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检索条件"任意字段=14th International Conference on Distributed Computing Systems"

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14th international conference on Applications and Techniques in Information Security, ATIS 2024

14th International Conference on Applications and Techniques...

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14th international conference on Applications and Techniques in Information Security, ATIS 2024

ISBN: (纸本)9789819797424

the proceedings contain 24 papers. the special focus in this conference is on Applications and Techniques in Information Security. the topics include: Optical Neural Networks – A Strategy for Secure Quantum computing;guarding Against Quantum threats: A Survey of Post-Quantum Cryptography Standardization, Techniques, and Current Implementations;cryptographic Distinguishers through Deep Learning for Lightweight Block Ciphers;detection and Mitigation of Email Phishing;securing Digital Forensic Data Using Neural Networks, Elephant Herd Optimization and Complex Sequence Techniques;Design of Image Encryption Technique Using MSE Approach;Low Latency Binary Edward Curve Crypto Processor for FPGA Platforms;Augmenting Security in Edge Devices: FPGA-Based Enhanced LEA Algorithm with S-Box and Chaotic Functions;machine Learning Approach for Malware Detection Using Malware Memory Analysis Data;DDOS Attack Detection in Virtual Machine Using Machine Learning Algorithms;an Unsupervised Method for Intrusion Detection Using Novel Percentage Split Clustering;HATT-MLPNN: A Hybrid Approach for Cyber-Attack Detection in Industrial Control systems Using MLPNN and Attention Mechanisms;silent threats: Monitoring Insider Risks in Healthcare Sector;Enhanced Deep Learning for IIoT threat Intelligence: Revealing Advanced Persistent threat Attack Patterns;Adaptive Data-Driven LSTM Model for Sensor Drift Detection in Water Utilities;Enhancing FGSM Attacks with Genetic Algorithms for Robust Adversarial Examples in Remote Sensing Image Classification systems;GAN-Enhanced Multiclass Malware Classification with Deep Convolutional Networks;IOT Based Locker Access System with MFA & Remote Authentication;a Secure Authentication Scheme between Edge Devices Using Hypergraph Hashing Technique in Iot Environment;enhancing Access Control and Information Sharing in Cloud IoT with an Effective Blockchain-Based Authority System.

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Parkinson’s Disease Prediction Using Spectrograms and Deep Belief Networks

Parkinson’s Disease Prediction Using Spectrograms and Deep ...

引用

international conference on Communication systems and Network Technologies (CSNT)

作者： Sandeep C S Martin Margala School of Computing and Informatics University of Louisiana at Lafayette United States of America

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

this research presents a way to forecast Parkinson's disease in the early stages by means of spectrogram investigation and deep belief networks (DBNs). the aforesaid way concentrates on the use of spectrograms in order to get the best results and the extraction of innovative features and patterns from the signal of the speech or motor, which is indicative of Parkinson's disease. By using DBNs, which is a resilient deep learning mechanism, the system has the good feature of learning deep representations of the data, which makes the detection and classification accurate. the coupling of spectrograms with DBNs gives the model perspective to notice even the less severe differences in the input signals which are often missed out by traditional methods. the outlined aggregate demonstrates a prologue to a system that would be an accurate and automated solution to the problem, which in the end could be a help to healthcare professionals in a timely procurement of intervention and treatment. the proposed model makes clear the the ability of advanced machine learning techniques to solve hard neurological disorders and enhance patient outcomes.

关键词： Neurological diseases Procurement Accuracy Parkinson's disease Feature extraction Motors Regulation Slabs Spectrogram Medical diagnostic imaging

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A Novel distributed Economic Dispatch Method for Smart Grid with Time-Varying Feedback Gain 8th

A Novel Distributed Economic Dispatch Method for Smart Grid...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Guo, Hui Li, Xue Yang, Junlin Tian, Runze Shanghai University Shanghai200444 China Shanghai University of Finance and Economics Shanghai200433 China

ISBN: (纸本)9789819602315

the feedback gain in the traditional economic dispatch consensus algorithm is a fixed value, which leads to a slow convergence speed. To solve the problem, a novel distributed economic dispatch method for smart grid with time-varying feedback gain is proposed in this paper. First, by adding the direct cost, overestimation cost and underestimation cost of wind power to the objective function, an economic dispatch model considering wind power uncertainly is established, which can be flexibly scheduled under random wind speeds. Second, an improved consensus algorithm with time-varying feedback gain is proposed to solve the economic dispatch problem, which provides a design method of dynamic feedback gain instead of a fixed value and improves the convergence speed. Finally, several case studies are carried out in the IEEE 14-bus system to illustrate the effectiveness and superiority of the proposed method. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Windmill

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16th international conference on Mobile systems and Pervasive computing, MobiSPC 2019, 14th international conference on Future Networks and Communications, FNC 2019, 9th international conference on Sustainable Energy Information Technology, SEIT 2019

16th International Conference on Mobile Systems and Pervasiv...

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16th international conference on Mobile systems and Pervasive computing, MobiSPC 2019, 14th international conference on Future Networks and Communications, FNC 2019, 9th international conference on Sustainable Energy Information Technology, SEIT 2019

the proceedings contain 112 papers. the topics discussed include: towards an actor-based approach to design verified ROS-based robotic programs using Rebeca;the choice of the optimal energy-saving technology redundant power steam turbines during the passage of the daily schedules of power consumption gaps;structure model and means of a smart public transport system;simulation, validation and FPGA implementation of a ring oscillator sensor for thermal management and monitoring;software-defined security controller-based end-to-end packet key security management;design and implementation of CPPS and edge computing architecture based on OPC UA server;and the recommendation system for cyclists LvivBicycleMap.

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Proceedings - 14th international Symposium on Pervasive systems, Algorithms and Networks, I-SPAN 2017, 11th international conference on Frontier of Computer Science and Technology, FCST 2017 and 3rd international Symposium of Creative computing, ISCC 2017

Proceedings - 14th International Symposium on Pervasive Syst...

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14th international Symposium on Pervasive systems, Algorithms and Networks, I-SPAN 2017, 11th international conference on Frontier of Computer Science and Technology, FCST 2017 and 3rd international Symposium of Creative computing, ISCC 2017

ISBN: (纸本)9781538608401

the proceedings contain 176 papers. the topics discussed include: lessons learned in design and delivery of an introductory styling MOOC;machine learning approach to task ranking;coercion resistance in a practical secret voting scheme for large scale elections;an evaluation method of data valuation based on analytic hierarchy process;more on the inverse problem of Steiner Wiener index;probabilistic frequent itemsets mining based on expectation bound over uncertain database;on detecting partially occluded faces with pose variations;grid-based indexing and search algorithms for large-scale and high-dimensional data;image segmentation based on exponential kernel function;a personnel identification method based on the regional division of a classroom image;accurate camera calibration using iteratively optimized algorithm of control points;and a novel fish image retrieval method based on saliency spatial pyramid.

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Proceedings - 18th IEEE international conference on High Performance computing and Communications, 14th IEEE international conference on Smart City and 2nd IEEE international conference on Data Science and systems, HPCC/SmartCity/DSS 2016

Proceedings - 18th IEEE International Conference on High Per...

引用

18th IEEE international conference on High Performance computing and Communications, 14th IEEE international conference on Smart City and 2nd IEEE international conference on Data Science and systems, HPCC/SmartCity/DSS 2016

ISBN: (纸本)9781509042968

the proceedings contain 212 papers. the topics discussed include: fast community detection in large weighted networks using GraphX in the cloud;bare-metal container: direct execution of a container image on a remote machine with an optimized kernel;an experimental study of redundant array of independent SSDs and Filesystems;WeChat text and picture messages service flow traffic classification using machine learning technique;a node stability-based multipath routing protocol in heterogeneous ad hoc networks;SpongeNet+: a two-layer bandwidth allocation framework for cloud datacenter;SAWS: selective asymmetry-aware work-stealing for asymmetric multi-core architectures;managing performance degradation of collocated virtual machines in private cloud;a novel automated GUI testing echnology based on image recognition;periodic scheduling of deadline-constrained bandwidth reservations for scientific collaboration;leveraging locality to boost the update performance of network-coding-based storage systems;when video streaming meets named data networking: a case study;predicting the bursts of data access streams by filtering correlated I/Os;congestion-aware adaptive routing with quantitative congestion information;accelerating range query execution of in-memory stores: a performance;a content store-based module for congestion control algorithms of named data networking;a fast, scalable SLINK algorithm for commodity cluster computing exploiting spatial locality;PCAFP for solving CNOP in double-gyre variation and its parallelization on clusters;and an improved community detection algorithm based on the edge-graph of network under the spark environment.

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Dynamic Forensic Analysis of CryptBot Malware

Dynamic Forensic Analysis of CryptBot Malware

引用

international conference on Communication systems and Network Technologies (CSNT)

作者： Rizwan Ur Rahman Abanteeka Acharya Sanjipan Deb Priyanshi Panchal School of Computing Science Engineering and Artificial Intelligence (SCAI) Vellore Institute of Technology Bhopal Bhopal Madhya Pradesh India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

CryptBot, an infostealer malware first identified in 2019, poses a serious threat by stealing sensitive data, including login credentials and cryptocurrency wallet details, and transmitting them to command-and-control servers. It spreads through phishing emails, malicious ads, and pirated software, using advanced evasion techniques to remain undetected. this study employs dynamic malware analysis to observe CryptBot’s real-time behavior, including its data exfiltration methods, persistence mechanisms, and evasion strategies. Key findings reveal tactics such as subprocess creation, process renaming, and temporary file usage to facilitate data theft. To mitigate such threats, a multi-layered security approach is essential, combining dynamic, static, and hybrid analysis methods with security measures like intrusion detection, encryption, and multi-factor authentication. Additionally, user education on phishing and software piracy is crucial in preventing infections. this research enhances understanding of CryptBot’s operations and proposes detection and mitigation strategies. Future work includes developing advanced sandbox environments and integrating machine learning to improve threat detection and response.

关键词： Command and control systems Multi-factor authentication Phishing Prevention and mitigation Malware Virtual machines threat assessment Real-time systems Security Servers

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Deep Learning Techniques for Intrusion Detection systems in Healthcare Environments

Deep Learning Techniques for Intrusion Detection Systems in ...

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international conference on Communication systems and Network Technologies (CSNT)

作者： Mahfooz Alam Zubair Ashraf Priya Singh Bishwajeet Pandey Kaleemur Rehman Laura Aldasheva Department of Master of Computer Applications G. L. Bajaj Institute of Technology and Management Greater Noida India College of Computing and Information Technology University of Bisha Bisha Kingdom of Saudi Arabia Department of Computer Science Institute of Engineering & Technology Dr. B. R. Ambedkar University Khandri Agra India Department of CSE-AI G. L. Bajaj Institute of Technology and Management Greater Noida India Department of IS and CS Astana IT University Astana Kazakhstan

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Nowadays, healthcare systems face ever-increasing cyber security threats due to the sensitive nature of patient data and the proliferation of IoT-enabled medical devices. Traditional Intrusion Detection systems (IDS) struggle to adapt to healthcare environments' dynamic and heterogeneous data. Deep learning techniques, with their ability to analyze complex and high-dimensional data, have emerged as a promising approach to enhancing IDS performance. this paper explores the application of deep learning methods for intrusion detection in healthcare, focusing on their advantages, challenges, and practical implementations. Topics include neural network architectures, feature engineering for medical and network data, and real-time intrusion detection in resource-constrained environments. the goal is to provide a roadmap for leveraging deep learning to ensure robust and scalable security for healthcare systems.

关键词： Deep learning Performance evaluation Medical devices High dimensional data Neural networks Intrusion detection Medical services Real-time systems Security Long short term memory

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Classification of Cardiovascular Diseases through Retinal Fundus Images using Deep Learning

Classification of Cardiovascular Diseases through Retinal Fu...

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international conference on Communication systems and Network Technologies (CSNT)

作者： Md. Kayser Ahmed Hridoy Sabyasachi Roy Barman R Murugan Department of ECE National Institute of Technology Silchar Silchar Assam India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Cardiovascular disease (CVD) is one of the most significant global health concerns, necessitating early detection for effective treatment. Traditional diagnostic tools such as electrocardiograms (ECG), magnetic resonance imaging (MRI), blood tests, and coronary angiography are commonly used but are often invasive, costly, and inaccessible in resource-constrained settings. To overcome these challenges, this study employs a unique dataset of retinal fundus images to propose a novel, non-invasive, and economical approach for classifying CVD risk using deep learning techniques. Convolutional Neural Networks have demonstrated exceptional promise in recognizing disease abnormalities in the optic nerve and retinal vasculature. this study explores the advancements in CVD risk prediction and demonstrates the efficacy of retinal fundus imaging as a diagnostic tool. By automatically identifying detailed retinal features such as vasculature, microaneurysms, hemorrhages, exudates, and the optic disc, deep learning models offer superior efficiency when compared to traditional approaches, supporting early risk prediction and timely intervention.

关键词： Deep learning Magnetic resonance imaging Optical computing Predictive models Optical fiber networks Retina Optical imaging Cardiovascular diseases Convolutional neural networks Image classification

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DDOS Attack Detection in Virtual Machine Using Machine Learning Algorithms 14th

DDOS Attack Detection in Virtual Machine Using Machine Learn...

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14th international conference on Applications and Techniques in Information Security, ATIS 2024

作者： Lokeshwar, K. Sumathi, M. School of Computing SASTRA Deemed to Be University Tamil Nadu Thanjavur India

ISBN: (纸本)9789819797424

Cloud computing is advancing technology that provides scalability and accessibility to everyone at low prices. Similarly, everyone can access and use the cloud data from anywhere. this widespread service accessibility leads to security issues. In the digital landscape, distributed denials of service (DDoS) attacks are the most prevalent security threat. these attacks aim to disrupt the service availability of a target system or network with a flood of traffic, rendering it inaccessible to legitimate users. the proposed work leverages a virtual machine-based cloud environment to defend against DDoS attacks. the proposed work objective is to detect DDoS attacks swiftly and accurately to mitigate their impact. To achieve this goal, the machine learning algorithms such as Naive Bayes (NB), K-Nearest Neighbours (KNN), random forest (RF) and decision tree (DT) algorithms have been used in the proposed work to analyze the large number of application-layer packets. To detect the DDoS attacks, the proposed work is implemented in Wireshark for analyzing incoming packets. Compared to NB, KNN and DT models, the RF model achieved a comprehensive and highest detection accuracy rate of 99.3%. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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