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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Vinod, D. Prasad, M. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350336009

Traffic classification is an automated technique that divides computer network traffic into several categories depending on different factors like protocol or port number. In a complicated context, traffic categorization is an important tool for network and system security. A monitoring system called intrusion detection looks for abnormal activity and sends out notifications. In order to safeguard a system from network-based attacks, Network Intrusion Detection Systems (NIDS) play a crucial role in monitoring and analyzing network traffic. Active and passive intrusion detection systems (IDS), network intrusion detection systems (NIDS), host intrusion detection systems (HIDS), knowledge-based (signature-based) IDS, and behaviorbased (anomaly-based) IDS are some of the numerous types of intrusion detection systems (IDS). Passive IDS is just designed to monitor and analyze network traffic behaviour and notify an operator of potential vulnerabilities and attacks, whereas Active IDS is also known as Intrusion Detection and Prevention System. A network's malicious traffic is identified using a network-based intrusion detection system (NIDS). A host-based IDS monitors system activity and seeks for indications of abnormal behaviour. For networks with unidentified traffic, the intrusion detection system designed using flow and payload statistical characteristics and clustering approach needs additional clusters. The present intrusion detection system however is affected by false alarm rate, poor detection rate, imbalanced datasets and response time which lead to misclassification of intrusions in various scenarios. Hence, there is a requirement for developing an automated intrusion detection system that works well in different scenarios. The proposed system uses supervised and unsupervised intrusion detection and classification methods to increase the classification accuracy. To categorize the intrusions, dimensionality reduction strategies are used in conjunction with the c

关键词： Chemical detection

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Deoxyribonucleic Acid Cryptography Based Least Significant Byte Steganography 7th

Deoxyribonucleic Acid Cryptography Based Least Significant B...

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7th International Conference on computer, Communication and Signal Processing, ICCCSP 2023

作者： Anavekar, Aditya Rajkumar, S. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9783031398100

Cryptography and steganography are used to safeguard data, but each has its own set of problems. One of the major issues with cryptography is its ridiculous out-put, so the attacker will interrupt the transfer or carefully examine the data from the sender to the receiver. The issue with steganography is that the message is known when the existence of hidden information is revealed or even just suspected. In order to increase information security, an integrated data protection ap-proach employing cryptography and steganography techniques is described in this study. The Least Significant Byte (LSB) Steganography technique and the Deoxyribonucleic acid (DNA) transcription method in cryptography are first suggested. The proposed method is implemented and tested with various perfor-mance metrics. Further, experimental results compared with the existing work, and it turns out to be more efficient. © IFIP International Federation for Information Processing 2023.

关键词： Cryptography

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Intrusion Detection System for Defending against DoS Attacks in the IoMT Ecosystem 4

Intrusion Detection System for Defending against DoS Attacks...

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4th International Conference on Communication, Computing and Industry 6.0, C216 2023

作者： Puthin Krishna, M. Kumar, Suvidha Rupesh Palani Thanaraj, K. School of Computer Science & Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350327328

This paper endeavors to enhance healthcare security in the Internet of Medical Things (IoMT) by developing an Intrusion Detection System (IDS) focused on countering Denial of Service (DoS) attacks. Leveraging machine learning algorithms including Support Vector Machine (SVM), Random Forest (RF), Linear Discriminant Analysis (LDA), and k-Nearest Neighbors (k-NN), the IDS is trained on Wireshark-simulated data to identify and mitigate DoS threats. By filling critical gaps in IoMT cybersecurity literature and providing a dataset for IoMT-based DoS attacks, this work fortifies the resilience of IoMT infrastructure, safeguarding health data, healthcare service continuity, and patient safety in our increasingly interconnected healthcare landscape. © 2023 IEEE.

关键词： Denial-of-service attack

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Breast Cancer Classification using Metaheuristic Optimization and Machine Learning 3

Breast Cancer Classification using Metaheuristic Optimizatio...

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

作者： Chandra, Avinash Kadam, Ayush Sangram Jain, Palak Kumawat, Mohit Patil, Hemprasad Yashwant Gawas, Mahadev Anant Vellore Institute of Technology School of Electronics Engineering Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India State Higher Education Council Directorate of Higher Education State Government of Goa Goa India

ISBN: (纸本)9798350372120

This research utilizes advanced machine learning techniques, specifically focusing on Decision Tree, Naive Bayes, Random Forest, and Ada Boost models, to conduct a thorough examination of breast cancer prognosis. Our findings reveal compelling insights into the effectiveness of diverse machine learning models in assessing breast cancer. The Decision Tree model achieves an impressive accuracy of 93.5%, accompanied by a precision rate of 86.73%. The Naive Bayes model demonstrates high accuracy, reaching 95.5%, with a precision of 91.32%. Notably, the Random Forest model stands out with exceptional performance, achieving an impressive accuracy of 98.3% and a precision of 96.5%. The Ada Boost model also proves to be robust, boasting an accuracy of 97.0% and a precision of 93.37%. After performing metahueristic hyperparameter tuning with differential evolution algorithm and derived feature engineering we have achieved accuracy of 98.83% and 98.41% precision. This study serves as a valuable resource for healthcare professionals, researchers, and policymakers, providing essential insights into the strengths and limitations of various machine learning models in the context of breast cancer prognosis. © 2024 IEEE.

关键词： Decision trees

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A Hybrid Approach to Support the Detection of Autism Spectrum Disorder(ASD) through Machine Learning and Deep Learning Techniques 12

A Hybrid Approach to Support the Detection of Autism Spectru...

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12th IEEE International Conference on Advanced Computing, ICoAC 2023

作者： Manoj, Meghna Praveen, Joe I R Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350318210

Autism spectrum disorder is a significant contributor to developmental disabilities worldwide, and its estimated prevalence in India is more than 2 million people. Research has emphasized that early detection and treatment can help to improve a person with autism's level of language, communication, and overall well-being. The main objective is to automate the diagnosis process, compare and identify the best classifiers, and find out the most significant traits for supporting detection. We attempted to investigate the use of Logistic Regression, Decision Tree, Naive Bayes, Random Forest, Support Vector Machine, Linear Discriminant Analysis, K-Nearest Neighbors, XGBoost Classifier, Multi-Layer Perceptron for predicting if the person is susceptible to ASD. For the analysis, ASD data for toddlers, children, adolescents, and adults were employed, and the best classifier techniques were determined using measures like recall, precision, F-measures, etc. Feature engineering was performed to identify the most impactful and effective features of the datasets. We identified that LR and MLP models provide the best results in adult and toddler datasets. In case of adolescent data, LR classifier showed the highest performance whereas LR and XGBoost algorithms worked well for child dataset. The results obtained justify that ML and DL techniques showed promise in developing an improved system for supporting the detection of Autism. © 2023 IEEE.

关键词： Diseases

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Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

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computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within computer science and engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

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Video Anomaly Detection using Factorized Self-Attention Transformer 2

Video Anomaly Detection using Factorized Self-Attention Tran...

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2nd International Conference on Computational Intelligence and Network Systems, CINS 2024

作者： Karthik, R. Adithya, S. Shalmiya, P. Subramaniyaswamy, V. School of Computing SASTRA Deemed University Thanjavur India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798331504106

ISBN: (纸本)9798331504106

Recently, video anomaly detection has seen significant interest in computer vision for identifying unexpected events in video streams using computational cameras, which has quite important applications in video surveillance, security, safety, and industrial control. Existing methods heavily rely on handcrafted features and rule-based algorithms to detect such anomalies, but these models typically tend to fail to generalize scenarios. Recently, with the success of deep learning-based approaches, particularly deep convolutional and recurrent neural networks, state-of-the-art models have been proposed to exploit the capability of deep learning to learn complex patterns from raw video without handcrafted features. Although deep neural networks have been shown to be the suitable solution to automatically capture complex relationships, there are still issues with addressing a wide spectrum of scenarios and acquiring large, well-labelled training datasets. In this research, a transformer-based architecture is proposed for anomaly detection using labelled training data. A Video Vision Transformer (ViT) is used to extract complex visual patterns from video frames, while audio data is encoded as spectrograms and transformed into audio features using an audio transformer. The outputs of the transformers are fused using a cross-modal relation-aware network and fed to a robust temporal feature magnitude (RTFM) learning module, which provides an anomaly score and classifies the input as anomaly or normal. Furthermore, different evaluation metrics are used to measure how well the model works, highlighting its ability. The proposed model is effective since transformer architectures are capable of grasping rich temporal relationships. ©2024 IEEE.

关键词： Recurrent neural networks

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Smart Assistant for Visually Impaired People using Deep Learning Algorithms

Smart Assistant for Visually Impaired People using Deep Lear...

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Bipin, Parmar Bliss Abirami, S. Vellore Institute of Technology School of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350333602

Vision impairment has a profound influence on quality of life. This proposed work aims to work as an eye for the blind people. It will take in pictures as an input and interpret the details in the given picture as captions. The challenge with this system is that it requires greater accuracy in extracting information from images and associating it with captions because captions for the blind must be clear, concise, and reliable. Deep learning teaches computers to perform human-like tasks through multi-layer architectures. To address the eyesight challenge for the blind population, this study proposes a hybrid deep learning autoencoder ensemble, which is a combination of Convolution Neural Network (CNN) with Gated recurrent units (GRUs) along with a novel attention mechanism to extract the inherent features of the image that are most closely related to the caption. Here, the CNN serves as the encoder and GRU serves as the decoder. The proposed attention mechanism uses dense layers to draws attention to areas of the image and grasp more relevant features of the image associated with the caption. Flickr8K dataset is used to assess the suggested model. which contains 8091 images, each of which includes five captions, for a total of 40455 captions. The efficacy of the proposed CNN-GRU model with attention layer to selectively attend to various components of the image while generating each word of the caption enables it to outperform the existing approach by 10.59% accuracy improvement by perceiving better Bilingual Evaluation Understudy (BLEU) score. © 2023 IEEE.

关键词： Learning algorithms

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An Empirical Review for Character Recognition in Video Frames using Text Detection Algorithms 13

An Empirical Review for Character Recognition in Video Frame...

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13th International Conference on Cloud Computing, Data science and engineering, Confluence 2023

作者： Rajaboina, Nagendra Babu Prasad Sariki, Tulasi Kumar, Gunturu Bharadwaja Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665462631

The characters in the text image or video is having more information for recovery and indexing applications. The recognition of characters from the video text images is a difficult task due to the complex backgrounds, grey-scale values, illuminations, and sizes. Hence, various text detection algorithms like feature extracting algorithms including texture and region-based, machine learning, and deep learning approaches are implemented. Optical Character Recognition (OCR) is implemented in detecting the characters from the text frames and gives the text information including the location of the frame for text in the video. The Natural Language Processing (NLP) technique results in high performance in detecting the different text languages from the video frames. Convolutional Neural Network (CNN) algorithms are used in the feature selection process for efficient recognition of text characters from the images. The deep learning algorithms are used in textual regions in the frames of the video for detecting the text. The LSTM combined CNN networks are also implemented for extracting the features from the image for efficient and high-quality output text. © 2023 IEEE.

关键词： Learning algorithms

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Building ML Workflow for Malware Images Classification using Machine Learning Services in Leading Cloud Platforms

Building ML Workflow for Malware Images Classification using...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Jagati, Anika Subbulakshmi, T. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350323917

To meet the growing computational needs of machine learning, cloud services have provided a cost-effective alternative to fulfil the memory intensive task requirements. Cloud providers offer many benefits including more storage capacity, server access and high-end analytical capabilities. The abundant presence of various cloud providers makes it a tough choice for users and companies to select the best service provider to meet their needs. This paper aims to review and evaluate the top three cloud platforms;Amazon Web services, Google Cloud Platform and Microsoft Azure with respect to the various features and services provided by them by building a machine learning workflow on the platforms for an image classification task. © 2023 IEEE.

关键词： Machine learning

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