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A Generalized Unknown Malware Classification 1

引用

18th EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2022

作者： Rani, Nanda Mishra, Ayushi Kumar, Rahul Ghosh, Sarbajit Shukla, Sandeep K. Bagade, Priyanka Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kanpur India

ISBN: (数字)9783031255380

ISBN: (纸本)9783031255373

Although state-of-the-art image-based malware classification models give the best performance, these models fail to consider real-world deployment challenges due to various reasons. We address three such problems through this work: limited dataset problems, imbalanced dataset problems, and lack of model generalizability. We employ a prototypical network-based few-shot learning method for a limited dataset problem and achieve 98.71% accuracy while training with only four malware samples of each class. To address the imbalanced dataset problem, we propose a class-weight technique to increase the weightage of minority classes during the training. The model performs well by improving precision and recall from 0% to close to 60% for the minority class. For the generalized model, we present a meta-learning-based approach and improve model performance from 48% to 72.06% accuracy. We report performances on five diverse datasets. The proposed solutions have the potential to set benchmark performance for their corresponding problem statements. © 2023, ICST institute for computer Sciences, Social informatics and Telecommunications engineering.

关键词： Malware

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Quantum-Safe Federated Learning: Enhancing Data Privacy and Security 2

Quantum-Safe Federated Learning: Enhancing Data Privacy and ...

引用

2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Kannan, E. Carmel Mary Belinda, M.J. Ravikumar, S. Alex David, S. Kannan, Sriram Vijay, K. Vel Tech Rangarajan Dr. Sagunthala RD Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical And Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Fidelity Investments Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331534950

Ensuring data security and privacy is highly pertinent in today's era marked by the dominance of collaborative machine learning. This study presents a novel framework, named"Quantum-Resilient Privacy-Preserving Federated Learning for Enhanced Data Security,"which leverages quantum computing to strengthen federated learning models against adversarial risks and enhance privacy safeguards. Our research investigates the incorporation of quantum key distribution and entanglement protocols into federated learning, therefore establishing a quantum- resilient methodology. The findings demonstrate the model's ability to effectively resist advanced threats, hence assuring the maintenance of strong data security measures. The utilization of quantum technology in this context serves to strengthen the fundamental principles of cryptography, while simultaneously introducing a novel approach to enhancing privacy. Moreover, the study explores the potential of the framework to accommodate various datasets from different cooperating entities, while also resolving the difficulties presented by the diversity of the data. The framework's success in accommodating multiple machine learning models and applications is demonstrated by evaluations of scalability and computing efficiency. In essence, the research paper titled"Quantum-Resilient Privacy-Preserving Federated Learning for Enhanced Data Security"represents a significant progression in the field of safeguarding collaborative machine learning. The quantum-resilient paradigm not only increases privacy but also offers a safe foundation for the future of collaborative machine learning. The issue at hand pertains to the growing apprehensions over the safeguarding of data privacy and security within the context of our technologically advanced day. © 2024 IEEE.

关键词： Federated learning

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Comparative Analysis of Time Series Models for Traffic Flow Prediction 10

Comparative Analysis of Time Series Models for Traffic Flow ...

引用

10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Mavani, Meet Chaturvedi, Pooja Manekar, Swati Institute of Technology Nirma University Department of Computer Science and Engineering Ahmedabad India

ISBN: (纸本)9798350382471

One of the important purposes for machine learning is to analyze the data and use that data in order to forecast data and many times especially in time series data, forecasting becomes very hard. The need of analyzing and forecasting traffic flow has always been emphasized. Government can collect data and use the data for better traffic management and avoid many traffic congestions. The data collected can then be analyzed and used for traffic volume forecasting which can save a huge amount of time lost in commute. It may also save lives by allowing emergency vehicles, such as ambulances, to get to hospitals more quickly. The problems stated above need a solution where it becomes at most necessary to forecast the traffic volume in order to prepare for the same. In this comparative analysis four different models are used for predicting traffic flow. The models include ARIMA, FbProphet, XgBoost and LSTM. These selected models have been used in order to forecast the hourly traffic volume. The dataset for traffic flow analysis is taken from UCI machine learning repository. The performance is then evaluated on the basis of several parameters such as Root Mean Square Error(RMSE), and Mean Absolute Percentage Error(MAPE). The XgBoost model performs the best as it achieves the lowest MAPE among all the considered models. The LSTM model performs better than the other models considered for the study. © 2023 IEEE.

关键词： ARIMA FbProphet LSTM Time Series Forecasting Traffic Volume XgBoost

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Predicting Disaster Tweets using Enhanced BERT Model 7

Predicting Disaster Tweets using Enhanced BERT Model

引用

7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Duraisamy, Premkumar Duraisamy, M. Periyanayaki, M. Natarajan, Yuvaraj KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Government Arts and Science College Department of Computer Science Tirupattur India

ISBN: (纸本)9798350397253

Twitter has become an essential means of communication in emergency situations. The widespread use of smart phones, anyone can immediately report any incident they witness. As a result, more agencies are interested in programmatically monitoring Twitter. Social media sites such as Twitter provide a common forum for sharing and exchanging personal experiences with others. Most of the people share personal experiences, neighborhood problems also the activities on social websites to let unwanted peoples to know. Many rescue organizations constantly monitor this kind of data to detect disasters and reduce the likelihood of casualties. However, humans cannot manually comb through vast amounts of data to uncover threats in real-time. To achieve this, many studies have proposed representing words in a form that computers can understand and applying machine learning techniques to the word representations to determine the sentiment of the text. Previous search techniques provide single embeddings or representations of words from a given document. However, the new Advanced Contextual Embedding is Bidirectional Encoder Representations from Transformers (BERT) method creates different vectors for the same expression with different settings. Although there are no specific studies on how BERT embeddings can help analyze catastrophe-type tweets, these representations have been successfully used in various Natural Language Processing (NLP) tasks. © 2023 IEEE.

关键词： Embeddings

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DEEP LEARNING BASED LAND COVER ASSESSMENT USING HIGH RESOLUTION SATELLITE DATA 8

DEEP LEARNING BASED LAND COVER ASSESSMENT USING HIGH RESOLUT...

引用

8th International Conference on Computing in engineering and technology, ICCET 2023

作者： Srivastava, Shivang Sharma, Yashasvi Prakash, Aditya Gupta, Shruti Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9781839539237

Satellite imagery analysis is crucial for various applications such as environmental monitoring, land-use management, and disaster management. However, traditional image processing methods often struggle with accurately classifying different land use types due to variations in texture, shape, and pattern. This research work proposes a deep learning-based approach for land use classification in satellite imagery data. In this work, a Convolutional Neural Network (CNN) model has been trained on a large dataset of satellite images and corresponding land use labels, achieving high performance in land use classification as measured by metrics such as Jaccard index and accuracy from the dice function and focal function. The proposed approach offers several advantages as compared to the tradition ones. This trained model can recognize subtle differences in land use types that might be challenging to discern using traditional methods. The model is highly efficient, capable of processing large volumes of data in a relatively short time. It can be easily adapted to different satellite imagery datasets and land use classification tasks, making it a flexible and versatile approach. With the proposed methodology on analysing images against 6 classes we get a land cover classification accuracy of 81.47%. © The Institution of engineering & technology 2023.

关键词： Neural network models

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Counterfeit Drug Prevention in Pharma Supply Chain using Blockchain technology 3

Counterfeit Drug Prevention in Pharma Supply Chain using Blo...

引用

3rd International Conference on Intelligent Communication and Computational Techniques, ICCT 2023

作者： Jha, Ram Krishna Alam, Parvez Priyadarshi, Nitin Ghazi, Mohammad Asad Bhargavi, M.S. Bangalore Institute of Technology Department of Computer Science and Engineering Kamataka Bengaluru India

ISBN: (纸本)9781665453578

Fake drugs are a growing serious issue related to the healthcare industry which causes extraordinary dangers to the society and the environment. Tracing the drugs at every step of the pharma supply chain is a difficult task. The goal of the research is to create a blockchain-based model that can forestall drug forging in drug store networks. The scope of the research lies where irregularities in the pharmaceutical supply chain affect public health and economy. The advanced elements of Blockchain makes it fit for complete recognizability of medications from producer to end patients, and the capacity to prevent fake prescriptions or medications. Drugs change proprietorship from producers to distributors and afterward retailers before it arrives at the client. Through the proposed model, the producer would have the option to transfer the subtleties relating to a medication, after which it will be sent for endorsement to the Government. From there on, distributors and retailers, in view of their prerequisites, can demand the endorsed drugs. Later on, in the event that a patient needs some drug, the person should demand it on the blockchain network. The solicitation will be shipped off the closest retailer/drug store and from that point, the patient can gather their medicine. Hyperledger fabric is utilized because they are permissioned and have an open source blockchain framework, meaning all the organization and pears are well known and confirmed. Our execution of the proposed blockchain based model features that the model can effectively forestall any medication being fake. This will be advantageous for the clients getting impacted with fake medications. © 2023 IEEE.

关键词： Smart contract

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Broker Clustering Enabled Lightweight Communication in IoT using MQTT 6

Broker Clustering Enabled Lightweight Communication in IoT u...

引用

6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Tripathi, Shruti Chaurasia, Brijesh Kumar Pranveer Singh Institute of Technology Department of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350346961

The advent of Internet of Things (IoT) applications is unavoidably reliant on standard real-time communication technology. In order to get hassle-free assistance for unreliable networks and fast message delivery between server and clients;consequently, it is necessary to mindfully choose an appropriate messaging protocol. In this paper, we have analyzed a lightweight messaging system for high-latency, low-bandwidth, and for unreliable networks protocol called Message Queuing Telemetry Transport (MQTT) for IoT networks. For machine-to-machine (M2M) communication, MQTT is most frequently employed in an embedded setting because of its low overhead. Following the selection of the best-fit messaging protocol for IoT systems, the determination of the appropriate broker enterprise is prioritized. This work highlights the key points of MQTT using HiveMQ Broker Enterprise, focusing on the broker node clustering to efficiently handle the message traffic, the message retention facility, and offline queuing. Finally, we conclude our discussion by addressing real-time monitoring of MQTT clients and the health of cluster nodes deploying MQTT Protocol on the HiveMQ Control Center dashboard.. © 2023 IEEE.

关键词： Machine-to-machine communication

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Multimodal Fusion of Transformer with Attention Mechanism for Improved Contextual Image Captioning

Multimodal Fusion of Transformer with Attention Mechanism fo...

引用

2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Latha, V. Singampalli Suma Dindi, Shriya Prabhu, Vannelaganti Sai Praveen, Bejjam Abhishek, Poddu Cristin, R. Gmr Institute of Technology Department of Computer Science & Engineering Vizianagaram Rajam India

ISBN: (纸本)9798350348910

Image captioning can provide automatic annotations or labels for images, describing the objects, scenes or activities present in the image. This can be beneficial in applications that require large-scale image analysis such as image recognition, recommendation systems and content classification. Traditional methods for generating image descriptions rely on rule-based systems, which are time consuming and lack the ability to capture the contextual details of complex images. The proposed method combines the power of two state-of the-art models: attention mechanism and Transformer. Attention mechanism is a powerful model for multimodal understanding that combines the domains of vision and language processing. Attention mechanism leverages pertaining on large-scale datasets to capture semantic relationships between images and text. By combining the multimodal understanding of attention mechanism with the sequence modelling capabilities of Transformer, superior performance in image captioning task can be achieved. The model can better capture complicated context dependence in input captions by adding more transformer layers. The suggested system's performance is assessed using BLEU-1,2,3,4 and other objective evaluation criteria. © 2023 IEEE.

关键词： Image recognition

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Voice Assistant with Emotion Recognition

Voice Assistant with Emotion Recognition

引用

2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Srikanth, S. Sravana Manikantan, Sreenivasan Posonia, A. Mary Mary, S. Prince Revathy, S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350333244

Voice assistants have become a essential part in our daily life, which helps to provide essential tasks in a hands free manner. Here the voice assistant helps the user with hands free experience that includes setting reminders, playing music, alarm, wishes etc. The voice assistant is powerful ones of cutting-edge speech recognition and natural language processing technique. The system will be intended to interpret and respond to a wide range of spoken instructions and enquiries in a natural and intuitive manner. Furthermore, the initiative intends to improve the user experience by giving tailored replies and suggestions depending on the user's preferences and history. For forecasting emotions, features such as emotion recognition, which detects a person's feelings through their face, are implemented. The goal of this project is to create a voice assistant that interacts with and shorten the burden of activities. © 2023 IEEE.

关键词： Emotion Recognition

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Interactive Learning using Mobile based Augmented Reality Application 2

Interactive Learning using Mobile based Augmented Reality Ap...

引用

2nd IEEE International Conference on Advances in Computing, Communication and Applied informatics, ACCAI 2023

作者： Nazeer, Shaik Somu, K.B. Roobini, M.S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350315905

Augmented Reality has become a state-of-the-art technology that enhances the real-world environment with interactive and immersive virtual objects. This technology mixes the real and virtual worlds, enabling realtime interaction and accurate 3D registration of real and virtual objects. One of the most significant uses of Augmented Reality is in the educational system, especially for primary classes, where it brings imagination to life and increases children's interest and excitement in their studies. The technology can also be incorporated into mobile apps that are accessible and user-friendly. Software such as Unity and Vuforia are leading the development of Augmented Reality. In education, this technology has made a significant impact and made learning more engaging and interesting. The augmented reality application can be used to teach geometric figures, the solar system, wild animals, automobiles, and more. It uses target images and software that can detect special markers. 3D objects, animations, and other interactive elements come to life when the target image is placed in front of a mobile device's camera, making learning more enjoyable. The application has received positive feedback from both teachers and students in initial tests. © 2023 IEEE.

关键词： Augmented reality

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