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An Adaptive Image Watermarking based on Bellman-Ford Algorithm 7

An Adaptive Image Watermarking based on Bellman-Ford Algorit...

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7th international conference on Future networks and distributed systems, ICFNDS 2023

作者： Ferik, Brahim Laimeche, Lakhdar Meraoumia, Abdallah Laouid, Abdelkader Alshaikh, Muath Chait, Khaled University Of Larbi Tebessi Tebessa Algeria Liap Laboratory University Of El Oued El Oued Algeria Department Of Computer Science College Of Computing And Informatics Saudi Electronic University Riyadh Saudi Arabia

ISBN: (纸本)9798400709036

Ensuring the integrity and authenticity of images is a paramount task in the digital multimedia context. The emergence of digital technologies necessitates the development of robust techniques to safeguard medical images against tampering or unauthorized alterations. One such crucial approach is watermarking, which involves embedding imperceptible and secure data within the images to facilitate subsequent verification and authentication. This paper proposes a novel medical image watermarking technique utilizing fingerprint data and a hybrid standard deviation-Bellman Ford algorithm for enhanced security and minimal distortion. The host image is divided into blocks, and the standard deviation quantifies texture variations. The Bellman-Ford algorithm then identifies optimal paths in the image graph corresponding to regions suitable for embedding. Our methodology diverges from existing techniques by enabling adaptive watermark embedding within textured domains while circumventing smooth regions. This study concentrates on the targeted objects within the image to uphold their integrity by inserting the adaptive watermark in the regions of interest. Extensive evaluation of various modalities proves the efficacy of the proposed method with an average Peak Signal Noise Ratio of 60.67dB and Structural Similarity Index of 0.9999, demonstrating marked improvements in imperceptibility over state-of-the-art techniques. The approach is efficient, with embedding and extraction times of 0.0094s and 0.0083s, respectively. © 2023 ACM.

关键词： Embeddings

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Addressing Semantic Similarity Attacks in Online Social networks

Addressing Semantic Similarity Attacks in Online Social Netw...

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Blockchain and distributed systems security (ICBDS), IEEE international conference on

作者： Amardeep Singh Monika Singh Computer Science & Engg. Dept. SVIET Banur India Computer Science & Engg. Apex Institute of Technology Mohali India

An efficient technique for preserving user privacy of users while publishing data is anonymization. Banks, Social Network service providers and hospitals are examples of data owners/stakeholders who anonymize their customers' data before releasing it in order to protect users' privacy. However, trustworthy information consumers still value anonymous data. Numerous anonymization models and techniques have been developed for releasing data while preserving user privacy. These models/algorithms de-identify user data, which is typically presented as graphs. Giving clear perspectives on the entire information privacy field, including recent anonymization research, tabular and SN data, is crucial. In this article, we proposed an approach for anonymization to address privacy issues in social networks specifically addressing semantic similarity attacks. Far as we are aware, our proposed technique is capable of resolving the issues of semantic similarity between sensitive attributes efficiently. The results of the proposed approach have been evaluated using the dataset of around 6000 users of a real-time social network viz. Twitter. Evaluation metrics of APL (Average Path Length) show that our technique can protect privacy and also maintain the utility of data while publishing it for public use.

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IoT Based Smart systems using Machine Learning (ML) and Artificial Intelligence (AI): Vulnerabilities and Intelligent Solutions 13

IoT Based Smart Systems using Machine Learning (ML) and Arti...

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13th international conference on Society and Information Technologies, ICSIT 2022

作者： Ahmed, Shaftab Ilyas, Mohammad Raja, M. Yasin Akhtar Software Engineering Department Bahria University Islamabad Pakistan College of Engineering and Computer Science Florida Atlantic University Boca RatonFL United States Center for Optoelectronics & Optical Communications University of North Carolina Charlotte NC United States

ISBN: (纸本)9781950492633

Internet of Things (IoT) has assumed great importance in technical and social domains due to desire of smart living and intelligent solutions for industrial operations, home automation and healthcare. The telecommunication networks provide all-time internet connectivity for the devices in physical systems and hand- held devices. The developments have made it easy to remain engaged on all time, anywhere basis, while users interact with one or more applications. Many smart devices may interact in the background resulting in event-driven intelligent activities raising alerts or recording status summary under a policy. The smart solutions are being shaped for the industry, transport, eHealthcare, eEducation and other daily life activities. IoT activities are autonomous and support dynamic Machine-to-Machine (M2M) communication. The challenges of hetrogeneity, dynamic variation in signal quality and large volume of data are being addressed through number of techniques. In this paper, we discuss IoT based smart system technologies, security, vulnerabilities and role of intelligent solutions using Machine Learning (ML) and Artificial Intelligence (AI). A crucial factor hindering the ongoing efforts for widespread IoT-adoption, is security. We propose the requirement of Standard security Framework (SSF) for platform independent and interoperable hardware/software modules in distributed networks domains. Trends to use Fog- and Edge-computing along with cloud applications has been also reviewed in the context to security and efficiency. © 2022 by the international Institute of Informatics and Systemics. All rights reserved.

关键词： Internet of things

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Deepfake Video Detection using Neural networks 1

Deepfake Video Detection using Neural Networks

引用

1st IEEE international conference on Blockchain and distributed systems security, ICBDS 2022

作者： Jiwtode, Mayur Asati, Aman Kamble, Shailesh Damahe, Lalit Yeshwantrao Chavan College of Engineering Department of Computer Technology Nagpur India Indira Gandhi Delhi Technical University for Women Department of Artificial Intelligence and Data Science Delhi India Yeshwantrao Chavan College of Engineering Head of Department of Computer Technology Nagpur India

ISBN: (数字)9781665428323

ISBN: (纸本)9781665428323

Over the past few years, free mobile application tools on Artificial Intelligence and deep learning have made it easy to create reliable face exchanges in a video called 'DeepFake' (DF) video that leaves a little hint of traces to check if its fake. Creating a computerized edited video has been demonstrated for quite a long time by actually taking advantage of enhanced visual effects. Recently, Artificial Intelligence has led to a rise in fake content and the ability to access free tools to create it. These purported AI-engineered media are normally called DeepFake(DF). Making a DF with a computerized AI tool is a simple job. However, with regards to identifying this DF, it's a major challenging dispute. Preparing the calculations and training the model to distinguish DF is difficult. The challenge to train an algorithm model to spot the DeepFake (DF) is not simple. We have tried recognizing DF with the use of CNN and RNN. The framework utilizes a CNN for feature extraction at the frame level. The model uses features extracted from the frame level to train the RNN, which then learns to classify videos according to their temporal inconsistencies. Anticipated results, when compared with a large number of hoax videos, were gathered from standard datasets. Using a simple architecture, we will show you how this errand can make your framework accurate. © 2022 IEEE.

关键词： Large dataset

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Network Intrusion Detection System Employing Big Data and Intelligent Learning Methods 4

Network Intrusion Detection System Employing Big Data and In...

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4th international conference on Artificial Intelligence and Speech Technology, AIST 2022

作者： Verma, Jyoti Bhandari, Abhinav Singh, Gurpreet Punjabi University Patiala Department of Computer Science and Engineering Punjab India Punjab Institute of Technology Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9781665499026

With the steadily increasing use of computer networks and the exponential growth of data generated from multiple sources, several other practitioners of security management solutions have been faced with the principal objective of providing privacy and security for big data. As a consequence, methods and techniques have been developed for learning to cope with ever-increasing data volumes. Network intrusion detection system is a key component of cyber security systems and is widely used. Big data has posed many challenges for selecting features that have not been addressed by conventional artificial Intelligence and machine learning practices. To overcome this challenge, it is necessary to improve feature selection to present more effective methods to cope with extremely high dimensional space in big data. To address the challenges associated with feature selection in big data analytics and to foster investigations in this clan, we present an analysis of the feature selection challenges encountered by various researchers over the last decade. We propose a system for an intelligent NIDS feature selection model that allows its use as a big data engine. Intelligent NIDS features are selected using a combination of deep learning models and big data methods. Several tools are suggested for relevant data streaming and sustained storage systems in a distributed environment, which include Kafka and Hadoop (HDFS). The Intelligent NIDS Framework experimental procedure comprises a system with normal and malicious traffic, and the NIDS model would perform on cluster frameworks such as the Apache Stream Processing Cluster and the Apache Hadoop Cluster. © 2022 IEEE.

关键词： Big data

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Construction and Alignment Features of English Corpus based on Particle Swarm Optimization Algorithm

Construction and Alignment Features of English Corpus based ...

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distributed systems, computer networks and Cybersecurity (ICDSCNC), international conference on

作者： Wei Shang Xin Li School of Humanities Shan Dong Agriculture and Engineering University Jinan China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

With the continuous advancement of natural language processing technology, building a high-quality corpus has become a hot topic in current research. However, the traditional corpus construction method requires a lot of manual intervention, which is time-consuming and laborious, and cannot meet the requirements of large amounts of data. To overcome these limitations, this paper applies an automated method based on the particle swarm optimization (PSO) algorithm. First, the scale and text type of the corpus are determined, and then an adaptive function suitable for text similarity evaluation is designed and evaluated. The method mainly consists of three parts: initialization of the particle swarm, updating of particle positions, and selection of the best solution. Through experimental evaluation, we found that in a large sample scenario, the perplexity of the PSO algorithm can be stabilized at around 2, showing good robustness. In addition, the evaluation using the BLEU (bilingual evaluation) score also reached a high score of 0.99, which indicates the high quality of the alignment. The research results will further verify the feasibility of the PSO algorithm in the automatic construction and alignment of the English corpus, and provide a new and efficient method for corpus construction in natural language processing. Through automated methods, manual participation can be significantly reduced, construction efficiency can be improved, and alignment quality can be maintained or improved.

关键词： Costs Buildings Manuals Stability analysis Robustness computer networks Machine translation Particle swarm optimization computer security

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The Probability of Timely Redundant Service in a Two-Level Cluster of a Flow of Requests that is Heterogeneous in Functionality and Allowable Delays 25th

The Probability of Timely Redundant Service in a Two-Leve...

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25th international conference on distributed computer and Communication networks, DCCN 2022

作者： Bogatyrev, V.A. Bogatyrev, S.V. Bogatyrev, A.V. Department Information Systems Security Saint-Petersburg State University of Aerospace Instrumentation Saint Petersburg Russia Yadro Cloud Storage Development Center Saint Petersburg Russia ITMO University Saint Petersburg Russia

ISBN: (纸本)9783031306471

Currently, the development of methods for the synthesis and analysis of fault-tolerant computer clusters designed to service a flow of requests that is heterogeneous in functionality and acceptable delays in queues is relevant. The clusters under consideration assume a hierarchical structure with a multi-stage request service. At the first stage of servicing a request, its primary processing is performed in a general-purpose server, and at the second stage, it is processed on functionally oriented servers. The options for building clusters with and without combining servers for various purposes into groups (cluster nodes) are considered. The set of two-level cluster nodes includes a general-purpose server, as well as a complete or incomplete set of functional servers. Incomplete functional configuration of cluster nodes can be laid down during design or formed as a result of system degradation with the accumulation of failures in it. When designing a system, the incomplete functionality of two-level nodes can be caused by the heterogeneity of the cluster load for the execution of various functional requests, taking into account the restrictions on the allowable delays in their service. For the considered options for building clusters, models are proposed for estimating the probability of timely servicing of requests of various functionality and the allowable waiting time, taking into account the multiplicity of their reservation depending on the traffic intensity and the accumulation of delays in the queues of servers involved in various stages of service. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Structural design

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Student Behavior Detection on Campus using Ghost Crayfish Optimization Algorithm and Bi Directional Long Short-Term Memory

Student Behavior Detection on Campus using Ghost Crayfish Op...

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distributed systems, computer networks and Cybersecurity (ICDSCNC), international conference on

作者： Meng Jiang Xin Yang Peng Jiang Fengbo Ma Shaojun Zhou Beijing Institute of Technology Zhuhai China Zhuhai Sports School Zhuhai China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Student engagement is a supple and dynamic concept encompassing behavioural and cognitive involvement. To help instructors better understand how student engage with several classroom activities, it is crucial to predict their level of participation. However, were difficult to interpret, making it challenging for educators or administrators to inefficiently learn pattern. This paper, proposed Ghost Crayfish Optimization Algorithm (GCOA) for feature selection and Bi Directional Long Short-Term Memory (Bi-LSTM) for student behavior classification ensures better accuracy. The GCOA effectively reduce the dimensionality of data, selecting only most relevant feature while Bi-LSTM technique processing sequential data, handle long-term dependencies and gradient vanishing issue. Initially, data is obtained from Open University Learning Analytics Dataset (OULAD) dataset and pre-processing stage involves missing values, normalization which is efficiently handle missing values and scale feature. The proposed GCOA and Bi-LSTM technique achieves better accuracy 94.26%, precision of 94.75% and recall of 94.12% on OULAD dataset when compared to existing techniques Artificial Neural Network (ANN), Fully Connected Network (FCN) and Long Short-Term Memory (LSTM) approach.

关键词： Accuracy Heuristic algorithms Feature extraction Prediction algorithms computer networks Classification algorithms computer security Long short term memory Optimization

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Enhancing Peer-to-Peer Network security using 3D Convolutional Autoencoder based Anomaly Detection

Enhancing Peer-to-Peer Network Security using 3D Convolution...

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Intelligent systems and Computational networks (ICISCN), international conference on

作者： Rana Veer Samara Sihman Bharattej R Zaid Ajznblasm E. G. Satish Adlin Sylvia A M. Devi Department of Computer Science Saint Leo University FL USA Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering and Technology India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In this era, Peer-to-Peer (P2P) networks have developed as a revolutionized network for data distribution and alternate to traditional client-server network. The features of P2P networks allow a distributed group of users to stake resources in the form of digital content or information. However, P2P networks lack in controlling access and it has limited security. In this research, Three Dimensional-Convolutional Autoencoder (3D-CA) is proposed to improve the security in P2P network. Initially, the data is obtained from University of New South Wales Network Behavior 2015 (UNSW-NB15) dataset and then preprocessed with data cleaning to ensure data quality. After that, the P2P autoencoder architecture is employed to learn the normal patterns which employs the detection in P2P network. Finally, the proposed 3D-CA is trained to detect the anomalies based on reconstruction error between input node and reconstructed pattern. From the results, the proposed 3D-CA model attained better outcomes compared to existing K-means algorithm in terms of accuracy, precision as well as F1-score by achieving 0.764, 0.90 and 0.80 respectively.

关键词： Accuracy Three-dimensional displays Scalability Autoencoders Telecommunication traffic Reinforcement learning Probabilistic logic Cleaning Peer-to-peer computing Anomaly detection

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A Secure and Authentication Scheme to Preserve the Privacy of Electronic Health Records in the Healthcare System

A Secure and Authentication Scheme to Preserve the Privacy o...

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2022 Iraqi international conference on Communication and Information Technologies, IICCIT 2022

作者： Hamed, Naba M. Yassin, Ali A. Computer Science and Information Technology Computer Information Systems Basrah Iraq College for Pure Sciences Department of Computer Science Basrah Iraq

ISBN: (纸本)9781665472203

Electronic Healthcare Records (EHRs), networks, and cloud computing have significantly revolutionized the traditional healthcare system. Secure remote user authentication is an important component of the healthcare system for authenticating an individual's identification via an unsecure communication network. The roles are distributed among the system's users, contrasting between administrator and data entry within certain rules and policies. It is possible to restrict access to key user activities (addition, deletion, and modification) based on a user's position in the healthcare system. As a result, security issues, hostile attacks, and privacy concerns are viewed as key challenges in the healthcare system. Using role-based access control and smart multi-factor authentication, this research provides a privacy-preserving solution to solve the issues in earlier approaches. The proposed scheme is based on crypto hash functions and asymmetric cryptosystems by using chameleon digital signatures. The proposed scheme carried out two exhaustive formal security proofs for the work that was provided. Using scyther, a formal security tool, we present a secure user authentication technique in the healthcare system that overcomes the familiar security problems and ensures the security of the proposed scheme and informal analysis. Our proposed scheme focuses on two main components: users and administrators. The administrator is considered to be the heart of the system. Therefore, any information that is stolen from them poses a threat to the entire system and all its parts. Compared to others, the proposed scheme more secure. By the way, our work gains many potentials to protect users account gains malicious attacks. © 2022 IEEE.

关键词： Hash functions

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