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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

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A Computer Network Security Intrusion Detection Algorithm Based on Deep learning 3

A Computer Network Security Intrusion Detection Algorithm Ba...

引用

3rd international conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

作者： Song, Xuan Song, Meng Li, Xiaomei Weifang Engineering Vocational College Qingzhou Shandong262500 China

ISBN: (纸本)9798350376173

The rapid advancement of network technology has led to a surge in network security concerns. Detecting various intrusion behaviors promptly and precisely has become a critical issue that demands urgent attention in network security. Traditional machine learning (ML) methods for intrusion detection, which rely on feature extraction and separation, often encounter challenges such as limited detection capabilities and high false positive rates when confronted with intricate and evolving network attacks. To address these challenges, this article introduces a deep learning (DL) based algorithm for computer network security intrusion detection. The aim is to enhance detection accuracy and minimize false alarms. This algorithm leverages the DL model to autonomously extract deep-level features from network traffic data and accurately recognize network intrusion behaviors through the construction of an efficient neural network architecture. Experimental results reveal that the proposed DL-based intrusion detection algorithm surpasses traditional ML detection methods in terms of accuracy and processing speed. This algorithm not only accurately identifies known attack behaviors but also effectively responds to unknown attack patterns, exhibiting remarkable generalization capabilities. © 2024 IEEE.

关键词： Network intrusion

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A Deep learning-Cryptography Hybrid Approach for Ensuring Medical Image Confidentiality 3

A Deep Learning-Cryptography Hybrid Approach for Ensuring Me...

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3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Chinta, Hitesh Surya Sai, Maganti Akshita Uma Kavitha, C.R. Amrita School of Computing Department of Computer Science & Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350369083

This paper addresses the important need for security measures in the transmission of medical images to ensure the confidentiality of sensitive patient information. Leveraging a synergistic combination of advanced cryptography and steganography techniques, including DNA-based AES encryption and LSB embedding, the proposed system ensures the secure hiding and transmission of patient data. Furthermore, Discrete Wavelet Transform compression is employed to optimize data size while preserving integrity and also applied deep learning models to the HAM10000 dataset. Experimental evaluations prove the superiority and efficacy of the suggested strategy in safeguarding medical data against unauthorized access and ensuring confidentiality. This work contributes significantly to the advancement of secure medical data transmission systems, addressing security concerns and regulatory requirements in the healthcare domain. © 2024 IEEE.

关键词： Steganography

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Enhancing Diagnostic Precision: Comparative Analysis of BERT and machine learning Algorithms for Multi-Disease Prediction from Symptom Narratives 3

Enhancing Diagnostic Precision: Comparative Analysis of BERT...

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3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Kumar, Y Udit Kumar, K Pradeep Vijayarani, J. Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

This study examines the prediction of diseases through the analysis of 1,200 symptom narratives across 24 different diseases. Employing Huggingface's 'bert-base-cased' transformer model, renowned for its ability in natural language understanding, this research study capitalizes on its extensive training on diverse English language corpora. Utilizing the BERT model's advanced capabilities in next sentence prediction and masked language modeling, the study aims to deduce probable diseases from intricate symptom descriptions. Additionally, the research rigorously evaluates established machine learning models such as AdaBoostClassifier, SVM, and RandomForest, juxtaposing their predictive efficacy against the more sophisticated BERT model. The overarching objective of this comparative analysis is to develop a comprehensive predictive model for multiple diseases, transcending conventional methods focused on individual disease detection. © 2024 IEEE.

关键词： Forecasting

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Email Spam Filteration with machine learning 3

Email Spam Filteration with Machine Learning

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3rd IEEE World conference on Applied Intelligence and Computing, AIC 2024

作者： Sajwan, Manmohan Singh Singh Mahar, Abhinav Rawat, Priyanshu Sharma, Khushi Das, Purushottam Graphic Era Hill University Computer Science Engineering Dehradun India R/S Graphic Era Deemed to Be University Dehradun India Graphic Era Hill University Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350384598

Email spam has become a significant contemporary issue, mainly propelled by the rapid expansion of internet users. Correspondingly, the incidence of email spam is on the rise. These unsolicited communications serve as vehicles for nefarious and unethical purposes, including phishing and fraudulent activities. Using sophisticated techniques spammers create malicious links capable of compromising system security and infiltrating personal data. Crafting counterfeit profiles and email accounts is effortlessly achieved by spammers, enabling them to masquerade as genuine entities in their deceptive communications. Especially vulnerable individuals are unaware of such schemes, thus emphasizing the critical need to identify and mitigate such deceptive emails. This project aims to detect fraud email spam through the application of various machine learning algorithms. Numerous machine learning algorithms such as Naïve Bayes, SVMs, KNN & Decision Trees were used with specific datasets for this project. Integration has been done for the selection of the most effective algorithm for email spam detection as per definitive metric standards. Thus, our key findings indicate that Multinomial Naïve Bayes Algorithm is most suitable for spam filtration. © 2024 IEEE.

关键词： Adversarial machine learning

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Unified Payment Interface Imposture and Scam Detection Using Deep learning And ANN 3

Unified Payment Interface Imposture and Scam Detection Using...

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3rd international conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Karthick, N. Leema Roselin, G. Tamilarasan, M. Kalaiselvi, K. Sudha, S. Jayanthi, J. IFET College of Engineering Department of Computer Science and Engineering Villupuram India

ISBN: (纸本)9798350391565

The Unified Payment Interface (UPI) has revolutionized digital payments in India, providing a convenient and secure way for individuals to transfer funds. However, the increasing popularity of UPI has also attracted malicious actors engaging in imposture and scams to defraud users. A novel approach is proposed to detect UPI imposture and scams using Deep learning and Artificial Neural Networks (ANN). This system leverages the power of deep learning techniques, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to analyze transaction data and user behavior patterns. A comprehensive dataset is collected and preprocessed, containing legitimate UPI transactions as well as historical data associated with known scams and imposture cases. By training the model on this dataset, it learns to identify suspicious patterns and anomalies in real-time transactions. © 2024 IEEE.

关键词： Convolutional neural networks

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Federated continual learning based on weight self-optimization algorithm in non-iid 3

Federated continual learning based on weight self-optimizati...

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3rd international conference on Electronic Information engineering and data Processing, EIEDP 2024

作者： Xu, Hao School of Software Henan University Kaifeng475001 China

ISBN: (纸本)9781510680531

Federated learning enables collaboration among parties without the need for external data sharing, achieving knowledge sharing without data sharing. However, existing federated learning algorithms still encounter issues such as non-IID (nonindependent and identically distributed) data and catastrophic forgetting, such as data transfer, statistical differences, and local update overfitting. In this paper, we propose a new federated learning algorithm, FCLW, which integrates continuous learning to address catastrophic forgetting and introduces a weight auto-allocation mechanism to solve the heterogeneity of federated learning data. Experimental results demonstrate that, compared to baseline algorithms, FCLW achieves faster convergence and obtains a more accurate global model in both IID and non-IID data settings. © 2024 SPIE.

关键词： data transfer

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Performance Evaluation of Solar Photovoltaic Generation Forecasting using machine learning Algorithms 3

Performance Evaluation of Solar Photovoltaic Generation Fore...

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3rd international conference on Power, Control and Computing Technologies, ICPC2T 2024

作者： Abdul Salam, Shereen Siddhara Petra, M.I. Azad, Abul K. Sulthan, Sheik Mohammed Rai, Veena Universiti Brunei Darussalam Faculty of Integrated Technologies Bandar Seri Begawan Brunei Universiti Teknologi Brunei Faculty of Engineering Bandar Seri Begawan Brunei

ISBN: (纸本)9798350349207

In recent years, there has been a rapid growth in the utilization of electricity generated from renewable energy sources. Solar energy stands out as a promising solution to the need for a sustainable and easily accessible power source. The integration of intermittent solar PV (photovoltaic) systems into the electricity grid can have a substantial impact on its stability. The implementation of dependable and accurate power forecasting is of utmost importance to enhance grid stability, optimize energy management, and maximize the economic advantages of photovoltaic (PV) power generation. To study the prediction capabilities, three distinct machine learning models, namely Support Vector machine (SVM), Random Forest (RF), and eXtreme Gradient Boost (XGBoost), were selected and subjected to refining and testing. The chosen models are briefly described. The photovoltaic (PV) power is computed by utilizing weather data for a span of one year, and subsequent generation forecasting is conducted based on the computed PV power. The evaluation of the performance of the chosen tools is conducted by assessing metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Normalized Root Mean Squared Error (NRMSE), and R-squared (R2) values. The findings of this study not only contribute to the improvement of the integration of solar energy into the power grid, but also offer useful insights into the selection of machine learning techniques for accurate and efficient forecasting of solar photovoltaic (PV) generation. © 2024 IEEE.

关键词： Support vector machines

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Progress in Cross Language Text Sentiment Analysis Based on Deep learning 3

Progress in Cross Language Text Sentiment Analysis Based on ...

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3rd international conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Xu, Na Wu, Yu Department of Foreign Languages Dalian University of Science and Technology Liaoning Dalian China

ISBN: (纸本)9798331519032

With the acceleration of globalization, cross-language information processing technology is getting more and more attention. Among them, cross-lingual text sentiment analysis, as an important branch in the field of sentiment computing, understands and analyzes the user's emotional state in a multilingual environment, which is of great significance for improving user experience and optimizing decision making. In recent years, cross-lingual sentiment analysis methods based on deep learning have made significant progress. This paper summarizes the recent research progress in this field. Firstly, we introduce the research background and challenges of cross-lingual text sentiment analysis, including language differences, domain differences, data scarcity and other issues. Then, we focus on the application of deep learning-based methods, such as cross-language word embedding, adversarial transfer learning, meta-learning and other techniques in cross-lingual sentiment analysis. It is found that these methods can effectively capture the semantic and emotional links between different languages and significantly improve the accuracy of cross-lingual sentiment analysis. This paper discusses future research directions in this area, including fusing multimodal information, enhancing the generalization ability of models, and combining deep learning techniques with traditional knowledge-based methods. © 2024 IEEE.

关键词： Adversarial machine learning

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E-Commerce Big data Analysis and User Behavior Prediction Algorithm Based on Deep learning 3

E-Commerce Big Data Analysis and User Behavior Prediction Al...

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3rd international conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

作者： Lv, Qianwen Fushun Vocational and Technical College Liaoning Fushun113122 China

ISBN: (纸本)9798350376173

With the vigorous growth of big data technology and intelligent interaction technology, e-commerce has become a widely concerned focus of the public. In the field of e-commerce, how to accurately predict shopping behavior and achieve personalized recommendations from massive user data has become a top priority of research. Big data analysis plays a crucial role in the field of e-commerce, as it can extract potential patterns from complex and ever-changing data, extract useful information, and provide decision support for businesses. Deep learning (DL), as an important branch of machine learning (ML), also shows great potential for application in the field of e-commerce. This article proposes a DL based e-commerce big data analysis and user behavior prediction algorithm. This algorithm accurately predicts the future shopping behavior of users by deeply mining potential features in user behavior data, combined with the powerful feature representation and learning ability of DL models. The experimental results show that the algorithm performs well in predicting user behavior, effectively improving the personalized recommendation effect and user experience of e-commerce platforms, and providing strong technical support for e-commerce enterprises. © 2024 IEEE.

关键词： Marketplaces

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Secure and Robust User Authentication Using Transfer learning and CTGAN-based Keystroke Dynamics 3

Secure and Robust User Authentication Using Transfer Learnin...

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3rd international conference on Computing and machine Intelligence (ICMI)

作者： AbdelRaouf, Hussien Fouda, Mostafa M. Ibrahem, Mohamed I. Augusta Univ Sch Comp & Cyber Sci Augusta GA 30912 USA Idaho State Univ Dept Elect & Comp Engn Pocatello ID 83209 USA Benha Univ Fac Engn Shoubra Dept Elect Engn Cairo Egypt

ISBN: (纸本)9798350372977;9798350372984

Due to keystroke dynamics's reliability and efficiency, it has been widely used in multi-factor authentication schemes by enterprises for robust user authentication, hence enhancing online service security. Therefore, in this paper, we propose a novel methodology designed not only to enhance user authentication but also to detect imposter users who are trying to get unauthorized access. Our methodology uses transfer learning and conditional tabular generative adversarial networks (CTGAN)-based keystroke dynamics, and consists of a four-step process;mitigating outliers that hinder the model's performance through utilizing quantile transformation (QT), employing data augmentation through CTGAN, converting CSV data into 3D images, and concatenating features from three accurate transfer learning models (VGG16, ResNet50, and DenseNet121) followed by dense layers to detecting imposter users accurately and hence making precise decisions to enhance the user authentication process. We conducted extensive experiments to evaluate our methodology using the Carnegie Mellon University (CMU) keystroke dynamics benchmark dataset that contains real typing patterns, and the results demonstrate superior security performance and robustness using our methodology by achieving an accuracy of 99.99% and equal error rate (EER) of 1%, thereby, outperforming the state-of-the-art.

关键词： User authentication keystroke dynamics conditional tabular generative adversarial networks (CTGAN)

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