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data mining for Fraud Detection in Telecommunications: Detecting Anomalous Behaviors in Real-Time 3

Data Mining for Fraud Detection in Telecommunications: Detec...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Mishra, Nidhi Shivaji, Ghorpade Bipin Kalinga University Department of Cs & It Raipur India

ISBN: (纸本)9798331542375

In the recent past, telecom experts have been confronted with so many problems emanating from fraudulent deeds that apart from causing loss of money discredits organizations. To mitigate these problems, real-time fraud detection systems are very essential in detected those unusual and unauthorized activities. This study aims at examining how data mining can be used in the identification of the actual fraud in the telecommunication area through users' interactions in the course of which a huge amount of data is produced. In this study, the case uses different machine learning algorithms including clustering, decision trees, and anomaly detection for mapping out unusual call pattern, unauthorized access and Identity theft. To capture anomalous behavior, the research uses both time-domain and frequency domain data from different telecom networks for training and testing models. The research also intends to extend existing theories and practices of Naive Bayes, Support Vector machines (SVM), and deep learning networks to improve accuracy and effectiveness of the sort of systems used in the detection of fraud. The performance of these models is measured in terms of accuracy in classification and prediction of fraudulent actions thereby reducing potentials false negative and false positive cases. Further, the paper outlines how these models can be integrated into existing network security architecture to assess the prospects for scalability of these solutions within a maturing fraud environment. The findings also affirm that data mining is effective in real-time monitoring and prevention of frauds as such highlighting the importance of frequent updates of the fraud detection solutions within the telecommunication sector. © 2025 IEEE.

关键词： Anomalous data mining Fraud Detection SVM Telecommunication

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3rd international conference on Computing and Communication Networks, ICCCN 2023

3rd International Conference on Computing and Communication ...

引用

3rd international conference on Computing and Communication Networks, ICCCN 2023

ISBN: (纸本)9789819726707

The proceedings contain 60 papers. The special focus in this conference is on Computing and Communication Networks. The topics include: Enhancing Security in Wireless Sensor Networks: A Broadcast/Multicast Authentication Framework with Identity-Based Signature Schemes;TSFTO: A Two-Stage Fuzzy-Based Tasks Orchestration Algorithm for Edge and Fog Computing Environments;tracking Climatic Variations Through Smart IoT-Driven Approach: An Exploratory Analysis;Design and Manufacturing of an Efficient Low-Cost Fire Surveillance System Based on GSM Communications and IoT Technology;a Comparative Analysis of Optimized Routing Protocols for High-Performance Mobile Ad Hoc Networks;integrating Sandpiper Optimization Algorithm and Secure Aware Routing Protocol for Efficient Cluster Head Selection in Wireless Sensor Networks;the Effects of Weather Conditions on data Transmission in Free Space for Wireless Sensor Networks;Using Wireless Sensor Network Performance and Optimizing in Underground Mines with Virtual MIMO Antenna;a Novel Logistic Regression-Based Fire Detection Model Using IoT in Underground Coal Mines;An MQTT IoT Intrusion Detection System Using Deep-learning;multi-objective Optimal Feature Selection for Cyber Security Integrated with Deep learning;application of Gradient Boosting Classifier-Based Computational Intelligence to Detect Drug Addiction Threat in Society;insurance 4.0: Smart Insurance Technologies and Challenges Ahead;review on Vision Transformer for Satellite Image Classification;primitive Roots and Euler Totient Function-Based Progressive Visual Cryptography;approach for Fire Detection Using Image Processing;review of Various Approaches for Authorship Identification in Digital Forensics;machine learning Model for Traffic Prediction and pattern Extraction in High-Speed Optical Networks;improved Markov Decision Process in Wireless Sensor Network for Optimal Energy Consumption.

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ML-Based AI Conceptual Framework for Assessing SMEs Digitalization Through the Lens of Sentiment Analysis 3

ML-Based AI Conceptual Framework for Assessing SMEs Digitali...

引用

3rd international conference on Digitalization and Management Innovation, DMI 2024

作者： Pelova, Boryana Hristova, Gloria Faculty of Economics and Business Administration Sofia University "St. Kliment Ohridski" Bulgaria

ISBN: (纸本)9781643685823

Tools from the machine learning and data mining domain become even more popular in the fields of economics, entrepreneurship, and policy-making. At the same time, the research on small and medium-sized enterprises (SMEs) is getting amplifying importance for governments and policy-makers especially when it comes to support of SME’s digitalization. A good understanding of the current level of digitalization of SMEs by industries is a prerequisite for design and integration of effective national policies. The goal of this paper is to design the architecture of an ML-based AI conceptual framework for assessing SMEs digitalization. We do this from the perspective of customers assuming that their preferences are absorbed in the publicly available (online) data that they generate in social media and community forums. This approach forms a significant contribution of this paper. Furthermore, we define an algorithm for data preparation, and we develop an algorithm based on sentiment analysis, which generates a set of industry-specific digitalization indices, which is another important contribution of this paper. © 2025 The Authors.

关键词： Adversarial machine learning

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A data-Driven Retrospective Analysis of Cyberbullying Using machine learning Algorithms 3

A Data-Driven Retrospective Analysis of Cyberbullying Using ...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Tripathi, Ashirwad R. Tyagi, Palki Sivaraman, Himani Kumar, Anuj Jain, Sachin Jigyasa University Department of Cs&it Dhradun India

ISBN: (纸本)9798331542375

Cyberbullying has become one of the main concerns in today's digital world, posing a great risk to everyone being online and effecting their mental health and their online safety. This research explores the application of machine learning using the machine learning algorithm to detect and reduce the cyberbullying behaviours. Here we utilize the data from the dataset of cyberbullying done by people online, preprocessing the data to clean and structure it for analysis. Here we use various machine learning models like Naive Bayes, Logistic Regression and SVM, which are then tested on dataset for accuracy, precision, recall, and overall effectiveness in detecting the harmful words and pattern. Our result highlight that which of the model that we used performed best contributing to detection of Cyberbullying online. Additionally, the research underscores the importance of integrating NPL(Natural Language Processing) for delicate text analysis and sentiment detection. The insights from this research aim to support the development of machine learning models for monitoring and addressing online harassment, making a safer digital environment. © 2025 IEEE.

关键词： Sentiment analysis

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Power data Security Defense Technology Based on Behavior Profiling and Its Application in Cloud Environment 3rd

Power Data Security Defense Technology Based on Behavior Pro...

引用

3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Ren, Qingqing Li, Haosheng Tian, Haomiao Feng, Haonan Cao, Shu Xinjiang Uygur Autonomous Region State Grid Xinjiang Electric Power Co. Ltd. Information and Communication Company Urumqi830000 China

ISBN: (纸本)9789819616978

This article focuses on the security challenges faced by power data in cloud environments, particularly the risk of data leakage caused by abnormal user behavior. The study proposes an innovative power data security defense technology based on behavioral profiling. This technology constructs a user behavior model through fuzzy C-means clustering algorithm, analyzes real-time access and operation characteristics of power data, forms accurate behavior portraits, and uses machine learning algorithms to identify abnormal behavior patterns. The experimental results show that this technology has extremely high accuracy in detecting normal and abnormal electricity usage behavior, with most data points having an accuracy rate of over 90%, and some even reaching 100%, effectively distinguishing between normal and abnormal behavior. The research conclusion shows that the defense strategy based on behavioral profiling provides a new perspective and efficient means for the security protection of power data, significantly improving the security protection level of power data in cloud environments, and building a solid defense line for the digital transformation of the power industry and the safe operation of smart grids. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Information leakage

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Malicious Traffic Classification Algorithm Based on Multimodal Fusion 3rd

Malicious Traffic Classification Algorithm Based on Multimod...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Li, Xinhang He, Mingshu International School Beijing University of Posts and Telecommunications Beijing China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789819616930

The application of deep learning in classifying malicious network traffic is prevalent. However, our paper highlights two frequently neglected issues in current methodologies: first, network traffic data need to be truncated and zero-filled to adapt to the training process of deep learning, which brings a certain degree of information loss;second, individual packets of network traffic will show different importance in the classification task due to their own data quality and location. To solve these two problems, a multimodal attention network is proposed to mitigate the information loss by fusing data from two different modalities in the network traffic and solving the imbalance of data from different modalities in the training process using a regularization method. Additionally, we incorporate a dual attention module to assess the significance of each packet, enabling the model to prioritize and emphasize the most crucial ones. To evaluate the model’s effectiveness, we test it on three openly accessible datasets: UNSW-NB15, ISCXIDS2012, and CIC-DoS2017. Our findings reveal that the model presented herein surpasses several conventional deep learning approaches in terms of classification outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： deep learning malicious behavior multimodal network traffic classification

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data Quality-Driven Top-k Rule Discovery 3rd

Data Quality-Driven Top-k Rule Discovery

引用

3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Han, Ziyan Chen, Wanjia Han, Yunpeng School of Computer Science and Engineering Beihang University Beijing China School of Mathematical Sciences Shenzhen University Shenzhen China School of Electrical Engineering and Computer Science Louisiana State University Baton Rouge United States

ISBN: (纸本)9789819616978

Existing works on rule discovery often yield an overwhelming number of rules with a high computational cost. Moreover, few studies consider the impact of data quality on the reliability of rules produced. This paper studies the top-k rule discovery problem that returns a set of top-k reliable rules. We introduce a novel scoring function that incorporates data quality, providing a more comprehensive and fine-grained assessment of rules. Based on this scoring function, we develop an efficient algorithm for discovering top-k high-quality rules in relational data. To enhance the performance of our algorithm, we propose effective pruning and optimization strategies that significantly reduce the search space and improve computational efficiency;additionally, we parallelize it for further acceleration. Through extensive experiments on real-life datasets, we verify the effectiveness and efficiency of the proposed method, confirming its practical utility in data quality-driven top-k rule discovery. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： data reliability

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Revolutionizing Legal Intelligence: Advances in Neural Networks and Language Models for Legal NLP 3

Revolutionizing Legal Intelligence: Advances in Neural Netwo...

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3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Noronha, Raoul Samuel Alenchery, Alex Stanley Deepa, S. Jayapriya, J. Vinay, M. Christ University Department of Computer Science Bengaluru India

ISBN: (纸本)9798331523893

As the legal field continues to generate vast amounts of complex text, from contracts to court rulings, machine learning and natural language processing (NLP) techniques have emerged as valuable tools to help analyze and organize this data. In this paper, a number of state-of-the-art models will be reviewed and evaluated, including transformer models like BERT, GPT, and T5, and neural network models such as LSTM and CNN-RNN hybrids. These were then tested for the legal tasks of document classification, text summarization, and entity recognition. Some of the metrics used for evaluation include Accuracy, F1-Score, ROUGE, and BLEU. Advanced models, in particular large language models (LLMs), outperform the traditional methods by a large margin since they capture the niceties of legal language and structure much more completely. Meanwhile, high-quality legal datasets remain scarce, legalese remains incomprehensible to most, and the models are still relatively unexplainable. In sum, these challenges clearly call for future research in terms of data augmentation, explainable AI techniques, and more robust training methods that would allow AI-powered tools to be integrated much more effectively within lawyers' workflows to support them in their decision-making processes. © 2025 IEEE.

关键词： BERT Contract Clause Extraction GPT Large Language Models (LLMs) Legal Document Classification Legal NLP Legal Text Summarization machine learning Named Entity recognition (NER) Neural Networks Transformer Models

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Research on Medical Image Classification for Oral Cancer 3rd

Research on Medical Image Classification for Oral Cancer

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Wang, Kunkun Shi, Linjun Gu, Chaochen Mo, Fan Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Department of Oral Mucosal Diseases Ninth People’s Hospital Shanghai Jiaotong University School of Medicine Shanghai China Shanghai Key Laboratory of Stomatology and Shanghai Research Institution of Stomotology National Clinical Research Center of Stomatology Shanghai China

ISBN: (纸本)9789819624676

In recent years, deep learning has made significant progress in medical imaging, deepening the crossover between the medical and industrial fields. However, not all medical images are suitable for deep learning neural networks;simultaneously, some medical images are difficult to collect and preprocess. In this paper, we reconstruct the image information collected by dental clinicians over many years, normalizing it with diagnostic results as the gold standard. On this basis, we explore the applicability and potential of deep learning on complex medical images. Further, we compare the performance of multiple models on this dataset and visualize the learning outcomes, demonstrating that deep learning networks exhibit good learning capabilities on certain images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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Optimized Management of English Distance Education database Based on WEB Content Recommendation Algorithm 3

Optimized Management of English Distance Education Database ...

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3rd international conference on Artificial Intelligence and Education, ICAIE 2024

作者： Fan, Xuefei Feng, Jingwen BinHai School of Foreign Affairs Tianjin Foreign Studies University Tianjin China Qiqihar University Heilongjiang Qiqihar China

ISBN: (纸本)9798400712692

Current age with rapid developments in network technology has led to rapid changes in distance education, which has now begun to take center stage in the academic sector. Nevertheless, the gap between the wide availability of online resources and personalized, individual, differentiated, and multi-faceted learning needs has come to light. This fact, however, increasingly shows the inadequacy of conventional approaches to satisfactorily meet the multifaceted, various, and constantly evolving needs of users. To this effect, the academicians have been assiduously engaged in searching for newer approaches based on individualized web-published content recommendation algorithms to improve the English distance education framework. Emerging as one of the best learning systems mainly by blending advanced data-mining methods with cutting-edge machine-learning technologies, these algorithms can specifically provide resources according to the particular interests and level of proficiency of learners while raising overall educational efficiency and quality of instruction. Besides this, database query optimization algorithms play an especially important role in this model. By availing efficient data management and timely, swift queries that are clean and effective, this new technology boosts the overall system operational efficiency tremendously. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： English distance learning modeling diverse constraints optimal database management Recommendation algorithms

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