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Fortifying Email Security: A Machine learning Approach 15

Fortifying Email Security: A Machine Learning Approach

引用

15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Begum, Shaik Salma Pavani, Ananta Naidu, Gedela Narasimha Sandeep, Gonela Pavan, Devu Seshadri Rao Gudlavalleru Engineering College CSE Gudlavalleru India

ISBN: (纸本)9798331300579

In today's communication landscape, emails are essential for both personal and business interactions, serving as a common method to transmit sensitive information, including financial data, credit reports, and login ***, the time delay between sending and receiving emails provides an opportunity for cybercriminals to exploit vulnerabilities and compromise data integrity. Phishing, a frequently used malicious technique, involves impersonating reputable entities to acquire confidential information from unsuspecting individuals. To counter the growing phishing threat, this project focuses on utilizing machine learning (ML) algorithms. Through dataset segmentation, model training, and validation, this project demonstrates the effectiveness of our approach. Our systematic comparison of different datasets and features reveals that incorporating more features enhances accuracy and efficiency, strengthening our efforts to combat the widespread threat of phishing attacks. © Grenze Scientific Society, 2024.

关键词： Phishing

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Parity-Based Amplitude Embedding for Rational Quantum Dataset Preprocessing 1

Parity-Based Amplitude Embedding for Rational Quantum Datase...

引用

1st international conference on Quantum Communications, Networking, and computing, QCNC 2024

作者： Prasetiadi, Agi Mambo, Masahiro Kanazawa University Graduate School of Natural Science and Technology Kanazawa Japan Kanazawa University Institute of Science and Engineering Kanazawa Japan

ISBN: (纸本)9798350366778

Quantum dataset preprocessing for amplitude embedding is understudied despite its importance for achieving good generalization in quantum machine learning models. Amplitude embedding, known for its compression capabilities, has garnered attention in recent research. Combined with the Variational Quantum Classifier, they form a potential combination for high performance in quantum machine learning, outperforming many classical models. However, the full normalization of amplitude embedding has fundamental issues, such as inconvertibility for specific values and irrational amplitude distribution due to normalization. To overcome these challenges, we propose a novel and straightforward dataset preprocessing solution using guardian parity designed for amplitude embedding. This method ensures the normalized dataset remains recoverable to its original MinMax scaled values, simultaneously enhancing dataset quality and improving accuracy when employed in quantum machine learning models. Experimental results show that the proposed parity-based amplitude embedding significantly impacts the accuracy of machine learning models compared to the original amplitude embedding method. © 2024 IEEE.

关键词： Quantum computers

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Image-Based Cognitive States Identification through Deep learning 2

Image-Based Cognitive States Identification through Deep Lea...

引用

2nd IEEE international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Kundan Rao, S. Ambannavar, Siddarth Sinha, G.R. Bms College of Engineering Dept. of Machine Learning Bangalore560019 India Gfsc University Gujarat Vadodara India

ISBN: (纸本)9798350306415

Yoga and mindfulness practices offer a variety of benefits, promoting both physical and mental well-being. This research intersects with AI, psychology, neuroscience, and more within cognitive science, contributing to the study of the brain, mind, memory, and disorders. The study presents a novel approach utilizing deep learning and image classification techniques to distinguish practitioners from non-practitioners of yoga based on pre-processed data. Data pre-processing involves down sampling, filtering, and Independent Component Analysis (ICA) to enhance the extraction of relevant features. The pre-processed data is transformed into images using Gramian Angular Field, facilitating the utilization of Convolutional Neural Network (CNN) models. The proposed methodology leverages custom-built CNN models to classify individuals into their respective groups. The models are trained on the image representations of the pre-processed data, enabling the extraction of intricate patterns and features for accurate classification. © 2024 IEEE.

关键词： Deep learning EEG Gramian Angular Field (GAF) Image Classification Mindfulness

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Enhancing EEG Analysis for Rapid Brain Activities Detection in Patients 1

Enhancing EEG Analysis for Rapid Brain Activities Detection ...

引用

1st international conference for Women in computing, InCoWoCo 2024

作者： Yadav, Abhivyakti Ahmed, R Kaif Annamalai, R. Mercy, S.Sudha CSE Amrita School of Computing Chennai India R.M.K. Engineering College Department of Computer Science and Design Chennai India

ISBN: (纸本)9798331518943

Electroencephalogram (EEG) analysis plays a critical role in diagnosing various neurological conditions by detecting abnormal brain activities. However, the complex nature of EEG signals poses challenges for traditional analysis methods, which often struggle to handle diverse and high-dimensional data effectively. This paper develops a new technique of EEG signal classification using machine learning and deep learning models. Specifically, attention has been given to the CatBoost, ResNet34D, EfficientNetB0, and EfficientNetB2 models. The proposed approach utilized a vast EEG dataset with 11,000 samples of spectrograms, and it conducted an in-depth comparison of these models. Data preprocessing included features developed with statistical measures and the formation of uniform EEG spectrograms. All these models were evaluated in terms of their KL divergence score, defined as a measure of the divergence between the predicted and target distributions. From all the models considered, CatBoost proved to be one with an excellent performance, having had a KL score value at 0.78 compared to that of deep learning models. This work demonstrates the potential of gradient boosting for EEG-activity classification tasks and can be of benefit as a source of insight for interested applications in real-time brain activity monitoring and diagnostics of neurological issues. © 2024 IEEE.

关键词： Electroencephalography

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Fraud Detection In Ethereum Transactions: A Machine learning Approach 1

Fraud Detection In Ethereum Transactions: A Machine Learning...

引用

1st international conference on Advanced computing and Emerging Technologies, ACET 2024

作者： Neogi, Anandarupa Mukhopadhyay, Disha Jaiswal, Anubhav Kumar, Ankush Misra, Bitan Techno International New Town Computer Science and Engineering Kolkata700156 India

ISBN: (纸本)9798350367720

This study explored the use of machine learning models to detect fraudulent transactions made over Ethereum, a type of cryptocurrency. The dataset included data from 9840 transactions which included both fraudulent and legitimate transactions and included various parameters such as average, minimum and maximum amounts received and sent, the number of unique addresses to and from which ether was sent and received, the time duration between the transactions and the remaining balance after the transactions. The transaction amount lies in a range of 12000.0 to 52000.0. Four machine learning models, namely Decision Tree, Random Forest Classifier, Catboost Classifier and XGBoost Classifier, are evaluated for fraud transaction detection. XGBoost has emerged as the most promising model, achieving near perfect accuracy (99.65%) and high precision, recall and F1-scores. Notably the robust performance of XGBoost suggests it's potential for accurately detecting fraudulent transactions in Ethereum. However, comparatively weaker performance is observed in models like the Decision Tree and Random Forest. This study underscores the significance of machine learning in enhancing fraud detection in Ethereum transactions and advancement of the field of cybersecurity in blockchain technologies by providing a useful and scalable fraud-detecting instrument. © 2024 IEEE.

关键词： Ethereum

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Extraction Method of Disturbance Elements of Urban Emergencies Based on Large Language Model

Extraction Method of Disturbance Elements of Urban Emergenci...

引用

2024 international conference on Intelligent computing and Data Mining, ICDM 2024

作者： Xu, Jian Dang, Anrong Chen, Yang Zhao, Mingyu Sun, Bijiao Wang, Hua Xie, Zhuokun Tsinghua University School of Architecture Beijing China State Key Laboratory of Geo-Information Engineering Xian China

ISBN: (纸本)9798331506261

The general knowledge extraction model lacks the knowledge learning of such as urban emergencies, and the precision of knowledge extraction is insufficient. In this paper, a large language model (LLM) based method for urban disturbance elements extraction (UDEE-LLM) is proposed. The method deploys the local large language model through LangChain and integrates the domain knowledge of urban disturbance, fine-tuning the large language model based on the prompting learning, designing the heterogeneous entity alignment strategy, and optimizing the entity extraction results. The results show that UDEE-LLM can extract and classify the information of urban disturbance elements. The coupling relationship of urban disturbance elements is analyzed, and the potential relationship between mode, object and influence of urban disturbance is dug out, which has important reference for improving urban resilience. © 2024 IEEE.

关键词： Digital elevation model

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Research on computing Offloading Methods Based on Edge computing and Reinforcement learning in The Industrial Internet of Things

Research on Computing Offloading Methods Based on Edge Compu...

引用

international conference on Internet of Things and Machine learning, IoTML 2023

作者： Wang, Xian Wan, Qiang Li, JianCheng Faculty of Intelligent Manufacturing Wuyi University Jiangmen529020 China School ofComputer Science and Engineering South China University of Technology Guangdong GuangZhou510000 China School of Computer Science and Technology Guangdong University of Technology GuangZhou528300 China

ISBN: (纸本)9781510671805

Most of the existing research focuses on a single MEC server scenario, and there are few studies on computing offloading and resource allocation in multi-MEC server scenarios. Therefore, this paper proposes a dynamic computation offloading and resource allocation algorithm based on hybrid decision-making deep reinforcement learning, which utilizes deep deterministic policy gradients and dueling dual deep Q-networks to enhance the Actor-Critic structure. By calling the Actor part of DDPG to deal with continuous power allocation, and then combining the Critc part of DDPG with D3QN, the discrete MID is associated with the MEC server to solve the mixed decision-making problem in multi-MEC server multi-user scenarios. From the simulation results, we can see that the proposed algorithm has faster convergence and better stability than baseline algorithms such as DQN. © 2023 SPIE.

关键词： Reinforcement learning

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Split and Federated learning with Mobility in Vehicular Edge computing 21

Split and Federated Learning with Mobility in Vehicular Edge...

引用

21st IEEE/ACIS international conference on Software engineering Research, Management and Applications, SERA 2023

作者： Moon, Sungwon Lim, Yujin Sookmyung Women's University Dept. of IT Engineering Seoul04310 Korea Republic of

ISBN: (纸本)9798350345889

Vehicular edge computing (VEC) is a promising technology to support vehicular applications that leverage machine learning (ML) technology. Due to limited resources of the vehicle, the vehicle uses Split learning (SL) to split the computation of the ML model and offload it to the VEC server (VECS). Federated learning (FL) is also used for data privacy and parallel training of the vehicles. Therefore, SplitFed learning, which combines SL and FL, enables parallel processing, which is an advantage of FL, and reduces the computational burden on the vehicle through ML model split, which is an advantage of SL. However, the SplitFed learning does not consider the mobility of device/vehicle. Therefore, we propose a SplitFed learning with mobility method to minimize the training time of the model. SplitFed learning with mobility method is a migration method of the ML model when the vehicle moves from the current serving VECS to the target VECS. Through simulations, compared with conventional SplitFed learning where the vehicle travels after 50% and 80% of training is completed, the proposed method can reduce training time by about 19-33% for LeNet and by about 22-44% for VGG16, respectively, and does not degrade accuracy of model. © 2023 IEEE.

关键词： Edge computing

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Integrating Synthetic Data Modelling into an Adaptive Sampling Framework for IoT Devices 17

Integrating Synthetic Data Modelling into an Adaptive Sampli...

引用

IEEE Congress on Cybermatics: 17th IEEE international conference on Internet of Things, iThings 2024, 20th IEEE international conference on Green computing and Communications, GreenCom 2024, 17th IEEE international conference on Cyber, Physical and Social computing, CPSCom 2024, 10th IEEE international conference on Smart Data, SmartData 2024

作者： Alawad, Faiga Herrmann, Peter Thambawita, Vajira Trondheim Norway Simula Metropolitan Center for Digital Engineering Oslo Norway

ISBN: (纸本)9798350351637

Wireless Sensor Networks (WSNs) often rely on constrained devices with limited processor and power resources. Therefore, many approaches in the literature have studied data reduction methods to relieve these resources. Static scheduling algorithms are state of the art, but novel adaptive scheduling methods gained momentum in recent years as well. In the study presented in this paper, we combine enhanced adaptive scheduling algorithms with synthetic data, created using a generative machine learning model. This allows us to replace some of the sensed data points by generated ones. In consequence, we can increase the time interval between collecting two samples, which alleviates the power resources of the sensors significantly. Utilizing working environment noise data sensed in Trondheim, Norway, we analyzed how generative machine learning models can enhance data collection in resource-constrained wireless sensor networks. The results of this analysis reveal that using synthetic data to reduce the sampling rate by over 50% greatly improves data reduction efficiency, outperforming traditional adaptive sampling frameworks. To the best of our knowledge, our approach constitutes the first effort to leverage generative models for crafting adaptive sensing policies in WSNs. © 2024 IEEE.

关键词： Generative adversarial networks

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Convolutional Neural Network-based image tamper detection with Error Level Analysis 2

Convolutional Neural Network-based image tamper detection wi...

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2nd IEEE international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Manjunatha, S. Swetha, M.D. Rashmi, S. Subramanian, Ananda Kumar Vinoth Kumar, V. Mallikarjuna Swamy, S. Global Academy of Technology Department of Information Science and Engineering Bengaluru India BNM Institute of Technology Department of Computer Science and Engineering Bengaluru India Bengaluru India Vit - University School of Computer Science Engineering Dept of Computational Intelligence Vellore India Vit - University School of Computer Science Engineering and Information Systems Vellore India Jss Academy of Technical Education Dept of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350306415

Photography is the most important, powerful, and reliable means of expression. Today, digital images not only provide disinformation but also act as agents for secret communication. Users and editing professionals work with digital images for a variety of purposes. Images are often regarded as facts or proof of reality, so they are misleading and fake news or publications of any form that use images manipulated in a highly misleading way. To recognize image tampering needs multiple image data and a model that can handle all the pixels in the image. Furthermore, training the data more efficiently and needed flexibility support everyday use. Models based on Deep learning such as Convolutional Neural Networks with error level analysis (ELA) are the perfect solution. © 2024 IEEE.

关键词： Convolutional Neural Network Deep learning Efficiency Error Level Analysis Image Forgery Detection

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