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

作者： Abhijit, Chunduru Sri Himmatramka, Rishav Bishoyi, Aum Shiva Rama Prasath, Lallith Subbulakshmi, T. Vellore Institute of Technology Dept. of Computer Science Chennai India Vellore Institute of Technology Dept. of Electronics Engineering Chennai India

ISBN: (纸本)9798350335095

Internet of Things (IoT) devices are more vulnerable to viruses, assaults, and harmful activities as they are developed in tandem with the rise in cybercrime. Network Intrusion Detection Systems (NIDS) are generally used to detect attacks in the IoT devices. Machine learning (ML) techniques are being employed to improve these systems. This study compares various ML techniques to develop NIDS using UNSW-NB15 dataset and proposes a federated learning (FL) approach to overcome the limitations of classical ML approaches. Feature selection is done on the basis of correlation between them and five ML approaches are implemented: Logistic Regression, K-Nearest Neighbors, Random Forest (RF), Decision Tree and Artificial Neural Network. RF was found to be the best performing model for both binary and multi-class classification. However, there are certain limitations in this centralized approach which assumes the presence of a central organisation to store and execute analysis on data from all participant devices. FL is a decentralized approach where each IoT device is considered as a client and models are trained on these devices without transmitting data. This preserves privacy of data and also reduces data transfer costs as only model weights are shared instead of huge data. To assess the effectiveness of the proposed method, we conduct extensive experiments on the UNSW-NB15 dataset. The proposed FL detection model is shown to be both accurate and superior to the centralised model in experiments and empirical analyses, demonstrating its resilience and benefits. © 2023 IEEE.

关键词： Internet of things

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An Innovative Approach Towards Efficient Diabetes Mellitus Prediction Using Federated Forest

An Innovative Approach Towards Efficient Diabetes Mellitus P...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Madhu, Gifsy De, Anurag Pal, Gautam School of Computer Science and Engg. VIT-AP University AP Amaravati India Tripura Institute of Technology Computer Science & Engineering Dept. Tripura Narsingarh India

ISBN: (纸本)9798331540821

In this study, we discuss a method for diagnosing diabetes mellitus that combines the Random Forest algorithm with Federated Learning. The global model preserves data privacy while guaranteeing accurate and dependable diagnostics by combining local models rather than data. The diabetes dataset, sourced from Mendeley Data, is preprocessed to address class imbalance through the Synthetic Minority Oversampling Technique (SMOTE). The dataset is further augmented using Conditional Generative Adversarial Networks (CGAN) to introduce non-lID variations while splitting the data. Our global model is evaluated and compared across different node configurations, with the optimal performance achieved using six nodes. The results show impressive performance characteristics with 98.18% accuracy, 99.09% specificity, 98.18% sensitivity, and an ROC AUC of 1.00. These findings indicate that our Federated Forest based approach significantly enhances the diagnostic capability for Diabetes Mellitus while maintaining data privacy and integrity. © 2024 IEEE.

关键词： Federated learning

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Block-Hash Signature (BHS) for Transaction Validation in Smart Contracts for Security and Privacy using Blockchain

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Journal of Mobile Multimedia 2023年第4期19卷 935-962页

作者： Bhatnagar, Sonika Dayal, Mohit Singh, Deepti Upreti, Shitiz Upreti, Kamal Kumar, Jitender Dept. of Computer Science & Engineering Government Polytechnic Baheri Bareilly India Dept. of Applied Science Bharati Vidyapeeth’s College of Engineering New Delhi India Dept. of Information Technology ABES Institute of Technology Ghaziabad India Dept.of Information Technology Asian Education Group (AEG) Noida India Dept. of Computer Science & Engineering Dr. Akhilesh Das Gupta Institute of Technology and Management New Delhi India Dept. of Computer Science & Engineering J.C Bose University of Science & Technology YMCA Haryana Faridabad India

Some of the well-known signature techniques like Winternitz and Lamport are not considered to be very appropriate for the usage of hashing or smart contracts in Blockchains security because of their size O(n²), which is prominently too high. Although in Blockchain, the security concern is on the top priority because of its distributed P2P design still, the security enhancement is required to sign and verify the documents forwarded to the peers, especially in Hyperledger Fabric. Here, this paper presents a new signature technique “Block-Hash” to enhance Blockchain security by using it in smart contracts as well as hashing with size 3Xn bits (n = 256, generally for SHA-256 Hashing) and which can score 112 bits security. The proposed signature can be used appropriately for signing a smart contract by the endorser or committer node. Also, it can be used with a hash algorithm in forming a Merkle tree. Apart from the description and implementation of Block-Hash Signature, this paper has covered the analysis of its security and correctness measures with a table for result comparison. © 2023 River Publishers.

关键词： blockchain security Digital signature hyperledger fabric smart contract

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Personality Prediction Using ML and NLP 7

Personality Prediction Using ML and NLP

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Mahandule, Adinath Fiske, Gaurav Malusare, Vedang Bhambure, Omkar Shedge, Rajashree Ramrao Adik Institute of Technology Dept. of Computer Engineering Navi Mumbai400706 India

ISBN: (纸本)9798350304268

Estimating a person's personality is essential in understanding how they manage stress, lead, communicate, collaborate, and influence others. One of the critical factors that affect how people interact with their environment is their personality. This project aims to utilize machine learning and also natural language processing techniques to predict the personality of students based on a questionnaire. The system analyzes the user's personality in the database, which already records personality traits. The system then identifies the user's personality based on the MBTI model, a widely used personality model. This project's significance lies in the capability of the system to aid students, teachers, and businesses in identifying students' personalities to plan their actions better. The ML and NLP techniques implemented in this system can categorize or anticipate students' personalities, thus assisting businesses in their hiring process. © 2023 IEEE.

关键词： Catboost Gradient Boosting Machine Learning Myers-Briggs Type Indicator (MBTI) Natural Language Processing TF-IDF(Term Frequency-Inverse Document Frequency)

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Towards Adoption of Green Blockchain with Emphasis on Blockchain Type, Consensus Protocols, Data Sharding and Smart Contracts 2

Towards Adoption of Green Blockchain with Emphasis on Blockc...

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2nd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2023

作者： Phansalkar, Shraddha Mishra, Deepa Chaube, Neha Sonkamble, Rahul MIT Art Design and Technology University Dept. of Computer Science & Engineering Pune India

ISBN: (纸本)9798350333763

Blockchain and related Distributed Ledger Technologies (DLT) are anticipated to transform a the world of web from a centralised document-sharing platform to a comprehensive decentralised platform that facilitates the exchange of digital currency and supports autonomous management of digital assets. The central server is susceptible to attacks, distrust and collusion. If the web can assure reliable, safe, and responsible updates among independent participants without the need for a centralized server, the perception of a decentralised web can be re-instantiated. One of the essential technologies required to restore the openness of the internet while maintaining its security is distributed ledger technology (DLT). DLTs may now totally handle business and legal transactions online, creating a more trustworthy and accountable environment. Blockchain technology marks a major breakthrough by removing the need for a centralised trusted authority in a widely distributed network. Instead, a consensus must be reached among several sources of trust, based on an algorithm, that this transaction may be believed to be legitimate. The consensus algorithms in blockchain technology offer an immutable and permanent record of a transaction that is immutable, trustworthy and secured. Consensus algorithms are however energy consuming because of their computation heavy nature. This has been a biggest inhibition towards blockchain adoption. The energy needs for committing a blockchain transaction is also governed by whether it is a public/permissioned blockchain, its consensus algorithm, onchain-offchain data and the code complexity of smart contracts. The paper presents a state-of art evaluation of the current blockchain platforms and cryptos and evaluate them with the energy consumptions. Additionally, it also proposes a framework architecture of a green blockchain application. A green blockchain refers to the implementation of environmentally sustainable algorithms, tools and platforms

关键词： Smart contract

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Design and Implementation of a Distributed Network Management System for a Multi-Branch University using GNS3 14

Design and Implementation of a Distributed Network Managemen...

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

作者： Hosen, Md Apu Hoshen Moz, Shahadat Akber, Md. Ali Northern University of Business and Technology Khulna Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350335095

The rapid digitalization of communication necessitates continuous skill updates to keep pace with evolving technology. Network management involves multiple functionalities that play a crucial role in optimizing network performance and ensuring continuous availability. It is very important to ensure a proper network management system for a university that has multiple branches to effectively manage and monitor their network infrastructure. A proper network management system provides centralized control and visibility over the entire network infrastructure, allowing administrators to efficiently manage and monitor network devices, configurations, and performance from a single interface. this research has developed a network management system that facilitates real-time network monitoring and troubleshooting, enabling proactive identification and resolution of issues to ensure smooth operations across all branches. It simplifies configuration management, ensuring consistency and compliance in network settings and updates. Resource optimization is achieved through insights into network utilization and performance metrics, allowing administrators to allocate resources efficiently and plan for future expansion. Security and compliance measures can be enforced, protecting sensitive data and ensuring adherence to regulatory requirements. This study utilized GNS3 to create a network layout for a university with multiple branches, along with essential security configurations for the network design. A diverse range of protocols was implemented to safeguard and cater to the users of the secure university network. Furthermore, Switches, Data Routers, firewalls, and BGP Routers were incorporated into the network to enhance communication efficiency and streamline operations. © 2023 IEEE.

关键词： Sensitive data

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Youtube Spam Detection Scheme Using Stacked Ensemble Machine Learning Model

Youtube Spam Detection Scheme Using Stacked Ensemble Machine...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Shabadi, Laxmi Chaitra, Y.L. Srikanth, P. Vijay Kumar, L. Kashyap, Udisha Sjb Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350300826

This work introduced a method to identify English spam comments on YouTube, a platform that has experienced a significant increase in spam activity. Despite YouTube's efforts to implement its own spam filtering system, it still struggles to effectively block spam comments. To address such an issue, we analyzed existing research on YouTube spam comment detection and performed classification experiments using an ensemble model that combines various techniques. A stacked ensemble model combines the predictions of multiple individual models to improve the accuracy and robustness of spam detection in social media applications. It leverages the strengths of different models and captures a broader range of features, leading to a more accurate classification of spam and non-spam content. It has then implemented a web extension to hide the spam comments from the page by modifying the CSS (Cascading Style Sheets). The comment data utilized in experiments was sourced from popular music videos by artists such as Psy, Katy Perry, LMFAO, Eminem, and Shakira. © 2023 IEEE.

关键词： Classification (of information)

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Deep Variational Sequential Monte Carlo for High-Dimensional Observations

Deep Variational Sequential Monte Carlo for High-Dimensional...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： van Nierop, Wessel L. Shlezinger, Nir van Sloun, Ruud J.G. Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Dept. of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer-Sheva Israel

ISBN: (纸本)9798350368741

Sequential Monte Carlo (SMC), or particle filtering, is widely used in nonlinear state-space systems, but its performance often suffers from poorly approximated proposal and state-transition distributions. This work introduces a differentiable particle filter that leverages the unsupervised variational SMC objective to parameterize the proposal and transition distributions with a neural network, designed to learn from high-dimensional observations. Experimental results demonstrate that our approach outperforms established baselines in tracking the challenging Lorenz attractor from high-dimensional and partial observations. Furthermore, an evidence lower bound based evaluation indicates that our method offers a more accurate representation of the posterior distribution. © 2025 IEEE.

关键词： Particle Filters Sequential Monte Carlo Variational Inference

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Impact of Machine Learning in Music Genre Classification using CNN 14

Impact of Machine Learning in Music Genre Classification usi...

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

作者： Venkatesh, Jorige Kannan, Karthik Ayyadurai, M. Sathish, M.G. SRM Institute of Science and Technology Dept. of Computer Science and Engineering Chennai Ramapuram India

ISBN: (纸本)9798350335095

The rapid expansion of available music tracks has made the classification of music genres increasingly crucial. To achieve accurate genre predictions, machine learning techniques have been widely employed and proven highly efficient. In this study, we focused on enhancing the accuracy of music genre classification systems by training a Convolutional Neural Network (CNN) model using Keras. Music genre classification serves as a valuable tool for music learning, offering a reliable method of categorization. Given the immense amount of data and the inherent complexity in identifying music genres, we leveraged the power of deep neural networks to classify ten distinct genres. To evaluate the effectiveness of our approach, we compared the performance of the CNN model to other existing models using the GITZAN dataset. Our results demonstrated the success of the CNN model in accurately classifying music genres, surpassing previous studies while requiring less data. This research contributes to the field of music genre classification by providing an advanced and efficient solution using deep learning techniques. © 2023 IEEE.

关键词： Music

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Secure Sentinel Leveraging Machine Learning for Fraud Detection in Blockchain Transactions 5

Secure Sentinel Leveraging Machine Learning for Fraud Detect...

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5th International Conference on Data Intelligence and Cognitive informatics, ICDICI 2024

作者： Shirley, C.P. Thanga Helina, S. Berin Jeba Jingle, I. Saran, P. Absin, S.J. Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India Kpr College of Arts Science and Research Department of Commerce with Computer Application Coimbatore India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

Blockchain technology's decentralized and transparent transaction platforms have changed a number of sectors. However, harmful activities like fraud and money laundering are also drawn to blockchain due to its distributed and unchangeable nature. The distinctive features of blockchain transactions frequently prove to be too much for conventional fraud detection techniques to handle. In order to identify fraud in blockchain transactions, this work suggests "Secure Sentinel,"a machine learning-based method based on the Isolation Forest concept. By separating anomalies in a binary tree structure, the Isolation Forest algorithm is highly effective in detecting abnormalities inside datasets. By utilizing this approach, our system is able to identify transactions on the blockchain that are suspicious or stray from the norm. The Isolation Forest model will be trained using attributes such transaction amount, frequency, source/destination addresses, and transaction time. The project's goal is to improve blockchain networks' security and integrity by offering a real-time fraud detection system. Users, miners, and regulators are among the blockchain stakeholders who can take proactive steps to reduce risks and preserve system confidence by automatically identifying potentially fraudulent transactions. We will use past blockchain data to test our model's performance and determine how well it detects different kinds of fraudulent activity in terms of accuracy, precision, recall, and efficiency. By using machine learning to protect blockchain ecosystems from new dangers, Secure Sentinel is a step in the right direction toward advancing the acceptance and sustainability of blockchain technology. © 2024 IEEE.

关键词： Adversarial machine learning

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