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Detecting phishing attacks using Convolutional Neural Network and LSTM 3

Detecting phishing attacks using Convolutional Neural Networ...

引用

3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Jesmithaa, S. Eliyas, Sherin Hindustan Institute of Science and Technology Department of Computer Application Chennai India

ISBN: (纸本)9798350399264

Cybersecurity threats are increasing immensely due to the evolution of digitization. Due to this evolution, many security threats are happening around the world. One serious threat is phishing. It is a fraudulent activity that aims to steal a victim's private data using fraudulent emails and websites. Sensitive data such as login information, passwords, and bank account details are obtained and misused. In these types of attacks, the attackers appear to be legitimate. It is very hard to differentiate between what is real and phishing. A lot of research has been carried out to classify URLs as legitimate or related to phishing. However, a more efficient detection approach is needed to accurately predict and distinguish genuine sites from phishing sites. This issue can be solved by using deep learning algorithms such as convolutional neural networks (CNN) and long-term short-term memory (LSTM). This paper proposes a hybrid (CNN-LSTM) approach that provides maximum accuracy. © 2023 IEEE.

关键词： Cybersecurity

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A Survey of Complex Network Representation Learning Methods 23

A Survey of Complex Network Representation Learning Methods

引用

7th International Conference on High Performance Compilation, Computing and Communications, HP3C 2023

作者： Wang, Keke Zhu, Yu Huang, Jiangqiang Wang, Xiaoying Department of Computer Technology and Application Qinghai University Xining810000 China

ISBN: (纸本)9781450399883

Network representation learning is a technique that embeds the nodes in a network into a low-dimensional vector space, providing strong support for network analysis tasks such as node classification, link prediction, and community detection. This paper provides a summary of the research in the field of network representation learning in recent years. Firstly, the definition of network representation learning is introduced. Then, traditional network representation learning methods are presented, including matrix factorization-based methods, random walk-based methods, and neural network-based methods. However, traditional network representation learning methods can only be applicable to the traditional networks abstracted as the graphs and not applicable to the hypernetworks abstracted as the hypergraphs. Therefore, this paper also introduces hypernetwork representation learning methods, including homogeneous hypernetwork representation learning and heterogeneous hypernetwork representation learning. Finally, this paper summarizes future research directions in network representation learning. © 2023 ACM.

关键词： Vector spaces

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Heart Disease Prediction and Diagnosis Using IoT, ML, and Cloud Computing 6th

Heart Disease Prediction and Diagnosis Using IoT, ML, and Cl...

引用

6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Maurya, Jyoti Prakash, Shiva Department of Information Technology and Computer Application Madan Mohan Malaviya University of Technology Uttar Pradesh Gorakhpur273010 India

ISBN: (纸本)9789819940707

Heart disease is currently regarded as the main cause of illness. Regardless of age group, heart disease is a serious condition nowadays because most individuals are not aware of their kind and level of heart disease. In this fast-paced world, it is essential to be aware of the different types of cardiac problems and the routine disease monitoring process. As per the statistics from the World Health Organization, 17.5 million deaths are because of cardiovascular disease. Manual feature engineering, on the other hand, is difficult and generally requires the ability to choose the suitable technique. To resolve these issues, IoT, machine learning models and cloud techniques, are playing a significant role in the automatic disease prediction in medical field. SVM, Naive Bayes, Decision Tree, K-Nearest Neighbor, and Artificial Neural Network are some of the machine learning techniques used in the prediction of heart diseases. In this paper, we have described various research works, related heart disease dataset, and comparison and discussion of different machine learning models for prediction of heart disease and also described the research challenges, future scope and discussed the conclusion. The main goal of the paper is to review the latest and most relevant papers to identify the benefits, drawbacks, and research gaps in this field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Forecasting

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6G WIRTinX Subnetworks in context of ICN

6G WIRTinX Subnetworks in context of ICN

引用

2024 IEEE Calcutta Conference, CALCON 2024

作者： Saha, Kundan Kanti Ray, Sangram Dasgupta, Mou National Institute of Technology Sikkim Department of Computer Science and Engineering Sikkim Ravangla India National Institute of Technology Raipur Department of Computer Application Chhattisgarh Raipur India

ISBN: (纸本)9798331516666

This article provides a vision of combining Wireless Isochronous Real Time (WIRT) in-X Subnetworks with the Information Centric Networking framework. Here, the advantages of ICN over traditional IP-based networks are discussed in the context of WinXS implementation. A novel privacy preserving naming scheme along with an ICN packet structure and associated communication protocol are proposed for WinXS. The proposed scheme is tested on the AVISPA and NS3 simulation tools and it is found that the scheme satisfies the WinXS security and efficiency requirements, respectively © 2024 IEEE.

关键词： Internet protocols

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Enhancement of Farming Using IoT Based Unmanned Aerial Vehicles 2

Enhancement of Farming Using IoT Based Unmanned Aerial Vehic...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Tiwari, Vanshika Prakash, Shiva Madan Mohan Malaviya University of Technology Department of Information Technology and Computer Application Uttar Pradesh Gorakhpur273010 India

ISBN: (纸本)9798350371055

The agriculture sector faces the pressing challenge of increasing food production while optimizing resource use and minimizing environmental impact. One promising solution to address these challenges is the integration of Internet of Things (IoT) based Unmanned Aerial Vehicles (UAVs) into farming practices. This research paper explores the potential benefits, challenges and implications of using IoT-based UAVs in agriculture. It examines how this technology can enhance farming by providing real-time data, precision agriculture capabilities and improved resource management. There are detecting diseases in agriculture field to reduce background noise using the Median filters, Temporal filters. This helps to remove noise and vestiges caused by the movement of the drone or the target factory. © 2024 IEEE.

关键词： Image Processing Internet of Things (IoT) Median Filter Temporal Filter Unmanned Aerial Vehicles (UAVs)

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Fusion of IoT and Blockchain (BIoT): A Novel Architecture for Smart Agriculture 8th

Fusion of IoT and Blockchain (BIoT): A Novel Architecture...

引用

8th International Conference on Internet of Things and Connected Technologies, ICIoTCT 2023

作者： Das, Priyanka Ray, Sangram Dasgupta, Mou Govil, Mahesh Chandra Department of Computer Science and Engineering National Institute of Technology Sikkim Sikkim Ravangla737139 India Department of Computer Application National Institute of Technology Raipur Chhattisgarh Raipur492010 India

ISBN: (纸本)9789819757855

Internet of Things (IoT) plays a crucial role in automating systems, offering effectual and reliable services with minimal human intervention. To be precise, IoT-enabled smart agriculture is a vital factor in the modern digital world which ensures to provide various functionalities such as—water level monitoring, temperature monitoring, moisture sensing, and plant growth tracking. These functionalities of smart agriculture significantly simplify the work of farmers. But on the contrary, several security and privacy-related concerns were identified such as critical agriculture field data storage and management. Therefore, the evolution of integration of the IoT and blockchain (BIoT) came into the limelight. BIoT-based smart agriculture can enhance efficiency, sustainability, etc., leading to increased productivity, improved resource management, and secure data storing management in a decentralized manner. In this paper, we propose an architecture for a smart agriculture ecosystem based on the fusion of both IoT and blockchain. Primarily, our proposed architecture consists of three stages, PEGASIS-based chain construction phase, mutual authentication and key agreement phase, and blockchain-based data storage in base-station phase. Finally, this paper highlights on security analysis of our proposed architecture and found that it is well-secured against broadly known security attacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart agriculture

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Predictive Analytics for Future Air Pollution Levels Based on Population Growth 9th

Predictive Analytics for Future Air Pollution Levels Based o...

引用

9th International Conference on computer Science and Computational Intelligence, ICCSCI 2024

作者： Yeshe, Darel Wairooy, Irma Kartika Makalew, Brilly Andro Computer Science Department School of Computer Science Jakarta11480 Indonesia Mobile Application & Technology Program Computer Science Department School of Computer Science Jakarta11480 Indonesia

Air is very beneficial and crucial for every living creature on earth, hence it is very important to protect the air quality in order to avoid diseases. However, due to the increase in population, some human activities have been causing detrimental damage to the air quality. Some people have realized the potential impact of this problem and have done studies on predicting future air pollution. Some implemented machine learning models such as random forest regression, support vector machine, etc, while others have utilized deep learning models in their research.. This study will implement five different models, specifically linear regression, ridge regression, random forest regression, and multilayer perceptron regression. There will be two datasets used in this paper which will be merged and processed. There will be three key evaluation metrics being used in this paper namely root mean squared error, mean absolute error, and r-squared. The results from all of the models have concluded that more optimization and factors are needed in order to boost the final result of this study. © 2024 The Authors.

关键词： Adversarial machine learning

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Pneumonia Identification and Classification Using CNN Model Based on Chest X-Ray Image in Healthcare 7th

Pneumonia Identification and Classification Using CNN Model ...

引用

7th International Conference on Advanced Computing and Intelligent Engineering, ICACIE 2022

作者： Kumar, Suraj Prakash, Shiva Department of Information Technology and Computer Application Madan Mohan Malaviya University of Technology Uttar Pradesh Gorakhpur273010 India

ISBN: (纸本)9789819950140

Pneumonia is a bacterial, viral, or fungal disease within one or even both lungs. It is a serious disease where the air sacs in both lungs fill up with pus as well as other substances. Many architectures and models were developed to evaluate such diseases, but still, the possibility to further enhancement. We built a CNN model to identify and categorise Pneumonia disease in the lungs using CXR images. The dataset was developed and pre-processed by the Kaggle CXR dataset. In the research, the convolutional neural network method has been used in conjunction with other data augmentation approaches to improve classification accuracy which will help to enhance the training and validation accuracies, as well as characterisation of the precision of the CNN model and achieve various results. Our model's training accuracy and validation accuracy are 0.9784 and 0.9511, respectively, and its training loss and validation loss are 0.0634 and 0.1476. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Development of educational game as a media to raise public awareness of illegal animal trafficking issue in Indonesia 9th

Development of educational game as a media to raise public a...

引用

9th International Conference on computer Science and Computational Intelligence, ICCSCI 2024

作者： William, Jonathan Ahmadi, Khalid Fajar, Muhamad Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The amount of illegal wildlife trade cases increases every year in Indonesia. This causes some unique Indonesian wildlife to become endangered or even extinct. Therefore, the purpose of this research is to raise public awareness to illegal wildlife trade issue by using a game as its media to reduce the number of cases of illegal wildlife trade in Indonesia. The game made in this research will be developed using the GDLC development method and 2021.3.11f1 version of Unity as its game engine. To evaluate the game that has been developed in this research, a comparation to other related games is conducted and resulted in the game that has been developed in this research is significantly better than other related games in Challenge, Learnability and Concentration aspects (p © 2024 The Authors.

关键词： Invertebrates

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Player evaluation and learning outcomes of "NusaVenturas": A serious game for enhancing player knowledge and interest in Indonesian traditional foods 9th

Player evaluation and learning outcomes of "NusaVenturas": A...

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9th International Conference on computer Science and Computational Intelligence, ICCSCI 2024

作者： Sanjaya, Vito Sebastian Tanoto, Nicholas Valenthinus Tandinata, Nicholas Ramdhan, Dimas Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The popularity of Indonesian traditional food has been declining due to the increasing influence of foreign cuisine. This study presents the development method that combining Game Development Life Cycle (GDLC) with Experiential Game Learning Design Framework and evaluation of a serious game "NusaVenturas" using usability, learning outcome, and Game Experience Questionnaire (GEQ) that designed to introduce, promote interest, educate, and preserve knowledge of Indonesian traditional foods. "NusaVenturas" is a unique 2D serious game that integrates puzzle-platformer elements with time-management cooking mechanics. The study involved 37 participants aged 18 to 23. Evaluation results indicate that "NusaVenturas" significantly enhances players' knowledge of Indonesian traditional foods, with statistical analysis confirming the significance of the findings. © 2024 The Authors.

关键词： Food ingredients

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