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An Integrated Dynamic Behavior Analysis and Adaptive Detection Framework for Enhanced Cloud Malware Detection

An Integrated Dynamic Behavior Analysis and Adaptive Detecti...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Keerthika Devi, S. Sandhya, G.R. Krithika, J. Joseph, Linda School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331543617

As malware threats continue to evolve and pose significant risks to cloud computing environments, there is a pressing need for advanced detection systems that can effectively address these challenges. This research introduces the Dynamic Behavior Analysis and Adaptive Detection Network (DBA-ADN), a novel framework designed to enhance malware detection through a combination of dynamic behavior analysis and adaptive detection techniques. DBA-ADN integrates real-time behavioral data with sophisticated adaptive algorithms to achieve superior detection performance. The study involves a comprehensive comparison of DBA-ADN against several benchmark systems, including MDCD, GloVe with CNN, CloudIntellMal, Hybrid Deep Learning Models, and DenseNet-121. The results reveal that DBA-ADN outperforms these established methods, achieving an impressive accuracy of 98.20% and an F1 Score of 0.99. This enhanced performance is attributed to DBA-ADN's ability to accurately identify both known and novel malware threats while minimizing false positives and false negatives. The framework's innovative approach addresses key limitations observed in existing solutions, as difficulties in handling sophisticated malware and inefficiencies in traditional detection techniques. By leveraging dynamic behavior analysis and adaptive algorithms, DBA-ADN not only improves detection accuracy but also reduces the complexity of deployment compared to its counterparts. The findings highlight DBA-ADN's potential to set a new benchmark in malware detection within cloud environments, offering a robust solution to the ongoing challenges of cybersecurity. This research contributes significantly to the field by providing a highly effective and adaptable approach to malware detection, laying the groundwork for future advancements and continued innovation in the domain. © 2024 IEEE.

关键词： Cloud security

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Skin Cancer Detection using Image Processing and Machine learning 3

Skin Cancer Detection using Image Processing and Machine lea...

引用

3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Rajkumar, S. Jaiswal, Shivam Jenila Livingston, L. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350372120

Skin cancer, with melanoma being the most perilous type, has a low survival rate. Detecting melanoma early can greatly enhance survival prospects. Skin cancer detection technology consists of the key components: image preprocessing (involving tasks like hair removal, noise reduction, and resizing), segmentation (used to isolate the region of interest), feature extraction from the segmented image, and image classification based on extracted features. In the proposed system section Convolution Neural Network (CNN) has been used as a base algorithm for training my model followed by training my model on ResNet and DenseNet and based on the evaluation results. Fine-tuning has been applied to adjust the learning rate dynamically. Different methods can be used for segmentation. Some commonly used segmentation algorithms are threshold in the histogram, etc., features extraction from the segmented image, and classification of the image from the features set extracted from the segmented image. © 2024 IEEE.

关键词： Diseases

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Real Time Bimodal Emotion Recognition using Hybridized Deep Learning Techniques 2

Real Time Bimodal Emotion Recognition using Hybridized Deep ...

引用

2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Bhat, Akshata A. Kavitha, S. Satapathy, Shashank Mouli Kavipriya, J. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

Emotion detection has become an important study subject due to the potential uses that it has in many other domains. It incorporates data from various sources, such as facial expressions, voice patterns, physiological indications, and textual signals. To identify human emotions, a real-time bimodal image and audio-based detection-based approach has been proposed. The approach is termed as "bimodal" since images and audio data are considered as inputs to the proposed model. In this study, hybridized CNN-LSTM for the image-based model and Multi-Layer Perceptron is used for the developing audio model. For the image-based model, two combined layers of CNN, and LSTM along with regularization techniques like drop-out, and batch normalization have been adopted for model development. Similarly, for the audio model, the dataset has been fine-tuned using a standard scaler and PCA before training. Based on the combined results of both models, the user's final emotion is determined. Both the audio-based model and the video-based model outperform the existing models discussed in the literature with higher performance accuracy of 79.34% and 77.27% respectively. © 2024 Elsevier B.V.. All rights reserved.

关键词： Emotion Recognition

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Alzheimer's Disease Classification Using CUDA-Accelerated DenseNet201 and VGG16 5

Alzheimer's Disease Classification Using CUDA-Accelerated De...

引用

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

作者： Munugala, Shravya Marathe, Atharva Prashant Vijayakumar, Rahul Kumaran, K. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331518097

Automated classification of Alzheimer's disease through MRI analysis presents significant computational challenges due to complex deep learning architectures and largescale image processing requirements. This paper introduces a GPU-accelerated multi-branch neural network framework that combines modified DenseNet201 and VGG16 architectures for efficient Alzheimer's disease stage classification. The proposed model employs parallel feature extraction pathways, with each branch independently processing MRI inputs before feature fusion. CUDA optimization techniques are implemented across the processing pipeline, including data loading, augmentation, and model training phases, resulting in significantly reduced computational overhead. The framework classifies brain MRI scans into four categories: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented. Experimental results demonstrate that the proposed hybrid architecture achieves 98% classification accuracy while substantially reducing training time through GPU acceleration. The model's performance is further enhanced through adaptive learning rate scheduling and selective layer fine-tuning. This work establishes an efficient approach for deploying complex neural architectures in clinical neuroimaging applications where computational resources and processing time are critical constraints. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Enhancing Context-Aware Hybrid Collaborative Filtering Using DBSCAN Clustering Approach 1

引用

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

作者： Abinaya, S. Sharma, Ayush School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9789819981359

ISBN: (纸本)9789819981342

With the increasing availability of travel-related data and the rising demand for personalized travel recommendations, context-aware recommender systems (CARS) are crucial for assisting travelers in discovering relevant and better experience. The technique known as density-based spatial clustering of application with noise (DBSCAN) is utilized by the framework that has been proposed to locate user clusters that have similar travel preferences and contextual signals. In addition to that, it utilizes a hybrid collaborative filtering strategy, which combines collaborative filtering. This combination improves the precision and variety of the recommendations, providing users with a wider array of pertinent options. By incorporating contextual data such as location, time, and user behavior, the proposed system CHCF_DBSCAN is able to generate recommendations that are specific to the user’s context and current circumstance. Experiments were conducted using Flicker City Platform travel dataset. The outcomes demonstrate the approach’s efficacy in enhancing the recommendations’ precision and relevance. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Collaborative filtering

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AutoML as a Catalyst for Predictive Maintenance Innovation: Strategies and Outcomes 3

AutoML as a Catalyst for Predictive Maintenance Innovation: ...

引用

3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Jayasurya, B. Suguna, M. Saravanan, Pooja Revathi, M. Vellore Institute of Technology School of Computer Science Engineering Chennai India

ISBN: (纸本)9798350372120

The intersection of predictive maintenance and Automated Machine Learning (AutoML) ushers in a transformative era for industrial optimization, wherein the foresight of equipment failures becomes both attainable and actionable. Conventional methodologies for predictive maintenance often necessitate profound technical knowledge and comprehensive data manipulation. This proposed work introduces an innovative framework based on AutoML, employing tools such as AutoKeras, Sweetviz, NumPy, pandas, Streamlit, and PyCaret, to democratize the crafting of predictive models. The outlined methodology automates the stages of data preprocessing, feature selection, and model optimization, markedly simplifying the process of predictive maintenance. The application of this framework illustrates a streamlined model development cycle, attaining significant achievements in the prediction of equipment failures, thereby augmenting operational efficiency, and diminishing downtimes. Through practical deployment, the proposed work demonstrates how this strategy not only alleviates the complexity inherent in predictive maintenance but also yields considerable enhancements in maintenance strategy and resource allocation. This exploration validates the potential of AutoML to revolutionize predictive maintenance, enhancing its efficiency and accessibility. © 2024 IEEE.

关键词： Predictive analytics

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Automated Safety Compliance Monitoring in Industrial Environments with Autonomous Rover

Automated Safety Compliance Monitoring in Industrial Environ...

引用

2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Sanjay, K.S. Devi R, Kanniga Arjun, P. Geethan, Daniel Anandha Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350378092

In today's industrial sector, the efficient monitoring of safety compliance is essential. The number of industrial accidents has been on the rise and this study proposes an alternate approach to traditional monitoring systems, designed to enhance safety by integrating NVIDIA's Jetson Nano for data processing and computer vision supported by NVIDIA's DeepStream SDK pipeline for real-time video analytics. Equipped with IR and ultrasonic sensors, the rover autonomously navigates and detects obstacles while seamlessly sending over data from the edge device to the cloud through the integration of Azure IoT resources, it achieves efficient data communication, enabling real-time safety management and analysis. © 2024 IEEE.

关键词： Ultrasonic sensors

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Enhancing Healthcare through the Symbiosis of Blockchain and Machine Learning in Preprocessing Skin Disease Images: A Review 15

Enhancing Healthcare through the Symbiosis of Blockchain and...

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Rokade, Sonali Mishra, Nilamadhab School of Computer Science and Engineering Vellore Institute of Technology Bhopal India

ISBN: (纸本)9798331300579

Recent advancements in skin disease identification leverage machine learning for automated diagnosis. However, safeguarding the integrity of sensitive medical data remains a top priority. This paper explores the integration of machine learning and blockchain in skin disease identification preprocessing, promising trust, transparency, and accuracy in dermatological diagnosis. The preprocessing workflow starts with gathering a diverse, labeled skin image dataset, followed by data cleaning and augmentation to enhance quality and quantity. Standardization methods like resizing and normalization ensure data consistency, while addressing class imbalances prevents model bias. Blockchain technology is introduced to secure data integrity. Each skin image and its metadata are encrypted and timestamped on a decentralized blockchain, guaranteeing tamper-proof authenticity. Machine learning models, typically based on CNNs, extract features and classify diseases, benefiting from high-quality preprocessed data. During training, blockchain securely records model parameters and progress, ensuring transparency and auditability, crucial for medical accountability and *** fusion of machine learning and blockchain enhances diagnostic accuracy while addressing data privacy and security concerns, potentially revolutionizing dermatological healthcare with a trustworthy, automated diagnosis system. © Grenze Scientific Society, 2024.

关键词： Data integrity

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A Predictive Analysis of Life Cycle Emissions of CO2 in Electric Vehicles Using Machine Learning 3

A Predictive Analysis of Life Cycle Emissions of CO2 in Elec...

引用

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

作者： Koilery, Shriya Sanjay Narikutty Muhammad, Ridha Karalakath, Nabeel Faisal Payyani Rasheed, Azeem A. Revathi, M. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350369083

Electric vehicles (EVs) have emerged as a quick solution and eco-friendly alternative to internal combustion engine vehicles (ICEVs), offering fewer emissions and better energy consumption. Lithium-ion batteries are used to give power to electric vehicles, but their production and disposal add a lot to the upstream Carbon Dioxide (CO2) emissions, which is often otherwise, neglected by the society while considering EV as a 0 to minimal CO2 emission substitute to fossil fuels. This is where a proper understanding of EV's life-cycle emissions is necessary. This paper proposes a novel approach to estimating the CO2 emissions of an EV considering its life-cycle emission, addressing limitations in the already available datasets by creating a new and comprehensive dataset. Four machine learning models, i.e., Linear Regression, Random Forest, Support Vector Regression (SVR) and Bi-LSTM, are evaluated for their accuracy in predicting emissions. These results indicate that Linear Regression and SVR struggled with the dataset's non-linearity, whereas Random Forest performed well but faced issues with unnecessary data. The Bi-LSTM model was the most effective, offering the best accuracy and efficiency in the prediction of CO2 emission by making use of long-term dependencies. The research emphasizes the importance of considering the entire life-cycle emissions of EVs to assess their impact on the environment and highlights Bi-LSTM's efficiency in accurately predicting CO2 emissions. © 2024 IEEE.

关键词： Low emission

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Harnessing Advanced Learning for Sarcasm Detection 3

Harnessing Advanced Learning for Sarcasm Detection

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Prasanna Kumar, M. Veeramani, Karthika Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350391565

Sarcasm in social media and in various methods of communication attracts higher attention and create profound impact even more than the customary positive and negative responses. It is necessary to propose a prototype and henceforth a fully-fledged system as a solution to detect sarcasm, to spread positive etiquette and avoid misguidance and misinformation. Unlike other text classification tasks, sarcasm detection is bit more challenging as it relies more on contextual nuances rather than actual word sentiment. The work leverages 5 learning algorithms (Logistic Regression, Multi-head Attention Encoder, Siamese Networks, BiLSTM and BiGRU) with data extracted from SARC (Self Annotated Reddit Corpus) Dataset and News Headline dataset. The models use contextual nuances, linguistic features to detect and analyze sarcasm. An analysis on how the model's performance varies based on the nature of text data (ie. formal and informal) has been made to learn about the nuanced nature of sarcasm. © 2024 IEEE.

关键词： Logistic regression

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