Talpalaru, Andrei Georgiani Department of Computer Science Università Degli Studi di Milano Via Celoria 18 Milano Italy

Vector Label Propagation (VLP) is a machine learning technique used for tasks like semi-supervised learning and graph-based analysis. It is valuable in various fields, including social network analysis and recommendation systems, where each data point has multiple attributes. this paper discusses the development of a web application aimed at demonstrating the Agent-based vector-label propagation algorithm's (AVPRA) operation. Prior to this application, AVPRA execution and visualization were challenging for non-experts, relying on scripts. the web app offers a user-friendly, graphical interface, enabling real-time tracking and transparent visualization of the algorithm's iterations. © 2023 CEUR-WS. All rights reserved.

关键词： Visualization

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Construction of a computer Network Performance Prediction Optimization Model Based on machine learning Algorithms 4

Construction of a Computer Network Performance Prediction Op...

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4th international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Su, Jianhao College of Integrated Circuits and Optoelectronic Chips Shenzhen Technology University Guangdong Shenzhen518118 China

computer networks have been widely used in critical infrastructure for businesses, schools, governments, and individuals. the development of computer networks is an irreversible historical trend. this article studies computer network performance prediction optimization models through machine learning, which can improve the intelligence and efficiency of network management. Existing research has made some progress in machine learning based network security and performance optimization. However, good results have not been achieved in real-time performance prediction and optimization. this article uses Long Short Term Memory Network (LSTM) to establish an effective predictive optimization model. Firstly, the data is processed, such as missing values, duplicates, and device time redundancy. then, features are selected to select the corresponding input features of the model. Finally, the model is optimized to obtain the optimized model. the performance of the LSTM model is compared with the random forest (RF) and support vector machine (SVM) models. Model performance indicators include mean absolute error (MAE), coefficient of determination, F1 score, and model computation time. the experimental results show that the LSTM model has higher accuracy than RF and SVM models in all evaluation metrics. Meanwhile, the LSTM model has significant advantages in computational time compared to the other two models. the LSTM based computer network performance prediction optimization model can be optimized in terms of prediction accuracy, generalization ability, and computational efficiency. It has the ability to effectively process time series data. the LSTM model used for network performance prediction has a minimum average absolute error of 8% and a maximum coefficient of determination of 0.95. It achieves higher F1 scores and faster calculation speed. the results reflect the potential and practicality of the LSTM model in optimizing computer network performance prediction. © 2024

关键词： Prediction models

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Nonlinear Facial Attribute Editing Method based on Autoencoder in StyleGAN Latent Space 4

Nonlinear Facial Attribute Editing Method based on Autoencod...

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4th international conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Kuang, Qixian He, Xiping Yang, Chutian Xie, Xiaoqin Chongqing Technology and Business University School of Artificial Intelligence Chongqing China

ISBN: (纸本)9798350373233

Currently, facial attribute editing based on StyleGAN latent space has achieved great success, but it suffers from problems such as attribute entanglement and identity information loss in real facial attribute editing. To address these issues, this paper suggests utilizing an autoencoder network to extract the high-level identity information from the latent code, and then reconstruct the latent code following the incorporation of conditional semantic information. the embedded conditional semantic information aims to guide the autoencoder to achieve facial attribute editing. In the training process, reconstruction loss and conditional loss are introduced to constrain the autoencoder network, so that it can reconstruct the editing results that keep the identity information unchanged and meet the preset attribute change conditions. the experiments show that this method achieves facial attribute editing with no entanglement, controllability, and retention of identity features in real facial data. In several quantitative evaluation indicators, proposed method is better than the recent methods. © 2023 IEEE.

关键词： learning systems

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Cognitive Twins for Predictive Maintenance and Security in IoT Software Systems 4

Cognitive Twins for Predictive Maintenance and Security in I...

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4th IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Swamy, B. Venkata Barmola, Pareshwar Prasad thangavel, Senthil Kaliappan, S. Patel, Hardik Abhyankar, Girish B V Raju Institute of Technology Department of Chemistry Medak India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Paypal Inc CA United States Lovely Professional University Division of Research and Development Phagwara India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Electronics & Communication Engineering Post Limda India Faculty of Law Pune India

ISBN: (纸本)9798350352931

the increasing complexity and interconnectedness of Internet of things (IoT) software systems necessitate the development of intelligent solutions for predictive maintenance and security. Conventional techniques often fail to provide real-time insights and proactive responses due to the diverse and dynamic nature of IoT environments. To address these challenges, cognitive technologies offer promising avenues for enhancing the operational efficiency and security of IoT networks. this paper introduces Cognitive Twins, an AI-driven framework designed to optimize predictive maintenance and strengthen security in IoT software systems. Cognitive Twins leverage advanced machine learning models and real-time data streams to create dynamic digital replicas of IoT devices and software components. the framework employs a combination of deep learning-based anomaly detection, reinforcement learning for proactive maintenance scheduling, and natural language processing (NLP) for automated security log analysis. By continuously learning from device interactions and evolving threat patterns, Cognitive Twins predict potential failures and detect security threats before they occur, enabling real-time decision-making and automated responses. Cognitive Twins were evaluated on a large-scale IoT network, consisting of 500 nodes across multiple application domains. the framework achieved a predictive maintenance accuracy of 96.8%, reducing downtime by 35% compared to traditional models. In security applications, Cognitive Twins identified cyber threats with a detection rate of 98.3%, lowering the false positive rate to 1.5%. © 2024 IEEE.

关键词： Reinforcement learning

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