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Deep learning-based Worm Detection Method for Polymorphic Networks 4

Deep Learning-based Worm Detection Method for Polymorphic Ne...

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4th IEEE international conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Shen, Wuqiang Wang, Yechao Yao, Chaosheng Xie, Ning Guangdong Power Grid Corporation Guangzhou510220 China China Southern Power Grid Joint Laboratory on Cyberspace Security Guangzhou510220 China Shenzhen E-Link Information Technology Co. Ltd. Shenzhen510663 China

ISBN: (纸本)9798350361643

the maintenance of cybersecurity has become increasingly vital with the advancement of the information age, particularly in the detection of polymorphic network worms. Due to their ability to mutate, these worms can evade the capture of traditional detection techniques, for which the application of deep learning provides a new perspective. this paper delves into the exploration of deep learning, especially Convolutional Neural Networks (CNN), in the field of polymorphic network worm detection. By studying deep learning algorithms and understanding how they operate in complex pattern recognition tasks, this study constructs a detection framework based on CNN for the identification and disruption of worm propagation. In addressing network traffic data, a unique method of effective payload matrix processing is proposed to meet the data input requirements of CNN. this method can extract deep-seated features hidden in traffic data, enabling the effective identification of polymorphic worm attacks. through the establishment of an experimental environment and the validation of the proposed method using standard datasets, the experimental results confirm the accuracy and practicality of this approach in polymorphic worm detection. this work significantly enhances the capability of the network security defense system and lays a foundation for defending against future worm attacks. © 2024 IEEE.

关键词： Network security

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data-Driven Personalized Pregnancy Care: Exploratory data Analysis and Ensemble learning or Personalized Prediction of Gestational Diabetes 4

Data-Driven Personalized Pregnancy Care: Exploratory Data An...

引用

4th international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Loganathan, V. Sreesubha, S. Mohanaprakash, T.A. Ilavarasan, S. Shalinipriya, J. Saveetha Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Aiml Chennai India Cmr University Soet Department of Computer Science and Engineering Bengaluru India Saveetha Engineering College Department of Information Technology Chennai India Sri Sairam Engineering College Department of Eee Chennai India

ISBN: (纸本)9798331508845

the disease in endocrine metabolic named as Gestational Diabetes Mellitus (GDM) is found in pregnant womens due to carbohydrate intolerance. the impact of this disease will not be severe if it is detected early. the prediction model deploys machine learning algorithms to identify the cause and factors. If the dataset have no missing values then only machine learning algorithms outperforms well. these challenges must be addressed. It can be implemented through various perspectives of algorithms with different features. the model must adapt mining the features from the data which eliminates the wrong assumptions and constraints. this proposed model helps to construct the missing data and make it complete one. In order to implement this concept, entire dataset is applied as input and Support Vector machineand K-Nearest Neighbour algorithms based solutions are provided. the dataset is trained mean-square loss function for seismicdata and adam technique to opmize. It also aims to train the dataset in reconstruting the missing values using feature maps and ensemble method using classifers. the performance analysis is made with various other methods for its efficiency. It also addresses the regularly missing data in additionof missing values in the dataset. Finally, the results show that K-Nearest Neighbour algorithm is the state-of-art algorithm in predicting the disease early and addressing missing values in the dataset than Support Vector machine. © 2024 IEEE.

关键词： Prediction models

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the Hidden Influence of Latent Feature Magnitude When learning with Imbalanced data 4th

The Hidden Influence of Latent Feature Magnitude When Learn...

引用

4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Dablain, Damien A. Chawla, Nitesh V. Department Computer Science and Lucy Family Institute for Data and Society University of Notre Dame Notre DameIN46553 United States

ISBN: (纸本)9789819787012

machine learning (ML) models have difficulty generalizing when the number of training class instances are numerically imbalanced. the problem of generalization in the face of data imbalance has largely been attributed to the lack of training data for under-represented classes and to feature overlap. the typical remedy is to implement data augmentation for classes with fewer instances or to assign a higher cost to minority class prediction errors or to undersample the prevalent class. However, we show that one of the central causes of impaired generalization when learning with imbalanced data is the inherent manner in which ML models perform inference. these models have difficulty generalizing due to their heavy reliance on the magnitude of encoded signals. During inference, the models predict classes based on a combination of encoded signal magnitudes that linearly sum to the largest scalar. We demonstrate that even with aggressive data augmentation, which generally improves minority class prediction accuracy, parametric ML models still associate a class label with a limited number of feature combinations that sum to a prediction, which can affect generalization. © the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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the Performance Analysis of Several machine learning Methods in Predicting Diabetes 6

The Performance Analysis of Several Machine Learning Methods...

引用

6th IEEE international conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Wu, Yuanyuan Xia, Zhuomin Liu, Huizhou Huang, Mengxing Zhang, Yu Hainan University College of Information and Communication Engineering Haikou570228 China Hainan University College of Computer Science and Technology Haikou570228 China

ISBN: (纸本)9798350325485

Diabetes is a chronic disease characterized by hyperglycemia. Early screening of diabetes patients will help reduce the incidence of diabetes. Accurately modeling the early symptoms of diabetes to reduce the incidence of diabetes has become an urgent problem. therefore, it is a valuable research direction to mine and study the information of diabetes patients, predict the occurrence of noninfectious diseases, and assist doctors to make correct diagnoses. the precision of different data mining techniques has been compared in this research, employing machine learning techniques to predict the incidence of diabetes. the University of California, Irvine's open-source dataset has been used as the main database for experiment. Different machine learning classifiers such as random forest, neural network and support vector machine have been employed in this experiment. this research gives a technology review of machine learning algorithms that can be used for diabetes prediction through classification. the experimental results indicate that random forest performs better than other machine learning methods. © 2023 IEEE.

关键词： Decision support systems

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Improving Biomedical Claim Detection using Prompt learning Approaches 4

Improving Biomedical Claim Detection using Prompt Learning A...

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4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Chen, Tong Stefanidis, Angelos Jiang, Zhengyong Su, Jionglong Jiangsu Suzhou215123 China

ISBN: (纸本)9798350324303

Biomedical claim detection is an effective method to uncover negative effects arising from the treatment of disease and detect misinformation about medications from online platforms. Due to the power of pre-trained language models (PLMs), such as BERT, RoBERTa and T5, fine-tuned PLMs perform exceptionally well in biomedical claim detection. However, a gap exists in the text classification task between objective forms used in pre-training and fine-tuning for PLMs methods, preventing these models from taking full advantage of the information for biomedical claim detection. Motivated by the prompt learning approach, we propose a method, in which the classification task is transformed into a masked language modeling task that fully utilizes the mask learning capability of PLMs for better prediction of biomedical claim detection. In our method, a template with a mask representing the label is first constructed, and the mask is then filled and mapped to the corresponding label. We use three PLMs as backbone models, i.e., BERT, RoBERTa, and T5, with both hard and mixed templates which are fully and partially predefined templates. Experimental results using the BioClaim dataset demonstrate the superiority of the prompt learning methods over the BERT and RoBERTa classification baselines. Furthermore, the T5 model with mixed template consistently outperforms the rest of experimented models and achieves state-of-the-art performance with an increase of 5.3% on F1-score compared to previous research on this dataset. © 2023 IEEE.

关键词： learning systems

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A Survey of data mining Techniques for Digital Forensic Analysis 16

A Survey of Data Mining Techniques for Digital Forensic Anal...

引用

16th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th international conference on Emerging data and Industry 4.0, EDI40 2025

作者： Abdallah, Emad E. Elsoud, Esraa Abu Abdallah, Alaa E. Department of Information Technology Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology The Hashemite University P.O. Box 330127 Zarqa13133 Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology The Hashemite University P.O. Box 330127 Zarqa13133 Jordan

Analyzing digital devices to generate digital evidence relevant to incidents is essential in modern digital investigations. machine learning models and pattern recognition capabilities can be used in forensic analysis to uncover hidden evidence in digital objects that would have been missed if performed manually, especially given the exponential growth in textual sources and digital devices used in the forensic investigation process over the years. Text mining, a subset of data mining, has played an important role in developing effective digital forensic techniques. this survey provides a comprehensive overview of digital forensic analysis using data mining techniques. It presents the critical role of data mining in developing the digital forensics process and addressing the challenges posed by increasing data complexity. © 2025 Elsevier B.V.. All rights reserved.

关键词： Feature Selection

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Parkinson's Disease Detection Using data mining Models: A Comparative Study 4

Parkinson's Disease Detection Using Data Mining Models: A Co...

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4th international conference on data Analytics for Business and Industry, ICDABI 2023

作者： Majed, Reem Jamal Al-Heddi, Rashid Moahmmed Zeki, Ahmed M. University of Bahrain College of Science Sakhir Bahrain University of Bahrain College of Information Technology Department of Information System Sakhir Bahrain

ISBN: (纸本)9798350369786

data mining techniques have emerged in the healthcare industry over the past years in determining the occurrences and development of diseases. this paper aim is to conduct a comparative study between three data mining models in predicting Parkinson's Disease using biological voice measurements dataset. the three models that are evaluated are: Random Forest (RF), K-Nearest Neighbors (KNN) and Support Vector machine (SVM). data oversampling using Synthetic Minority Oversampling Technique (SMOTE) method was used to balance the data and feature selection was applied to select the most relevant attributes. the CRISP-DM framework is used to perform the analysis using Weka machine learning tool. the results showed that the KNN model obtained the best accuracy of 94.11% on balanced dataset. © 2023 IEEE.

关键词： Support vector machines

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Using EEG data to Classify Hand Motor Imagery in Stroke Patients 4

Using EEG Data to Classify Hand Motor Imagery in Stroke Pati...

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4th international conference on Electronic Information Engineering and Computer Technology, EIECT 2024

作者： Shen, Yiwei Liu, Junting Yang, Yuxuan Xie, Wenjun Hefei University of Technology School of Software Anhui Hefei China

ISBN: (纸本)9798331528850

EEG-driven brain-controlled prosthetics can greatly help people with movement disorders in their daily lives. Still, the task of accurately translating intentions into expected actions in the brain remains a technical challenge. In recent years, as a branch of machine learning, Deep Neural Networks (DNN) have made great progress in improving the recognition and classification ability of computer vision tasks. this study evaluate the information representation ability of 2 machine learning models and 16 NN models and their variants. the experiment is conducted on an open source EEG dataset of hemiplegic stroke patients, and we evaluate the thematic and cross-thematic performance of the above algorithm. We find that a single-layer GRU network remained an optimal choice in subject subject classification because it is able to effectively reduce model overfitting. For cross-subject analysis, some deep-learning models outperform all other methods due to the availability of training data and can function effectively in cross-subject decoding tasks. Independent Component Analysis (ICA) processing is also proved to be effective for improving the accuracy of classification. these improvements will provide important information for researchers in the field of Brain-computer interface (BCI) to select data processing strategies appropriate for their research tasks, it also lays a foundation for the future research on the interaction between the characteristics of bioelectric signals and the structure of neural networks. © 2024 IEEE.

关键词： Brain computer interface

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Role of Cyber Physical System in Insider Trading 4

Role of Cyber Physical System in Insider Trading

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Godse, Shreyasi Verma, Sandhya Pandey, Shweta Aluvala, Srinivas Negi, Prafful Sahu, Mansi Law College Dehradun Uttarancchal University Dehradun India School of Computer Science and Artificial Intelligence Sr University Warangal India Uttaranchal Institute of Technology Uttarancchal University Dehradun India School of Life and Applied Sciences Uttaranchal University Dehradun India

ISBN: (纸本)9798350300888

Illegal insider trading detection and enforcement of insider trading laws is a challenging task that attracts the attention of regulators due to its serious harm to confidence of investors and sustainable development of financial markets. Due to the finite availability of resources, it's still very difficult for the regulators to book the offenders into the act of doing it. the paper focuses on the significance of having advanced technology in this area while it discusses the recent studies on insider trading detection. the author explicitly discusses the challenges faced in detection of financial crime by applying machine learning and data mining techniques. the paper also examines several market surveillance strategies that are used to protect financial markets. the primary goal of the study is to provide a thorough review of the potential effects of data mining and machine learning on market data and how to use them to ensure the integrity of financial markets. Furthermore, the author identifies some of the key challenges that currently exist and proposes potential solutions, such as integrating new tools and techniques with existing mechanisms to detect insider trading. Finally, the paper concludes with some recommendations that could be implemented in practice. © 2023 IEEE.

关键词： data mining

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Deep learning based garbage recognition system 4

Deep learning based garbage recognition system

引用

4th international conference on machine learning and Computer Application, ICMLCA 2023

作者： Song, Zelong School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

the rapid increase in the amount of rubbish and the diversity of the substances in people's daily rubbish are two major problems of rubbish disposal at present, and the rubbish problem brings a serious burden to the environment as well as a waste of resources. At present, the main approach to deal with the waste problem is through waste separation. However, today’s traditional manual classification method is still used to screen the rubbish, which is labour-intensive with low sorting efficiency. this paper proposes a deep learning-based intelligent rubbish classification system, which can quickly achieve the positioning and recognition of rubbish in complex backgrounds for classification by incoming pictures and effectively improve the efficiency of rubbish classification. the system uses TensorFlow as the deep learning platform, and the combination of a convolutional neural network and a fully connected neural network is used to achieve the construction of the deep learning model. Meanwhile, Some nodes of the fully connected layer are discarded with a given probability in the training phase to avoid the overfitting of the system to optimise the system, and the system is also optimised by collecting various types of rubbish pictures and building inspection datasets, using data augmentation to improve the robustness of the model, the fast and accurate identification of different types of rubbish is achieved. the experimental results demonstrate that the intelligent rubbish classification system proposed in this paper has a high performance in terms of both accuracy and efficiency. © 2024 SPIE.

关键词： Efficiency

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