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Enhanced Machine Learning Approach for Detecting Spammers in Industrial Mobile Cloud Computing Environment 5

Enhanced Machine Learning Approach for Detecting Spammers in...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Bhavana, K. Sai Abhinandu, B. Sriyesh, P. Sai Rama, K. Satheesh Kumar, S. Institute of Aeronautical Engineering Dept. Computer Science & Information Technology Hyderabad India

ISBN: (纸本)9798331518097

This research study proposes an innovative machine learning algorithm, Spammer Identification Novel K-Means Extension (SINKEX), to fortify IoT mobile networks against commands and potential disruptions in industrial production. Utilizing an industrial mobile cloud dataset, the algorithm employs basic, content, and network features, including follower count, post frequency, message content, and hyperlink usage, for spam detection. After employing Pearson Correlation and Euclidean Distance to eliminate dissimilar records, Principal Component Analysis (PCA) is applied to remove irrelevant features. The SINKEX algorithm then labels unclassified records, designating an account as a SPAMMER or NON-SPAMMER based on features like follower count and post frequency. Comparisons with a hybrid fuzzy c-means clustering algorithm demonstrate SINKEX's superior performance, achieving higher accuracy, precision, and recall. The results are presented through an intuitive graphical user interface, showcasing the algorithm's effectiveness in securing industrial IoT networks against malicious activities. © 2024 IEEE.

关键词： Adversarial machine learning

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Improving Time Series Forecasting Accuracy with Transformers: A Comprehensive Analysis with Explainability 3

Improving Time Series Forecasting Accuracy with Transformers...

引用

3rd International Conference on Electrical, Electronics, information and Communication Technologies, ICEEICT 2024

作者： Joseph, Susmera Jo, Ashly Ann Raj, Ebin Deni Indian Institute of Information Technology Dept. of Computer Science Engineering Kottayam India

ISBN: (纸本)9798350369083

Time series forecasting is crucial in numerous sectors, including healthcare, energy, and finance. Transformer models, initially designed for natural language processing applications, demonstrate potential in capturing temporal dependencies within sequential data. In this paper, the application of transformer models in time-series forecasting is explored, and their performance and interpretability are investigated. Transparent models build trust and facilitate decision-making, especially in industries with ethical guidelines. While inherently interpretable time-series Transformers exist, there is a gap in research regarding post hoc methods, which is an area ripe for exploration. Understanding and comparing complex Transformer models for time series forecasting can benefit from advancements in explainability, ensuring accurate and comprehensible predictions. In comparison to five other transformer-based models and classical models, the Temporal Fusion Transformer (TFT) model outperformed them in short-term forecasting. Further enhancement of the model performance was achieved through a meticulous analysis of the Encoder-Decoder's importance, which contributed significantly to refining the forecasting accuracy. The vanilla transformer was explained using permutation feature importance. The model stability was analyzed using perturbation methods. © 2024 IEEE.

关键词： Spatio-temporal data

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Security Enabled Supply-Chain System Implementation Using Blockchain Integration with Hyperledger and IPFS Techniques 5

Security Enabled Supply-Chain System Implementation Using Bl...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Gupta, Amritesh Kumar Dwivedi, Rajendra Kumar Madan Mohan Malaviya University of Technology Dept. of Information Technology and Computer Application Gorakhpur India

ISBN: (纸本)9798350383522

One of the important areas that needs attention is supply chain in this fast expansion of globalization. There are numerous tiny parts that are summed up to obtain an end and furnished product be it agricultural goods in food supply chain or even other goods such as electronics. Now the quality of the final product depends upon the quality of the raw materials used. Since raw materials are manufactured, distributed, and sold globally, ensuring the integrity and security proposes a challenging task due to widespread counterfeit, cloned items, and other infiltration of untrusted items. The proposed work will counter these issues by not only providing traceability but also empowering transparency through Hyperledger. Along with these features, bridges are added to make the transfer of assets such as payments across different blockchains. © 2024 IEEE.

关键词： Blockchain Consensus Internet of Things (IoT) Smart Contracts Supply Chain management

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Notes Application using Android Studio and Cloud-based Data Management 7

Notes Application using Android Studio and Cloud-based Data ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Dodda, Ratnam Yanala, Maneesh Reddy Pavishetti, Srija Cvr College of Engineering Dept. of Computer Science And Information Technology Hyderabad India

ISBN: (纸本)9798350359299

In today's rapidly advancing technological land-scape, the widespread adoption of mobile applications has become a defining feature of our digital age. This research study presents a on the development of a mobile notes application using Android Studio, with the primary goal of offering users a versatile platform for creating and managing notes. The proposed notes application is designed with a simple and user-friendly interface, facilitating effortless note creation, editing, and deletion. Moreover, it has features like voice-to-text for making notes, text-to-speech for reading the notes aloud, note sharing for easily sharing ideas with others and image insertion for storing the photocopy of the written notes or diagrams based on user interest. This research work uses Android Studio as the integrated development environment (IDE) and utilizes the Java programming language for the app's core functionalities. Furthermore, it incorporates Firebase Authentication to ensure secure user account management and the Firestore for Database for seamless storage and retrieval of notes. This research study provides an essential tool for students, professionals, and anyone seeking a convenient means to keep track of vital information. By combining mobile application development tools and cloud-based data management like Firebase, the application simplifies how information is managed, aligning with the way we live in the digital age. © 2024 IEEE.

关键词： Authentication

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Security Enhancement in Tea Supply Chain using Blockchain with Hyperledger 2

Security Enhancement in Tea Supply Chain using Blockchain wi...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Gupta, Amritesh Kumar Dwivedi, Rajendra Kumar Madan Mohan Malaviya University of Technology Dept. of Information Technology and Computer Application Gorakhpur India

ISBN: (纸本)9798350327533

With the advancement of technology and emergence of blockchain, it has become quite a popular tool which is often integrated with systems and processes that require decentralization, traceability, consistency etc. One such area is of supply chain management. Often the raw materials used to make a final product do not come from a single source and hence there has been always a risk of making counterfeit, cloned item of the same. Same goes in the case of tea powder production too. Seeds used and leaves planted come from different sources which collectively determine the quality of the end tea powder. The proposed work tackles issues not only by providing traceability but also contributes in the overall security of the system by making use of Hyperledger fabric and Elliptic Curve Digital Signature Algorithm (ECDSA). In addition to these features, cross platform payment is also made possible by integrating the system with bridges. By making use of Hyperledger fabric and Interplanetary File System (IPFS), latency is decreased as well as throughput is increased. Additionally, making use of ECDSA provides same level of security with smaller key size and generates signature faster as compared to other cryptographic algorithms like Rivest-Shamir-Adleman (RSA). © 2024 IEEE.

关键词： Blockchain Elliptic Curve Digital Signature Algorithm (ECDSA) Internet of Things (IoT) Interplanetary File System (IPFS) Supply Chain Management Tea Supply Chain

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Reconnaissance Detection in Decentralised IoT System using Model-Contrastive Federated Learning 18

Reconnaissance Detection in Decentralised IoT System using M...

引用

18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Dutta, Rajdeep Kumar Chhetry, Bishal Matam, Rakesh Barbhuiya, Ferdous Ahmed Indian Institute of Information Technology Dept. of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798350391725

The detection of reconnaissance attacks is crucial for safeguarding Internet of Things (IoT) environments, which are inherently more vulnerable and resource-constrained compared to traditional computing systems. Traditional centralised detection methods face significant challenges, such as privacy concerns and limited scalability due to the need to aggregate raw data on a central server. These issues become particularly pronounced in IoT environments where devices are diverse and geographically distributed. To address these challenges, we propose ReconGuard, a novel approach leveraging Model Contrastive Federated Learning (MCFL). ReconGuard enables collaborative training across multiple IoT devices without the necessity of sharing raw data, thereby preserving user privacy and enhancing data security. The approach integrates contrastive learning techniques, which improve the model's ability to discriminate between benign and malicious activities by contrasting positive (similar) and negative (dissimilar) data pairs. Our experimental results demonstrate that the ReconGuard-based detection system provides a scalable and privacy-preserving solution for identifying reconnaissance activities. These activities are often the initial step in more severe cyber threats, such as botnet attacks, which can lead to significant disruption and damage. By effectively detecting reconnaissance activities, ReconGuard enhances the overall cybersecurity framework of IoT environments. This research presents a viable and innovative method for improving cybersecurity in IoT systems, addressing the critical need for scalable, efficient, and privacy-preserving intrusion detection solutions. The use of MCFL in ReconGuard not only mitigates the challenges of data centralisation but also leverages the distributed nature of IoT networks to enhance robustness against cyber threats. © 2024 IEEE.

关键词： Contrastive Learning

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Designing A Secure Large Scale E -Voting System Leveraging Sharding Blockchain with Interoperability Protocol and Consensus Mechanism 2

Designing A Secure Large Scale E -Voting System Leveraging S...

引用

2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Maurya, Alka Dwivedi, Rajendra Kumar Madan Mohan Malaviya University of Technology Dept. of Information Technology and Computer Application Gorakhpur India

ISBN: (纸本)9798350306415

The advent of electronic voting (e-voting) systems has promised greater accessibility, efficiency, and transparency in the process of electoral. However, by adopting e-voting systems, they bring forth significant challenges, primarily related to security and scalability. Old e-voting systems encounter threats such as vote manipulation and denial-of-service attacks, making it imperative to design robust and secure solutions. This research focuses on addressing these challenges through a collaborative approach utilizing sharding technology with interoperability enabling it to handle a vast number of votes simultaneously without compromising performance or security. Recognizing the importance of seamless data exchange, our e-voting system places a strong emphasis on interoperability. The consensus mechanism providing rapid transaction finality, enhanced security, and resilience against malicious actors. It ensures the integrity in the process of voting and safeguards against alterations. We present a pioneering approach to reduce all kind of challenges in existing model by giving a secure e-voting that harnesses the power of sharding blockchain, interoperability, and a novel consensus mechanism. The analysis of blockchain structure that provide blockchain as a service initiates the article. Then, based on blockchain technology, we suggest a unique electronic voting method. That resolves all the issues we found. This reduces the cost of holding a national poll and enhances security. © 2024 IEEE.

关键词： Consensus Mechanism E-Voting System Interoperability Protocol security Sharding Blockchain Smart Contract

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Single- and Multi-Hop BERT Question Classifier for Open-Domain Question Answering (SiMQC) 31

Single- and Multi-Hop BERT Question Classifier for Open-Doma...

引用

31st International Conference on Electrical Engineering, ICEE 2023

作者： Sayyad, Faeze Zakaryapour Bohlouli, Mahdi Dept. Computer Science and Information Technology Zanjan Iran

ISBN: (纸本)9798350312560

Multi-hop Question Answering has recently received particular attention in research and practice, especially in the context of conversational systems and answering complex questions. Various architectures have been proposed to answer these complex multi-hop questions. However, in real-world scenarios, a conversational system should answer both simple (single-hop) and complex (multi-hop) questions. In this work, we propose an efficient BERT question classifier that supports retrievers in the question-answering systems to process single- and multi-hop questions. We also released a mixed dataset consisting of both single- and multi-hop questions. We show that utilizing our classifier inside the QA system can improve these systems' accuracy and enable them to answer both kinds of questions considering their complexity. © 2023 IEEE.

关键词： Deep learning

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Formal Modeling and Verification of Justification and Finalization of Checkpoints in Ethereum 2.0 Beacon Chain 1

Formal Modeling and Verification of Justification and Finali...

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1st IEEE Karachi Section Humanitarian technology Conference, Khi-HTC 2024

作者： Rashid, Muhammad Rasool, Imran Zafar, Nazir Ahmad Afzaal, Hamra Comsats University Islamabad Dept. of Computer Science Sahiwal Campus Pakistan Information Technology University Dept. of Computer Science Lahore Pakistan

ISBN: (纸本)9798350373042

New to the Ethereum platform with version 2.0 is the Beacon chain. Validator status, attestation information, and many more are maintained via the proof-of-stake (PoS) consensus protocol, which is relied upon. The Ethereum 2.0 beacon chain relies on the validation and completion of checkpoints to validate and finish all the blocks associated with those checkpoints. By formally verifying it using the SPIN model checker, this research tackles the issue of the dependability and security of the Beacon Chain's justification and finalization operations. Due of its novelty (launched in 2020), there is little any literature on the subject. Additionally, no previous study has formally verified the beacon chain using the SPIN model checker. The study makes use of PROMELA, a formal specification language, to formally outline the reasoning and finalization method of the Ethereum 2.0 Beacon Chain. Utilizing the SPIN Model Checker, a program graph is generated for this procedure, which formulaically expresses safety features via the use of linear temporal logic (LTL). To make sure everything is in order, we run the SPIN model checker with the program graph and LTL formulae as inputs to see whether the program graph satisfies the properties. This is the formal verification process. © 2024 IEEE.

关键词： Model checking

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A Multistage Cascaded Neural Network Model to Identify the Severity of Epileptic Attack

A Multistage Cascaded Neural Network Model to Identify the S...

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

作者： Simon, Martha Thampi, Lidiya Lilly Dept. of Computer Science College of Engineering Trivandrum India Indian Institute of Information Technology Kottayam Dept. of Computer Science Kottayam India

ISBN: (纸本)9798350376135

Epilepsy is a neurological disorder characterized by recurrent seizures, impacting the quality of life for millions of individuals worldwide. Timely and accurate detection of epileptic seizures is crucial for proper diagnosis and effective treatment. Electroencephalogram (EEG) signals provide vital information about brain activity and serve as a valuable tool for epilepsy detection. The methodology opted for this study was an enhanced multilayer feed forward neural network for automatic detection of epilepsy from EEG signals and hence predicting the severity. The authors build a multistage cascaded neural network model for achieving the same. Initially a Simple Artificial Neural Network (SimpleANN) is designed to classify EEG segments into seizure, healthy and interictal. Later a Multilayer Feed Forward Neural Network (MFFNN) is employed for detecting the severity of epileptic patients and hence can be classified into primary and secondary stages. The experimental results showed that the proposed SimpleANN classifier had provided noticeable results, and outperforms all tested classifiers. The study involves the collection of EEG data from a diverse group of patients, including those with epilepsy and healthy individuals. This automated epilepsy identifying system passes through different stages including decomposition, feature extraction for achieving a better performance model. The proposed system was tested and compared with SVM, KNN, MLP and Decision Tree under different measurement metrics. ANN has managed to give an accuracy of 80 - 90% which is significantly higher as compared to other machine learning models. The proposed work can automatically learn complex patterns from EEG data provides an advantage in identifying subtle seizure related features that may not be evident through manual analysis. Furthermore, the model offers real-time capabilities, making it suitable for continuous monitoring of patients at risk of seizures. © 2024 IEEE.

关键词： Electroencephalography

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