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Cutting Edge: Privacy Analysis of the ETSI MEC APIs and the Potential Vulnerabilities 11

Cutting Edge: Privacy Analysis of the ETSI MEC APIs and the ...

引用

11th IEEE international conference on Cloud networking (CloudNet)

作者： Fraunholz, Daniel Schoerghofer-Vrinssen, Richard Koenig, Hartmut Reti, Daniel ZITiS Commun Dept Munich Germany DFKI Intelligent Networks Dept Kaiserslautern Germany

ISBN: (数字)9781665486279

ISBN: (纸本)9781665486279

New cellular networks like 5G and 6G put novel methods and technologies forth which sustainably change the mobile communication domain. Edge cloud computing is one of these emerging technologies providing computational resources at the edge of the network in closer proximity to end users. It promises significantly reduced network latencies, better load balancing, and more precise optimizations through information about the states of the access networks provided by the cloud infrastructure, e.g., radio conditions, location information, and others. the European Telecommunications Standards Institute (ETSI) has summarized and standardized the requirements for a multi-access edge computing (MEC) architecture as basis for future implementations. Among others, the standard defines a large number of interfaces between the various components and to the environment. It also formulates security requirements for these interfaces. However, the implementation details are usually left to the platform operators. In this paper, we present an in-depth analysis of the privacy properties of these interfaces and point out several potential privacy vulnerabilities. We describe three possible attacks that exploit these vulnerabilities. For our analysis, we use a simulative edge computing sandbox environment that has been developed by ETSI to investigate the interfaces. As a conclusion, we formulate recommendations on how data protection requirements can be met in future MEC implementations.

关键词： 5G edge cloud multi-access edge computing privacy vulnerabilities privacy attack vectors

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distributed Machine Learning through Transceiver Competitive Connectivity of Remote computing Systems 11

Distributed Machine Learning through Transceiver Competitive...

引用

11th international Scientific conference on Computer Science, COMSCI 2023

作者： Slavov, Danail Hristov, Vladimir Slavova, Anastasiya Technical University of Sofia Faculty of Automatics Department of Electrical Motion Automation Systems Sofia Bulgaria

ISBN: (纸本)9798350325256

An architecture is built for sequential data transmission among individual machines participating in a decentralized distributed machine learning of a convolutional neural network for image classification. Simultaneously running multiple replicas of a machine learning task using TCP communication protocol has been successfully implemented. A unification of the final result of each epoch until the end of the training session is performed on each of the participating machines in the training process. A working image classification model is obtained on all machines involved in the process. the results from the experiments performed herein show a 63% improvement in training speed for the fastest configuration with only a 3% drop in accuracy compared to the most accurate one. the study therefore confirms that the use of distributed learning systems can have significant benefits for the AI researchers and practitioners. © 2023 IEEE.

关键词： Machine learning

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Adapting to Evasive Tactics through Resilient Adversarial Machine Learning for Malware Detection 18

Adapting to Evasive Tactics through Resilient Adversarial Ma...

引用

18th INDIAcom;11th international conference on computing for Sustainable Global Development, INDIACom 2024

作者： Krishna, G. Bala Sravan Kumar, G. Ramachandra, Mummadi Sampurnima Pattem, K. Rani, D. Sandhya Kakarla, Geeta Cvr College of Engineering Department of Computer Science Engg. Rangareddy Ibrahimpatnam India Cvr College of Engineering Department of Computer Science Engg.-Cyber Security Rangareddy Ibrahimpatnam India Sreenidhi Institute of Science and Technology Department of Computer Science Engg. Hyderabad India Sreenidhi Institute of Science and Technology Department of Computer Science Engg. Cyber Security Hyderabad India

ISBN: (纸本)9789380544519

this paper presents the Adaptive Resilience-based Convolutional Network (ARCNet), a sophisticated machine learning framework specifically designed to detect advanced, evasive malware. ARCNet combines convolutional and recurrent neural networks, making it highly adaptable to changing cyber threats. Its core components, the Adversarial Learning Module (ALM), Predictive Analysis Engine (PAE), and Dynamic Adaptation System (DAS), significantly boost its detection power. Tests using a synthetic dataset show ARCNet's superiority over traditional models like the Support Vector Machine (SVM). It achieved 95.2% accuracy under normal conditions (compared to SVM's 89.4%) and maintained 92.5% accuracy even during adversarial attacks (against SVM's 80.3%). Notably, ARCNet's detection rates improved from 78.5% to 86.7% in five months after integrating the DAS. these results confirm ARCNet's efficiency in tackling complex malware challenges, contributing greatly to cybersecurity. the study underscores the importance of evolving and enhancing machine learning methods to keep pace with the rapidly changing landscape of cyber threats. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Block chain Blockchains Challenges of Block chain Consensus algorithm Data privacy Digital signatures distributed ledger Faces Organizations Smart contracts

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Workload Prediction for Edge computing 24

Workload Prediction for Edge Computing

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25th international conference on distributed computing and networking, ICDCN 2024

作者： Shah, Kavish Gandhi, Ayush Gandhi, Kalgi Bhise, Minal Distributed Database Group Gujarat Gandhinagar India

ISBN: (纸本)9798400716737

In large-scale edge systems, along with the number of edge devices, the amount of data that needs to be processed has been increasing. Edge systems have limited processing power, memory, and network bandwidth. For such systems, handling resource utilization and data management during query processing is tricky. Moreover, joins being the costliest operations, when accelerated, can improve query processing substantially. the state-of-the-art technique for accelerating hash join, Column Imprint-Hash Join, is a data-aware approach which uses equi-height binning. Proposed Predicted Workload-Aware Column Imprint-Hash Join enhances the query performance and addresses real-time query processing by predicting the upcoming workload in advance. the algorithm is demonstrated on the TPC-H benchmark dataset. It shows an improvement of about 10% for the Percentage of Cachelines Read over CI-HJ, making the algorithm energy-efficient. An overall gain in Query Execution Time of 11% is observed. Although, Algorithm Execution Time shows an overhead of 6% as compared to the state-of-the-art which is due to an additional prediction phase. the proposed technique showcases the effectiveness of query prediction in optimizing edge system performance. © 2024 ACM.

关键词： Forecasting

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DDoS Attack Detection on MQTT Protocol Using Semi-supervised DBSCAN and Support Vector Machine Model 11

DDoS Attack Detection on MQTT Protocol Using Semi-supervised...

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11th international conference on Information and Communication Technology, ICoICT 2023

作者： Ikhsanudin, Muhammad Suryani, Vera Pahlevi, Rizka Reza Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350321982

Internet of things (IoT) is a system of connected objects with sensors, software, control systems and protocols. One of the protocols widely used in IoT is Message Queue Telemetry Transport (MQTT). Considering that users of these IoT devices can control their devices from anywhere makes them susceptible to various types of attacks. distributed Denial of Service (DDoS) is a common attack vector in the IoT. Among the methods that can be applied for identifying this attack is machine learning. In previous research, DDoS detection was accomplished using a singular SVM. the precision and f1-score generated by this solitary SVM are still inadequate. this research combines SVM with other machine learning techniques in an effort to enhance SVM's accuracy and f1-score. In this research, semi-supervised DBSCAN and SVM models were utilized. We use three datasets in this research, namely IoTID20, simulation, and CICDDOS2018. the proposed model has the ability to detect DDoS attacks with a 99.6% accuracy, 99.6% f1-score, and 0.8% false alarm rate, respectively. © 2023 IEEE.

关键词： Internet of things

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A Cloud-Agnostic Serverless Architecture for distributed Machine Learning 11

A Cloud-Agnostic Serverless Architecture for Distributed Mac...

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11th IEEE/ACM international conference on Big Data computing, Applications and Technologies, BDCAT 2024

作者： Predoaia, Ionut Garcia-Lopez, Pedro University of York United Kingdom Universitat Rovira i Virgili Spain

ISBN: (纸本)9798350367300

Serverless computing has shown vast potential for big data analytics applications, especially involving machine learning algorithms. Nevertheless, little consideration has been given in the literature to cloud-agnostic serverless architectures that leverage existing parallel implementations of machine learning algorithms. this work bridges this gap by proposing a multicloud serverless architecture for distributed machine learning, that enables machine learning engineers without cloud computing expertise to effortlessly port already implemented parallel machine learning algorithms to serverless, whilst overcoming vendor lock-in. In this work, two stateful machine learning algorithms have been ported to serverless, k-means clustering and logistic regression. the serverless implementation of k-means provided superior performance and scalability compared to a serverful implementation when using a number of workers that is equal to or slightly lower than the total number of vCPUs available on the VM running the serverful implementation. Additionally, it achieved an 87-fold speedup compared to a sequential implementation. Moreover, two storage designs of the shared state will be proposed for the serverless implementations, one that requires locks for updating the shared state, and another that is lock-free. Our experimental evaluation demonstrates that the performance of the lock-free serverless implementation of k-means declines with the increase in the number of clusters. © 2024 IEEE.

关键词： Adversarial machine learning

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Comparative Analysis of the Quality of *** and Traveloka Applications Using the E-Servqual Method on User Satisfaction 11

Comparative Analysis of the Quality of *** and Traveloka App...

引用

11th international conference on Information and Communication Technology, ICoICT 2023

作者： Al-Fajri, Fathoni Utomo, Rio Guntur Al Makky, Muhammad Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350321982

Currently, with the development of online ticket booking technology, there is a higher demand for it among the Indonesian people. therefore, the authors are interested in examining the quality comparison of two online ticket sales applications, namely *** and Traveloka, using the E-Servqual method with eight dimensions of research variables Efficiency, Fulfillment, Reliability, Privacy, Responsiveness, Compensation, Contact, and Security. the research was conducted for one month with a total of 400 respondents. the results obtained from the respondents indicate that the *** application better meets user expectations compared to the Traveloka application. However, the perceived value of service quality for both applications, *** and Traveloka, yielded similar results. Moreover, the *** application demonstrated superior service quality with a score of 0.931, while the Traveloka Application scored 0.927. therefore, it can be concluded that the *** application is closer to meeting user expectations compared to the Traveloka application. the resulting data is based on a questionnaire distributed to users of both applications. the Statistical Package for the Social Sciences (SPSS) and Microsoft Excel were used for data processing. these results highlight the importance of considering the value aspects to improve the quality of services for both applications. © 2023 IEEE.

关键词： Quality of service

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CMSec: A Vulnerability Prevention Tool for Supporting Migrations in Cloud Composite Services 11

CMSec: A Vulnerability Prevention Tool for Supporting Migrat...

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11th IEEE international conference on Cloud networking (CloudNet)

作者： Oulaaffart, Mohamed Badonnel, Remi Festor, Olivier Univ Lorraine CNRS RESIST Res Team LORIAINRIA Nancy Grand Est F-54600 Villers Les Nancy France

ISBN: (数字)9781665486279

ISBN: (纸本)9781665486279

the growing maturity of orchestration languages, such as the Topology and Orchestration Specification for Cloud Applications (TOSCA) language, and the continuous improvement of virtualization techniques contribute to the large-scale deployment of cloud composite services based on multiple resources that may be hosted over different cloud service providers. the resources composing these services may be subject to migrations, either live or offline, in order to cope with performance objectives, such as load balancing, latency minimization, and energy savings. However, these migrations may induce changes affecting the configurations of migrated resources. In particular, these changes may lead to vulnerabilities, that may compromise the security of resources, or even the security of the whole cloud service. this demonstration showcases a vulnerability prevention tool, called CMSec, for supporting the resource migrations of TOSCA-based cloud composite services by considering OVAL vulnerability descriptions together with verification techniques. Based on two main scenarios, we will expose how the CMSec tool is able to detect vulnerable configurations prior to resource migrations, and to suggest corrective operations for remediating them.

关键词： Cloud computing Minimization Load management Topology Security Virtualization

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An Energy Consumption Optimization Strategy for Mobile Edge Networks 11

An Energy Consumption Optimization Strategy for Mobile Edge ...

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11th international conference on Information Technology - IoT and Smart City (ITIoTSC)

作者： Qu, Hua Zhao, Jihong Li, Xing Xue, Yucheng Gao, Qian Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian Peoples R China Xia Univ Posts & Telecomminicat Sch Commun & Informat Engn Xian Peoples R China

ISBN: (纸本)9798350328356;9798350328349

Edge computing involves shifting computational and storage capabilities from centralized cloud computing centers to the edge of the network, allowing edge servers deployed at the edge to respond to user requests. this approach eliminates the need for users to traverse the uplink from the edge network to the cloud computing center when initiating a business request, as well as the downlink from the cloud computing center when returning the computed results, significantly reducing transmission time. However, as latency requirements continue to increase, meeting higher latency demands will inevitably lead to the deployment of a large number of edge servers. the consequent energy consumption problem has become a pressing issue for service providers today. Studies have shown that user business requests are unevenly distributed over time, with periods of high and low demand. To cope with the uncertainty of user business requests and respond quickly, many edge servers remain idle or operate at low business volume, leading to significant energy consumption even when idle. this paper proposes a centralized server-controlled edge device hibernation strategy based on upper and lower thresholds, aiming to minimize the overall energy consumption of all edge servers in the system while meeting user latency requirements. By dividing time slots under upper and lower threshold monitoring, the paper analyzes the load conditions of each edge server in the system during each time slot and assigns computing task migration through the central server to reduce the number of idle and low-business volume edge servers, ultimately achieving the minimization of overall system energy consumption.

关键词： Edge computing Time inhomogeneity Low latency computing migration Energy consumption

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the Influence of Users' Perception of Security, Privacy, and Trust in Using Online Dating Applications 11

The Influence of Users' Perception of Security, Privacy, and...

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11th international conference on Information and Communication Technology, ICoICT 2023

作者： Panjaitan, Agnes Gabriella Guntur Utomo, Rio Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350321982

Users of online dating applications have been widely used by the public, especially young people, to find relationships or partners online. the purpose of this study is to describe the perceptions of security, privacy, and user trust in using online dating applications. In order to maintain user satisfaction, the Online Dating App must ensure that the Dating App's services must have user perception. this study aims to see the effect of user perceptions on security, privacy, and user trust using the Technology Acceptance Model (TAM) method. there are seven variables used, namely Perceived Usefulness, Perceived Ease of Use, Perceived Security, Perceived Privacy, Trust, Behavioral Intention To Use, and Actual System Use. the data collected is data obtained from distributing questionnaires by collecting 400 respondents who have used online dating applications and have an age range of 17 years to 25 years. the applications used for data processing are Smart-PLS 3.2.9 and Microsoft Excel. the results showed significant results, significant results, namely Perceived Usefulness had a significant effect on Behavioral Intention, Perceived Ease of Use had a significant effect on Behavioral Intention to Use, Perceived Privacy had a significant effect on Trust, Perceived Security had a significant effect on Trust, Trust had a significant effect on Trust Behavioral, Behavioral Intention to Use has a significant effect on Actual System Use. © 2023 IEEE.

关键词： Social networking (online)

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