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A Secure Approach for Strengthening Session Management with Custom Session Handlers 2

A Secure Approach for Strengthening Session Management with ...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Kumaran, U. Gurupriya, M. Reddy, Annem Gnaneswara Nithin, G. Bhanusri, Kodati Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350368413

Session management is the underlying security mechanism that is instrumental in protecting the security and integrity of web applications' user sessions. The research is dedicated to the examination of session administration solutions in web applications to provide secure user sessions and minimize the risks of session hijacking and session fixation. It touches on session management before and after user login, breaking it down into session maintenance. The custom session handler offers passive protection against session hijacking via session management using a MySQL database. This approach provides a safe way of creating and managing sessions, validating them as well as handling session expiration and performance optimization. The implementation intends to strengthen session securities of users, therefore, increasing the safety and reliableness of web applications © 2024 IEEE.

关键词： Enhancing security Session management Vulnerabilities Web applications

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Auto-Scaling Anomaly Detection in Cloud computing Models using MAPE-K Loop 15

Auto-Scaling Anomaly Detection in Cloud Computing Models usi...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Mahesh, Nikhil Supriya, M. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

computing paradigms are changing at a faster pace leading to highly complex and dynamic environments, thereby demanding resource management acumen. Pivoting in these paradigms, auto scaling ensures optimal resource allocation, sufficient to accommodate variable demands. However, the dynamic nature of such settings poses challenges in maintaining reliability and efficiency in auto-scaling methodologies. This paper proposal solves this challenge through a dependable anomaly detection system intended for auto-scaling in cloud computing frameworks. This work tries to design, develop, and validate the framework of anomaly detection for resource consumption and workload patterns using a MAPE-K loop. The envisioned framework is meant to use machine learning techniques and methodologies of data analysis to track in real time system parameters for the early identification of potential scalability issues. In terms of scores for all metrics, it has performed better with the MAPE-K loop, and the R2 score for all metrics came out to be 1. © 2024 IEEE.

关键词： Resource allocation

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Augmented Data Protection: Fortifying Confidentiality through CBBPH approach 15

Augmented Data Protection: Fortifying Confidentiality throug...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Tripathy, Aayushi Jeeban Tiwari, Utkarsh Anirudh, S. Ashwani, Siddhant Kavitha, C.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350370249

The foundation of contemporary data security, guaranteeing integrity and confidentiality in digital communications, is referred to as cryptography. On the other side, image steganography strengthens data security by providing a stealthy way to hide information within seemingly same images. A new level of data camouflage is added by the tactile writing system Braille, whose small representation makes effective use of pixel components for storage. Columnar transposition is also applied to improve security by further obscuring the contained data. To improve data security, this paper presents the Columnar Binary Braille Pixel Hiding (CBBPH) approach, a revolutionary technology that combines Braille encoding, image steganography, encryption, and columnar transposition synergistically. The proposed method involves encoding plaintext messages into Braille, transposing them using columnar transposition, and then embedding them into the pixels in the least important portions of the image. The CBBPH strategy represents a significant breakthrough in data security techniques as experimental results show that it generates higher values in Peak Signal-to-Noise Ratio (PSNR) metric computations, indicating superior visual fidelity. ©2024 IEEE.

关键词： Steganography

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Identifying Sentiment in Legal Case Judgments using Random Forest Classifier 9

Identifying Sentiment in Legal Case Judgments using Random F...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Pavani, Bhupathi Vishva Mahitha, Desham Prabhakar, Priyanka Pati, Peeta Basa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

This paper explores sentiment analysis in legal judgments using machine learning techniques. Six machine learning models, including Naïve Bayes, K Nearest Neighbors, Logistic Regression, Support Vector Machine, Random Forest, and Decision Tree, are used along with three data embeddings namely T5, RoBerta, and LegalBert. The study aims to evaluate the effectiveness of these models in sentiment analysis tasks tailored to legal documents. Each model is trained and evaluated based on the unique characteristics of legal texts. Hyperparameter tuning has also been performed for all the models. The focus is on achieving high accuracy, F1 score and understanding the interpretability of the models' predictions. The highest accuracy achieved was by Random Forest of 67.5%. The study aims to provide insights into the applicability of various machine learning algorithms and data embeddings for sentiment analysis in the legal domain. © 2024 IEEE.

关键词： Sentiment analysis

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A Comparison of Explainable AI Models on Numeric and Graph-Structured Data 2

A Comparison of Explainable AI Models on Numeric and Graph-S...

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

作者： Avinash, Alekh Harikumar, Akshay Nair, Adithya Pai, Sreejith Kumara Surendran, Simi George, Livya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

An exponential growth of interest in the healthcare IoT over the past few years has increased the adoption of AI. However, healthcare analytics demands highly accurate and reliable systems that can not afford even a small amount of vulnerability. The black-box nature of AI models raises significant concerns about the computations involved in these predictions, leading to a lack of trust in these models. Explainable Artificial Intelligence (XAI) seeks to resolve this problem by providing a human-understandable explanation for AI decisions, bringing transparency, trust, and fairness to these AI models. This paper evaluates the existing XAI models on numeric and graph-structured healthcare data. The LIME and SHAP models are first used to explain the predictions in the numerical dataset of fetal health prediction. Second, graph-structured data is analyzed using GNN Explainer and PG Explainer. This evaluation suggests that XAI model interpretations benefit healthcare professionals and patients to trust AI predictions. © 2024 Elsevier B.V.. All rights reserved.

关键词： Forecasting

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Design and Implementation of a Movie Rental Website Using Automata Theory 5

Design and Implementation of a Movie Rental Website Using Au...

引用

5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Saluja, Abhinav Panda, Amitesh Kesavan, Suhas Panda, Niharika Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

This paper proposes a novel approach to designing a movie rental website using concepts from push down automata theory. The objective is to create a user-friendly platform for browsing, selecting, and renting movies while leveraging the computational power of push down automata (PDA) to manage the underlying logic efficiently. The system architecture employs push down automata to model various states and transitions in the movie rental process. Users can interact with the website through a GUI, where they can search for movies, view their details and begin rental transactions. Behind the scenes, push down automata handles the validation of user inputs and enforces business rules such as rental duration and payment processing. Push down automata is utilized to represent the different states of a movie's availability, including available for rent, items in cart, currently rented, and returned. Transitions between these states are triggered by user actions such as renting a movie, returning it, or extending the rental period. By modeling these states and transitions with push down automata, the system ensures consistency and correctness in the rental process. Overall, this project demonstrates the practical application of push down automata theory in the development of a movie rental website, offering a scale-able and efficient solution for managing movie rental operations while providing a seamless user experience. © 2024 IEEE.

关键词： Web Design

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Sapota Quality: Bruise Detection and Shelf Life Prediction 5

Sapota Quality: Bruise Detection and Shelf Life Prediction

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Raviteja, Mellacheruvu Reddy, Vanguru Venkata Varun Kumar Aqibuddin, Shaik Soppa, Mithil Teja Sarath, S. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350379945

The shelf life and consumer appeal of sapota, a tropical fruit, are influenced by fluctuations in its freshness and flavor over time. Apart from classifying bruises, this study looks into the application Using thermal imaging to estimate the shelf life and identify maturity of sapota. This research study intends to predict the shelf life of the sapota, maturity of the sapota and bruises detection for this we collected dataset using fluke thermal camera of 60 sapota fruits, each day for 5 days in order to capture the life cycle of sapota from unripe to ripen state. The weights are monitored for further analysis. Further, a seperate dataset is collected for bruise detection by intentionally damaging the fruit for bruise detection part. The dataset consists of ripen and unripen images together with the number of days that have passed since the photo was taken, along with weights on particular day. For these tasks, various convolutional neural networks CNN ResNet, VGG16 and MobileNet are used. In terms of ripeness classification, shelf life prediction and sapota bruise detection, the efficiencies of the models are assessed and accuracy we got is 93 percent for the best model. These findings suggest that the thermal imaging in combination with deep learning techniques for non-destructive quality Evaluation of sapota fruit may assist in reducing food waste. © 2024 IEEE.

关键词： Shelf life

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Malicious URL Detection: Comparative Study of Machine Learning Algorithms 15

Malicious URL Detection: Comparative Study of Machine Learni...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Jaswanthi, Thadakaluru Harshitha, Talluri Tanya, Simhadri Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350370249

Malicious URLs can be detected by leveraging lexical analysis and machine learning, where the parsed components of the URL are parsed, and its features include domain reputation, URL length, and the presence of suspicious characters. Research on the detection of malicious URLs suffers challenges in keeping pace with the changing structural landscape of URLs. Adaptation to dynamic patterns and addressing the technical issues of efficient processing and scalability become important to stay one step ahead of the evolving threat landscape. This paper concentrates on addressing these challenges with the integration of Machine Learning and Lexical Analysis. Features extracted in this process are then fed into machine learning algorithms for the classification of URLs and are tested on unseen data. Research performance is measured using several metrics including Precision, Recall, F1-score and Accuracy during the Model evaluation. Out of all the models experimented, Extra Trees classifier stands out the best with 92% accuracy, showing effectiveness in accurate detection of malicious URL’s. ©2024 IEEE.

关键词： Adversarial machine learning

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Cutting-Edge Steel Damage Classification Powered by Cloud Technology 15

Cutting-Edge Steel Damage Classification Powered by Cloud Te...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Reddy, Pennabadi Devendra Reddy, K Satya Sampath Gnaneswarachary, P. Reddy, P. Hemanth Prashanth, Mrudula Rajagopal, Shinu M. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

In this era of rapid technological advancement, the detection of metal damage has become increasingly crucial across various industries. The proposed work 'Cutting-Edge Steel Damage Classification Powered by Cloud Technology' aims to build an advanced system for real-time steel damage classification utilizing AWS services includes Rekognition, Lambda, and API Gateway that enables users to upload multiple images simultaneously for damage detection and classification, which will be an innovative approach to industrial defects detection which leads to their prevention. Such benefits of fast, accurate and scalable defect detection make it a critical asset for businesses expecting for exact and real-time solutions in every aspect. This study uses F1 score, precision, recall, and assumed threshold are as evaluation parameters. The use of AWS services is not just helping with model training and deployment, but it also provides failover mechanism and scaling capabilities to accommodate the varying workload demands. The model has the advantage of having rapid training time: 1.005 hours this shows the cloud infrastructure scalability and computational efficiency in dealing with the tasks requiring machine learning of complex nature. © 2024 IEEE.

关键词： Amazon API Gateway Amazon S3 AWS Lambda AWS Rekognition Cloud computing Damage Classification Grazing Pitted Steel detection

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Detection and Classification of Respiratory Syndromes in Original and modified DCGAN Augmented Neonatal Infrared Datasets 5

Detection and Classification of Respiratory Syndromes in Ori...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Sarath, S. Nair, Jyothisha J. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

In the current pandemic scenarios, a non-invasive method for determining a neonate's respiratory rate and categorizing them using a deep learning technique is highly pertinent. Acquiring an infrared neonatal dataset for detecting and classifying respiratory syndromes is challenging. The limited number of infrared videos and images representing different types of syndromes is a tremendous challenge to the accuracy of the deep learning model. This paper uses the Deep Convolutional Generative Adversarial Networks(DCGAN) with gradient penalty for the data augmentation. The Discriminator in a standard DCGAN architecture is a convolutional neural network (CNN) that receives an image as input and outputs a single scalar value that indicates the likelihood that the input image is real or fake. Adding a gradient penalty adds a regularisation term to the loss function. This modification helps to stabilize training by preventing mode collapse and generating higher-quality images. The augmented dataset helped to make the original imbalanced dataset more balanced and increased the size of the original dataset. When the accuracies of the deep learning models trained on the original and balanced augmented neonatal datasets were compared in this work, the model based on the balanced augmented dataset performed better. © 2024 The Authors. Published by Elsevier B.V.

关键词： Generative adversarial networks

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