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EDRLNet: privacy preservation in online social networks via deep reinforcement learning and graph convolutional networks

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International Journal of Information Technology (Singapore) 2025年 1-10页

作者： Gowda, Nithish Ranjan Venkatesh Venugopal, K.R. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru India Associate Professor Department Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bangalore India University Visvesvaraya College of Engineering Bangalore University Bangalore India

Online social networks (OSN) have become extremely popular in the past few decades. Prominent OSN companies, such as Facebook and Twitter, control a huge amount of information on its users as well as interactions that may cause people to have major privacy concerns. The OSN information is combined with other online sources to reveal sensitive information. As a result, there is a great need to anonymize the OSN data before providing it to a third party. Thus, in this study, we propose a Deep Reinforcement Learning (DRL)-based rewiring operation that entails changing the relationships (edges) between nodes (users) without changing their degree to protect users’ privacy. To accomplish this, we introduce a novel technique for Enhanced DRL Networks called “EDRLNet” which combines an Optimized Policy Network (OPN) and Graph Convolutional Network (GCN) that preserves the essential characteristics of OSN data while anonymizing it to prevent privacy breaches. This approach employs a GCN to extract embeddings (features) from the edges and nodes. Furthermore, OPN applies the graph feature representation learned by the GCN model to policy learning. Additionally, the hyperparameters of OPN are optimized by using the Addax optimization algorithm (AOA). The suggested scheme’s performance is assessed using PageRank with mean-squared error (MSE), Root mean-squared error (RMSE), mean absolute error (MAE), Privacy score, Utility Score, Clustering coefficient, privacy and information loss. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Addax optimization algorithm Anonymization Deep reinforcement learning Graph convolutional network Online social network Privacy preservation

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Security Architecture of Cloud Computing and Its Case Study 3rd

Security Architecture of Cloud Computing and Its Case Study

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3rd International Conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

作者： Vaishnav, Anurag Gupta, Ram Ji Dwivedi, Pranav Kumar, Sumit Shrivastava, Vishal Pandey, Akhil Computer Science and Engineering Arya College of Engineering and IT Jaipur India

ISBN: (纸本)9789819737444

Cloud computing provides services that use more internet resources as needed. This helps users because it’s easy to use and affordable. But, when important apps and private data are moved to shared clouds, there can be security issues. Cloud-based setups have good and bad points for keeping data safe. This study looks at the main security problems in cloud computing. It uses a cloud computing structure to sort and find security issues at different levels. This study focuses on using cloud services and security issues to build connections between different areas. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cloud security

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Cardiovascular Disease Prediction Using Deep Learning 11

Cardiovascular Disease Prediction Using Deep Learning

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Khanna, Vaneet Punjab Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9798350350357

Cardiovascular disease (CVD), ahead of all other causes of death worldwide in this era. There is an immediate need for accurate, reliable, and practically applicable ways of early detection and treatment of diseases, and it connects a number of hazards for cardiovascular disease. One common method for analyzing massive amounts of historical information in healthcare is data extraction. To assist physicians with CVD prediction, they employ a number of data mining and deep learning (DL) techniques to navigate complex medical data. This review paper critically examines the application of deep learning techniques in predicting cardiovascular diseases, emphasizing data collection, preprocessing, model selection, performance metrics, and challenges. The data-driven nature of DL models allows for the analysis of diverse patient information, contributing to more accurate risk assessments. The review achieves by discussing the challenges and limitations, emphasizing the importance of collaborative efforts to connect DL's potential in enhancing cardiovascular disease prediction and improving patient outcomes. © 2024 IEEE.

关键词： Forecasting

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Identification of Epileptic Seizures using Recurrent Neural Networks and Time Series Transformer 7

Identification of Epileptic Seizures using Recurrent Neural ...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Rahul Chiranjeevi, V. Keerthana, H. Keerthana, G. Keerthana, S.P. Rajalakshmi Engineering College Computer Science and Engineering Chennai India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372816

In recent years epileptic seizures have become a major impact on large number of people worldwide. Over 60 million people have been affected globally due to this neurological condition. These seizures are caused by excessive discharge of electrical signals in the brain. To identify the excess discharge of signals, a device called electroencephalogram (EEG) is used. This device captures the signals by placing multiple sensors in scalp of the brain. However, the signals from the brain vary in time with different individuals thereby requiring more data. Also, differences are found in the signals between various stages of seizures. To overcome these issues and to detect the seizure automatically from EEG signal a novel Ensemble approach is proposed using Recurrent Neural Networks (RNN) and Time Series Transformer (TST). Raw EEG signals are pre-processed to remove undesirable noises from the obtained signals for further processing. The TST model was utilized to extract characteristics from EEG recordings. Furthermore, a recurrent neural network incorporating a long input layer, fully connected layer, activation layer was constructed in parallel to train the features, classify the EEG data, and identify the seizures. The effectiveness of the network was further examined and compared using various activation channels. The findings demonstrated that the suggested network could achieve a reasonably accurate prediction using a mere 18 channels. Experiments were carried out on the CHBMIT and Bonn datasets. Proposed method achieved an accuracy of 94.31% with an F-Score of 91.93%. Furthermore, classification of seizures has been improved compared to state of art methods. © 2024 IEEE.

关键词： Electroencephalography

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AI-Powered Dynamic Images: A New Frontier in Graphical Password Authentication 2

AI-Powered Dynamic Images: A New Frontier in Graphical Passw...

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

作者： Jananee, V. Ragava Krishnan, N.S. Rakesh, V.S. Rajalakshmi Engineering College Computer Science and Engineering Chennai India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

Graphical Password Authentication is an emerging and advanced technique in password authentication for hypothesis that provides a stronger and more secure password compared to the traditional text password, which is thus deemed superior. These methods of graphical authentication, however, have benefits that they have designed for current systems that still incorporate vulnerabilities like shoulder surfing, screen recording, brute force, and other attacks. The present research works on the existing graphical encryption systems that indicate the strengths and weaknesses the systems have. Enter a brand-new method-the AI-based dynamic image-which brings in more security for graphical passwords on authentication process as an authentication difference. It is an artificial intelligence back-end, in which with every access attempt, the picture is automatically varying but presents easy visible contents which give a higher level of enhanced security. © 2024 IEEE.

关键词： Visualization Scientific computing Heuristic algorithms Authentication Passwords Generative adversarial networks User experience Recording Security Artificial intelligence

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Personalized Recommender System for E-Commerce 2

Personalized Recommender System for E-Commerce

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

作者： Nagomiya, S. Sanjay, S. Shriram Kumar, A.N. Rajalakshmi Engineering College Computer Science and Engineering Chennai India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

In the fast-changing e-commerce world, personalization with regard to increasing user engagement and driving sales is becoming essential. As products and interactions grow voluminous, traditional recommender systems suffer from key challenges related to the cold-start problem, data sparsity, and offering static and repetitive recommendations that do not adjust for preferences' changes. These lead to irritation by way of inappropriate suggestion and inefficient shopping experience. In the midst of these challenges, this paper proposes a hybrid form of recommender system, in that it combines collaboration filtering with contentbased filtering, CNNs in analyzing images, and it also uses VADER, as well as Large Language Models, to determine the sentiment. Instead of analyzing only patterns in user behavior for collaborative filtering, this system suggests products liked by similar users. For content-based filtering, it outlines product attributes that will help suggest items about which the user has previously interacted. By integrating various methods into one, this system will always guarantee a higher accuracy level and diversity of the recommendations. Apart from that, CNN-based image analysis can further improve personalization with visually attractive product recommendations based on users' preferences, particularly in visually driven categories such as fashion and home decoration. This also sets up a sophisticated filtering of the recommendations based on sentiment analysis and favours products for which reviews are positive so that users get only high-quality suggestions. This is a hybrid method that overcomes two of the significant problems of traditional systems, namely the cold-start problem and data redundancy, through dynamic, context-aware product recommendations. It enhances user satisfaction, improves sales conversion rates, and provides a more entertaining shopping experience. © 2024 IEEE.

关键词： Collaborative filtering

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A Secure Framework for WSN-IoT Using Deep Learning for Enhanced Intrusion Detection

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computers, Materials & Continua 2024年第10期81卷 471-501页

作者： Chandraumakantham Om Kumar Sudhakaran Gajendran Suguna Marappan Mohammed Zakariah Abdulaziz S.Almazyad School of Computer Science&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India School of Electronics&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India Department of Computer Sciences and Engineering College of Applied ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The security of the wireless sensor network-Internet of Things(WSN-IoT)network is more challenging due to its randomness and self-organized *** detection is one of the key methodologies utilized to ensure the security of the *** intrusion detection mechanisms have issues such as higher misclassification rates,increased model complexity,insignificant feature extraction,increased training time,increased run time complexity,computation overhead,failure to identify new attacks,increased energy consumption,and a variety of other factors that limit the performance of the intrusion system *** this research a security framework for WSN-IoT,through a deep learning technique is introduced using Modified Fuzzy-Adaptive DenseNet(MF_AdaDenseNet)and is benchmarked with datasets like NSL-KDD,UNSWNB15,CIDDS-001,Edge IIoT,Bot *** this,the optimal feature selection using Capturing Dingo Optimization(CDO)is devised to acquire relevant features by removing redundant *** proposed MF_AdaDenseNet intrusion detection model offers significant benefits by utilizing optimal feature selection with the CDO *** results in enhanced Detection Capacity with minimal computation complexity,as well as a reduction in False Alarm Rate(FAR)due to the consideration of classification error in the fitness *** a result,the combined CDO-based feature selection and MF_AdaDenseNet intrusion detection mechanism outperform other state-of-the-art techniques,achieving maximal Detection Capacity,precision,recall,and F-Measure of 99.46%,99.54%,99.91%,and 99.68%,respectively,along with minimal FAR and Mean Absolute Error(MAE)of 0.9%and 0.11.

关键词： Deep learning intrusion detection fuzzy rules feature selection false alarm rate accuracy wireless sensor networks

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Segmentation of MR Spinal Images Using a 2 - Stage Semantic Framework

Segmentation of MR Spinal Images Using a 2 - Stage Semantic ...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Kumarasamy, Karpagam College of Engineering Guindy Computer Science and Engineering Chennai India

ISBN: (数字)9798331535476

ISBN: (纸本)9798331535476

Segmentation of Spine with the 2 - Stage Semantic framework involves the Vertebrae and the Inter-vertebral discs components of the spine, helps in the identification of the spinal disorders. The methods using the Convolutional Neural Network does not precisely identify the interdependencies within the Vertebral and the Inter-vertebral disc components of the spine. The proposed method helps achieve Segmentation of MR Spinal images in two stages. The Coarse-Grained Segmentation utilizes the 3D-Graph Convolution Segmentation Network and the Segmentation Enhancement is carried out using the 2D-ResUNet. In Coarse-Grained Segmentation, the Spatial Pooling technique transforms the image into a graph-based representation, where individual spinal structures are represented as nodes. This graph undergoes three layers of graph convolutions to extract Semantic Features. Following this, the Inverse pooling process is applied to transform the graph back into a Visual Semantic Feature map. Experiments are carried out using the T2-weighted volumetric MRI data of 215 individuals[1], and achieved DSC values 88.56 ± 3.36%, 87.59 ± 3.54%, and 88.07 ± 5.25% for the Vertebrae, IVDs and the complete set of nineteen spinal components respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning Approach to Predict the Structural Irregularity of Retinal Images 2

Deep Learning Approach to Predict the Structural Irregularit...

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

作者： Sharvesh, R. Sharan Ram, M. Anandhi, S. Rajalakshmi Engineering College Computer Science and Engineering Chennai India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

According to the World Health Organization, at least 2.2 billion people have impaired vision. It is crucial to note that in at least 1 billion of these cases, vision impairment could have been prevented or is yet to be addressed. There exists a wide range of retinal irregularities ranging from Glaucoma to Diabetic Retinopathy (DR), each with its symptoms and causative. With vision impairment booming in number, it is important to put a system in place which can digitize the optic tests done to detect various retinal irregularities. This paper strives to be a better iteration of the pre-existing models in place, in terms of accuracy and F1 score using processed retinal fundus images with Discrete Wavelet Transform (DWT) and Flower Pollination Algorithm (FPA) in Deep Learning (DL) based Ensemble Learning model using Convolutional Neural Networks (CNNs). Additionally, the integration of the output results into a cloud-based application for storage and statistical analysis makes it a better solution for easy retrieval, future reference and visualization. © 2024 IEEE.

关键词： Ophthalmology

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Machine Learning based Early Detection of Ongoing Cyber-Attacks 3

Machine Learning based Early Detection of Ongoing Cyber-Atta...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Bhagyalakshmi, A. Sruthi Laya, C.D. Yoga Preethikaa, A.M. Varsha, V. Easwari Engineering College Computer Science and Engineering Department India

ISBN: (纸本)9798350375190

The need for sophisticated detection and response systems to counteract cyber threats has grown in importance as the cybersecurity landscape changes. In order to improve cyberattack detection and response, this study proposes a novel combination of a Security Orchestration, Automation, and Response (SOAR) framework with a machine learning (ML) model. A Random Forest classifier was created and trained to distinguish between benign and potentially dangerous files using a dataset that included a variety of executable file properties. The classifier demonstrated a notable level of accuracy and precision. Then, a prediction API was established to enable real-time threat analysis by seamlessly integrating the learned machine learning model into a SOAR system. Through this integration, possible threats can be continuously identified by evaluating real-time data that has been retrieved from network traffic and endpoints. The SOAR system is activated when a suspicious file is found, which sets off a sequence of automatic events that include notifying security staff and starting response procedures. For a comprehensive and strong defence strategy, the system also integrates with already-in-use security solutions like firewalls and intrusion detection systems (IDS). The integration strengthens the overall cybersecurity posture by improving response time and increasing the effectiveness of cyberattack detection. This study presents the integrated approach's methodology, system architecture, and effectiveness, showcasing its potential as a comprehensive cybersecurity solution. © 2024 IEEE.

关键词： Cybersecurity

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