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Federated Learning-Based Network Intrusion Detection Using Deep Auto Encoder 6

Federated Learning-Based Network Intrusion Detection Using D...

引用

6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Jeyavim Sherin, R.C. Parkavi, K. Vit University School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350394702

The progress of information technology is directly related to different elements of daily life, providing people with services that make living more comfortable. As the network infrastructure grows to support new services, it necessarily produces various weak points that cybercriminals might exploit. Building a reliable network intrusion detection system with deep learning methodologies demands a substantial dataset. In the past, the conventional approach involved collecting data for centralized learning, which was then transmitted to a central server for model training. Nevertheless, this approach raises valid concerns regarding the potential exposure of personal information contained within raw data, which could lead to legal repercussions for data providers. Consequently, this study presents a Modified FedAvg algorithm to enhance the existing FedAvg algorithm for network intrusion detection models. This modified approach maximizes the potential of federated learning, enhancing data privacy and minimizing the transmission of model weights. Employing a Deep Auto Encoder within a federated setting, we evaluated our methodology using the CICIDDoS 2019 dataset, attaining a notable accuracy rate of 98.82% in effectively identifying intrusions. © 2023 IEEE.

关键词： Network security

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Malicious URLs and QR Code Classification Using Machine Learning and Deep Learning Techniques 3

Malicious URLs and QR Code Classification Using Machine Lear...

引用

3rd IEEE Asian Conference on Innovation in technology, ASIANCON 2023

作者： Do Rosario Lourenco, Delano Oscar Sai Sriraj, M.V.H. Thambi, Krotha Kayne Ranjan, Vibho School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350302288

Attackers commonly employ malicious URLs and harmful QR codes to spread malware and phishing scams;therefore, they need to be classified. Malicious URLs that lead visitors to phishing or malware-infected websites that steal personal data can be included in emails, social media posts, or website content. Rogue QR codes can be used to propagate malware, steal data, or direct users there, much like malicious websites can. In this paper, the classification of malicious content is provided using two approaches:(1) Malware URL categorization based on ML and malware QR codes (2) classification of malicious QR codes based on deep learning. The first technique classifies URLs as dangerous or benign using machine learning models that are trained on features derived from the URLs. Several ML techniques, such as Random Forest, Naive Bayes, and Support Vector Machine, are used to evaluate the model's performance. The second option focuses on classifying harmful QR codes using deep learning techniques. The classification of QR codes as malicious or benign is done using CNN and well-known transfer learning models like RESNET. In general, the offered approaches provide effective techniques to classify unsafe material, which can be utilized to enhance security. © 2023 IEEE.

关键词： Websites

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Advance Chess Engine: an use of ML Approach 3

Advance Chess Engine: an use of ML Approach

引用

3rd International Conference for Innovation in technology, INOCON 2024

作者： Srivastava, Kushagra Pandey, Trilok Nath Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Mishra, Manoj Ranjan De, Utpal Chandra Vellore Institute of Technology School of Computer Science and Engineering Chennai India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350381931

Several researchers have sought to create a software that can learn to play cognitive games with little or no prior understanding of the game's rules. A typical chess playing machine investigates all of the possible moves from a chessboard arrangement before deciding on the next best move. The Deep Blue chess engine's brute-force searching strategy has had a significant influence on artificial intelligence, although it has been discovered to be resource intensive. Using the evolutionary and adaptive computing strategy on learning from human grandmasters, this study proposes a relatively easy and fast approach to developing an intelligent chess engine that can aid and hint at probable moves inside the game. Chess-playing machines may now easily surpass human talent thanks to the introduction of Machine Learning, Deep Learning and Artificial Intelligence. The game of chess is examined through the prism of these algorithms in this study. The comparison of the model performance with the existing ones has been performed to show the efficacy of the model. © 2024 IEEE.

关键词： Engines

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Artist-Specific Analysis of Spotify using BI Tools 14

Artist-Specific Analysis of Spotify using BI Tools

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Sharma, Varsha Atwal, Hetal Praveen Joe, I.R. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350335095

Spotify is one of the world's largest music audio streaming and media service provider with over 489 million monthly active users, including 205 million paying subscribers as of December 2022. The music from various artists around the globe can be found on Spotify where songs from these artists compete daily in order to top the charts. In this paper, we present an analysis of the music charts using many features that describe a song such as acousticness, danceability, etc., and present artist-specific suggestions that can help the artists know the genre of the songs that is most popular among the listeners and therefore, produce songs accordingly and hence help the artists gain popularity among the audience. © 2023 IEEE.

关键词： Audio acoustics

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Brain Tumor Detection and Classification Using Deep Neural Network and Interpretation Using XAI Techniques

Brain Tumor Detection and Classification Using Deep Neural N...

引用

2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Anitha, A. Nair, Anjana Kamaraj, Balakrishnan Vellore Institute of Technology School of Computer Science Engineering and Information Systems Tamil Nadu Vellore India Madurai Medical College Tamilnadu Madurai India

ISBN: (纸本)9798350376135

Clinical decision-making relies heavily on accurate brain tumor diagnosis from MRI imaging. Manual interpretation is time-consuming and error-prone. To enhance efficiency, automated techniques using deep neural networks (DNNs), particularly Convolution Neural Networks (CNNs), are proposed. This study incorporates explainable artificial intelligence (XAI) for interpretability. By addressing the need for automated and accurate diagnosis, this research aims to improve patient care and treatment outcomes, providing medical professionals with a dependable tool for brain tumor identification in clinical practice. © 2024 IEEE.

关键词： Deep neural networks

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A Simple Algorithm to Secure Data Dissemination in Wireless Sensor Network 1

引用

1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Sonai, Veeramani Bharathi, Indira Department of Computer Science and Engineering School of Engineering Shiv Nadar University Chennai Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Chennai India

ISBN: (数字)9789819981298

ISBN: (纸本)9789819981281

Wireless sensor networks are heavily utilized in numerous applications in both industry and science. As the original data is transferred from the sensor node to the sink node, unauthorized individuals attempt to access it, breaching confidentiality. The first step in enhancing the security of data transmission in a wireless sensor network (WSN) is figuring out the best method for encrypting data as it moves from the sensors to the central node. In order to move encrypted data from different sensor nodes to sink nodes efficiently in wireless sensor networks, this paper introduces a simple data encryption technique. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cryptography

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Prediction and Analysis of Sentiments of Reddit Users towards the Climate Change Crisis 1

Prediction and Analysis of Sentiments of Reddit Users toward...

引用

1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Ray, Sujana Senthil Kumar, A.M. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350336009

Climate crisis is one of the most talked about issues in the world today. In spite of a global agreement on the necessity to protect the earth from global warming, people still lack awareness on the graveness of the situation. Social media platforms such as Twitter, Facebook, Instagram, Reddit and others offer immense opportunities for people to become vocal and participate with their opinions and thoughts on these practical challenges by exchanging information and talking about them. By looking at their attitudes and the issues they discuss, it is possible to determine in this research how users of Reddit, one of the most well-known and popular social media platforms in the world, feel about climate change. Retrieved comments and posts are classified into two sentiment classes: Positive and Negative. To understand the sentiments, we find sentiment targets by comparing two neural networks CNN and RNN and using the more accurate model to predict sentiments of the comments in the test dataset and analyse the nature of climate change discussion over time. Although the computational maximal accuracy for the two models is comparable, it was discovered that the CNN model scored marginally better than the RNN in terms of average precision, average accuracy, and average loss. The examination of Reddit users' opinions demonstrates that the general attitude is negative, particularly when people acknowledge extreme weather events that have the potential to impact the public wellbeing framework. © 2023 IEEE.

关键词： Sentiment analysis

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A Comprehensive Review on Early Diagnosis of Alzheimer's Disease Detection

A Comprehensive Review on Early Diagnosis of Alzheimer's Dis...

引用

2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Gayathri, G.S. Nachiyappan, S. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350305708

A progressive neurodegenerative Alzheimer's Disease (AD) shrinks (atrophy) the brain and kills brain cells. On a global scale, Finland and the United Kingdom have the highest prevalence of AD, with 54.7 and 42.7 cases per 100, 000 inhabitants respectively. In India, there are 14.60 AD cases for every 100, 000 people and it is ranked 137th in the world and 13th in India among the 50 causes of death. At the beginning of the disease, a person may forget things, or have trouble remembering recent conversations. As the disease progresses, a person may experience severe memory loss and difficulty performing everyday tasks. Consequently, the only way to predict AD at an early stage is to use treatments that prevent the disease from progressing. This paper provides an overview of the current approaches to AD diagnosis and detection, with a focus on the use of Biomarkers and discussed the advantages and disadvantages of Machine Learning (ML)/Deep Learning (DL) techniques in early detection of AD. It also reviews the collection of AD datasets and the pre-processing techniques used in the studies. Although DL methods gives the best outcome measures such as accuracy, precision, F-measures, recall and Area Under the Curve (AUC) still DL techniques have some limitations that can be overcome via Quantum Computing (QC). QC is currently emerging new technology based on quantum theory where qubits are used to store the data instead of bits. It accomplishes 1000 times faster and also solves the complex problem within a fraction of time than the classical computers. © 2023 IEEE.

关键词： Alzheimer's Disease Biomarkers Datasets Deep Learning Machine Learning Quantum Computing

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Smartphone recommendation using popularity and collaborative filtering-based models 6

Smartphone recommendation using popularity and collaborative...

引用

6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Paul, Aniket Kumar Nath Pandey, Trilok Kumar, Aswathi Manoj Moloparambil, Savio Sajan Kartheek, Surya Singh, Suraj Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350394702

With so many options available in the smartphone market, the question of which model is to be purchased, arises. The model proposed in our article will be trained with large datasets which can then suggest the perfect model with a given set of inputs without any hassle to search for hours on the internet in order to decide which phone is best suited to the customer in accordance with his/her needs. In this paper, it should be assumed that "Users"and "Reviewers"are used interchangeably in this paper as it is assumed that phone reviewers and users are the same. Therefore, our smartphone's recommendation system is designed to create a system that can suggest the best suited smartphones based on the choices or behaviors of the consumers. © 2023 IEEE.

关键词： Smartphones

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Stroke Disease Prediction Using Adaboost Ensemble Learning Technique 4th

Stroke Disease Prediction Using Adaboost Ensemble Learning T...

引用

4th International Conference on Communication and Intelligent Systems, ICCIS 2022

作者： Ganachari, Sreenidhi Battula, Srinivasa Rao School of Computer Science and Engineering Vellore Institute of Technology Andhra Pradesh Amaravati India

ISBN: (纸本)9789819920990

This paper presents an approach to predict stroke utilizing a better model with Adaboost algorithm. Today the utilization of opportunely trained machine learning algorithms can be significantly utilized in fields such as medicine and data science to identify and provide solutions to quandaries that do not have a solution or if the current solutions are not efficacious. A stroke is a condition where blood arteries in the brain burst, harming the brain. The rigor of a stroke can be abated by early apperception of numerous stroke warning symptoms. Previous works have utilized sundry machine learning algorithms to determine stroke. Despite many research works in the literature indicating successful outcomes in preclinical stroke models, there has been no significant contribution to the medical field. Since one weak classifier cannot reliably predict an object’s categorization by itself, adaptive boosting, also known as AdaBoost, combines numerous weak classifiers in progressive learning to create a strong predictive classifier. It uses ensemble learning techniques to predict the accuracy which makes it better than the other algorithms discussed in the paper. The dataset used emanates from Kaggle with sundry parameters that have been taken into consideration. The machine learning algorithms considered are random forest algorithm, decision tree algorithm, SVM, logistic regression algorithm, KNN, Naïve Bayes, and AdaBoost algorithm to train different models and compare the results for the best model. Amongst all the algorithms AdaBoost algorithm gives us the best precision of 98.8%. Thus, the main aim of this paper is to propose a classifier—AdaBoost classifier that can be utilized by medical practitioners to detect the ailment in its early stages. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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