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15th international conference on distributed computing and internet technology, icdcit 2019

15th International Conference on Distributed Computing and I...

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15th international conference on distributed computing and internet technology, icdcit 2019

ISBN: (纸本)9783030053659

the proceedings contain 35 papers. the special focus in this conference is on distributed computing and internet technology. the topics include: Data scheduling and resource optimization for fog computing architecture in industrial IoT;Research on CNN parallel computing and learning architecture based on real-time streaming architecture;Detection of alcoholism: An EEG hybrid features and ensemble subspace K-NN based approach;on the growth of the prime numbers based encoded vector clock;A sensitivity analysis on weight sum method MCDM approach for product recommendation;event detection and aspects in twitter: A bow approach;a new automatic multi-document text summarization using topic modeling;improved visible light communication using code shift keying modulation;optimization of transmission range for a fault tolerant wireless sensor network;the digital turn: On the quest for holistic approaches;community detection using an enhanced louvain method in complex networks;a fast handoff technique for wireless mobile networks;adaptive partitioning using partial replication for sensor data;design and implementation of cognitive radio sensor network for emergency communication using discrete wavelet packet transform technique;a network formation model for collaboration networks;bees detection on images: Study of different color models for neural networks;mobile charging of wireless sensor networks for internet of things: A multi-attribute decision making approach;efficient searching over encrypted database: Methodology and algorithms;tag-reader authentication system guarded by negative identifier filtering and distance bounding;RSA-based collusion resistant quorum controlled proxy re-encryption scheme for distributed secure communication;secured communications on vehicular networks over cellular networks.

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Empowering IoT Security: Leveraging Deep Neural Networks Against Ransomware and DDoS Attacks 15

Empowering IoT Security: Leveraging Deep Neural Networks Aga...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Geetha, K. Chaudhary, Dharmindar Shrivarshini, K. Amrita Vishwa Vidhyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

Using internet of things (IoT) devices has become more efficient and convenient, but it has also increased the potential of security breaches, particularly from ransomware and distributed Denial of Service (DDoS) assaults. this study looks into the ever-changing threats posed by ransomware on the internet of things, emphasizing human-centered detection and mitigation techniques. It highlights attack methods like supply chain breaches and device exploitation that jeopardize privacy and service continuity. To enhance IoT security, a deep neural network-based approach is proposed for automated reaction and real-time monitoring. the superior precision and efficacy of the suggested approach in mitigating ransomware and DDoS assaults in contrast to existing techniques underscores the significance of robust security protocols and active stakeholder engagement. this paper's goal is to provide a thorough study of these dangers along with a novel solution that will improve security protocols in IoT ecosystems. this study seeks to close the gap between sophisticated technological protections and workable, user-oriented tactics by highlighting the need of human-centered approaches, guaranteeing that IoT devices continue to be dependable and safe in an increasingly interconnected world. this study emphasizes the vital need for an integrated strategy to IoT security that incorporates both cutting-edge technology and proactive human engagement through thorough evaluation and suggested solutions. © 2024 IEEE.

关键词： and IoT security automated reaction device exploitation distributed Denial of Service (DDoS) human-centric detection internet of things (IoT) mitigation techniques privacy violations ransomware service disruptions supply chain breaches

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Research on Intelligent Scheduling Algorithm Based on Cloud computing 15

Research on Intelligent Scheduling Algorithm Based on Cloud ...

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15th international conference on Computer Research and Development, ICCRD 2023

作者： Yan, Xiaofeng Zhao, Jie Han, Jianchao Chongqing Medical and Pharmaceutical College Chongqing China

ISBN: (纸本)9781665487504

With the rapid development of the information age, computer technology and network technology are more and more mature, and the application of internet technology is more and more extensive. On the basis of the gradual development and maturity of distributed computing, parallel computing and grid computing, cloud computing based on big data came into being. the core idea of cloud computing is to connect a large number of computing resources, storage resources and service resources through the network to form a resource pool, and then conduct unified scheduling and management of resources according to user needs. this paper mainly studies the resource scheduling method based on neural network and genetic algorithm to dynamically provide data centric computing service system for users, and maximize the reliability, customization and security of the services provided. © 2023 IEEE.

关键词： Genetic algorithms

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EdgeShield: Hybrid Real-Time Attack Detection in IOTA Tangle via Edge Devices 15

EdgeShield: Hybrid Real-Time Attack Detection in IOTA Tangle...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Arunodhai, V. Susan, Leena P. Kannimoola, Jinesh M. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Applications Amritapuri India

ISBN: (纸本)9798350370249

the internet of things (IoT) revolutionizes technology interaction by enabling seamless connectivity and automation, but it introduces significant security challenges. A distributed ledger technology (DLT) called IOTA Tangle, a utilizing a Directed Acyclic Graph (DAG), enables secure and feeless transactions among IoT devices. Despite its advantages, the IOTA Tangle faces threats such as replay attacks and double spending, which highlight vulnerabilities in distributed ledger technologies. To address these security concerns, we initially developed EdgeGuard, a rule-based system for spam detection within the IOTA ecosystem. However, its reliance on predetermined rulesets limited its adaptability. We have now enhanced our approach with EdgeShield, incorporating a neural network for superior threat *** enhances adaptability over EdgeGuard in threat detection for the IOTA Tangle by integrating machine learning. EdgeShield analyzes network packets to identify threats, leveraging machine learning to adapt to evolving risks. Trained using the MQTTSet dataset, it detects network-based threats such as denial-of-service (DoS) attacks and packet flooding. Tested with simulated IoT data, EdgeShield demonstrates effectiveness across various domains, particularly in healthcare IoT. Its decentralized processing on edge devices like the Raspberry Pi 4 enables immediate network traffic analysis, reducing reliance on centralized servers and improving system resilience. Additionally, real-time alerts empower administrators to proactively secure IoT deployments and maintain transaction integrity within the IOTA Tangle. this paper traces our research evolution from EdgeGuard to EdgeShield, highlighting advancements and demonstrating the enhanced security of IoT transactions on the IOTA Tangle. © 2024 IEEE.

关键词： and Decentralised Security Measures distributed Ledger Edge computing Edge Devices internet of things (IoT) IOTA Tangle Machine Learning Network Packet Analysis Real-Time Security

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Split computing based Quantized PoseNet Model for distributed AI Architecture in IoT-Edge 15

Split Computing based Quantized PoseNet Model for Distribute...

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15th international conference on Communication Systems and Networks (COMSNETS)

作者： Karjee, Jyotirmoy Naik, Praveen S. Anand, Kartik Byadgi, Chandrashekhar S. Dabbiru, Ramesh Babu Venkat Srinidhi, N. Samsung R&D Inst India Bangalore Karnataka India

ISBN: (纸本)9781665477062

In recent times, internet of things (IoT) devices is gaining popularity in advanced wireless technology (i.e., 5G). However, in 5G applications (say in edge platform), the IoT devices have limited computation & processing capabilities which makes it challenging to execute Deep Neural Network (DNN) models on them. To address this, we introduce Split computing technology, to partition DNN inference layers based on the computational capabilities (such as bandwidth, battery level and processing power, etc.) of IoT and edge (computationally powerful) devices, respectively. To validate split computing, we propose a framework called distributed Artificial Intelligence (DAI) architecture. We use the architecture for a fitness application (use-case) where we detect the pose of a person for our proposed Quantized Split PoseNet DNN (QSP-DNN) algorithm which partitions the DNN layers among IoT device and edge based on Wi-Fi bandwidth. We perform experiments to validate the QSP-DNN algorithm using DAI architecture. the QSP-DNN with DAI compares split execution (computed among IoT device & edge) for partial offload and full-offload executed on edge device. the result shows that using QSP-DNN in DAI architecture provides split execution performing 20.76 % improvement compared to the full offload case.

关键词： Performance evaluation Computational modeling Image edge detection Computer architecture Bandwidth Inference algorithms Partitioning algorithms

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distributed Crowdsensing Based on Mobile Personal Data Stores 15th

Distributed Crowdsensing Based on Mobile Personal Data Store...

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15th international conference on Ubiquitous computing and Ambient Intelligence

作者： Perez-Vereda, Alejandro Cabanero, Luis Moreno, Nathalie Hervas, Ramon Canal, Carlos Univ Castilla La Mancha Ciudad Real Spain Univ Malaga ITIS Software Malaga Spain

ISBN: (纸本)9783031485893;9783031485909

Users' personal information is one of the most important actives for nowadays enterprises. Knowing user preferences allows to offer personalized interactions and obtain more high-value information. In this context, crowdsensing shows as a technique that aims to collect information about the users and their internet of Everything (IoE) environment. Personal smartphones are the devices that act as the interface between people and the IoE. However, most of the related works in the literature consider smartphones as mere sensors that gather user data, which is then transferred to the crowdsensing requester. As an alternative, in this paper we propose a distributed crowdsensing platform based on a extension of the Digital Avatars framework for Mobile Collaborative Social computing. In our proposal, smartphones are responsible for compiling and keeping the digital avatar or virtual profile of each of the users participating in the crowdsensing activity. Based on these avatars, the framework is extended to provide a distributed platform for both the dissemination and the aggregation of the results of the activity, granting users with privacy and ownership of their personal data. Our proposal also takes into account trust and user reputation by means of subjective logic. the proposed system is tested and validated through a proof of concept.

关键词： Crowdsensing distributed Crowdsensing Virtual Profiles Digital Avatars Personal Data Store

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DDoS Attack Detection and Mitigation Using Mininet and RYU Controller in SDN Environment 15

DDoS Attack Detection and Mitigation Using Mininet and RYU C...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Kaushik, C. Varunteja, D. Krishna, M Shiva Jaavali, S. Vnr Vignana Jyothi Institute of Engineering and Technology Dept. of Ece India

ISBN: (纸本)9798350370249

In today's digital era, where internet-connected devices are becoming more prevalent, DDoS attacks remain a significant concern for network infrastructure security. Software-Defined Networking (SDN) emerges as a promising solution to bolster network security through centralized control and adaptable network management strategies. this paper outlines an innovative method for detecting and countering DDoS attacks within SDN by utilizing machine learning technologies. this proposed framework capitalizes on the agility and programmability of SDN to collect data on network traffic from diverse network components. Furthermore, to combat DDoS threats promptly, this framework seamlessly integrates with SDN controllers to dynamically adjust network policies based on the severity of detected threats. In this RYU controller is used as SDN control and Mininet is used to observe the network setup. By utilizing programmable network switches, suspicious traffic streams can be rerouted to specialized scrubbing centres for further scrutiny and filtration, while legitimate traffic maintains uninterrupted flow. Detection module is successfully detecting the Ddos attack and Using mitigation module the attacker port is blocked. © 2024 IEEE.

关键词： DDos attack Detection distributed denial of service Mininet Ryu controller software defined network Wireshark

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Detection of distributed Denial of Service in the Application Layer of IoT Using Machine Learning 15

Detection of Distributed Denial of Service in The Applicatio...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Nanthiya, D. Gopal, S.B. Manjula Devi, R. Praveenkumar, R. Vijayakumar, S.D. Raja, T. Dharanish Sriram, L. Kongu Engineering college Department of Computer Technology -UG Perundurai India Kongu Engineering College Department of Electronics and Communication Engineering Perundurai India Kpr institute of Engineering and Technology Department of Computer science and Engineering Coimbatore India Builders Engineering College Department of Electronics and Communication Engineering Kangeyam India Vivekanandha college of Technology for women Department of Electronics and Communication Engineering Tiruchengode India

ISBN: (纸本)9798350370249

Cybersecurity prevents harmful assaults on computers, networks, and mobile devices. Included in the category of cyber-attacks are threats like as malware, distributed denial of service assaults, phishing, spoofing, code injection, and supply chain attacks. Massive denial-of-service assaults are destructive. As part of a distributed Denial-of-Service (DDoS) assault, hackers bypass botnets and go straight for the server. In order to temporarily block or suspend a website, these botnets send a flood of requests to the server. the server typically takes requests and returns responses. there will be a brief outage if the server gets more requests than it can handle. While this attack makes the service inaccessible, it prevents data theft. A machine-learning approach is used to detect server-side DDoS assaults. the term 'machine learning' refers to software that can learn new tasks and solve old ones without any input from a human. Learning Vector Quantization, Support Vector Machine, Naive Bayes, K-Nearest Neighbour, Random Forest, and Decision Tree are the six ML classification models used since these ML procedures enhance accuracy. the main goal of this research is to use the CIDAD dataset to find ways to protect the application layer of the internet of things (IoT) against distributed Denial of Service (DDoS) attacks. the lower layers of the IoT network architecture are disregarded by this dataset, which comprises information at the COAP level. © 2024 IEEE.

关键词： CIDAD Cyber security Cyber-attacks DDoS attacks

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Secure Data Collection and Management in internet of things-Based Cloud Networks 15

Secure Data Collection and Management in Internet of Things-...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sharma, Nupur Sharma, Deeplata Ezhilarasan, G. Arka Jain University Department of Computer Science & It Jharkhand Jamshedpur India Vivekananda Global University Department of Chemistry Jaipur India School of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India

ISBN: (纸本)9798350370249

Net of factors (IoT)-primarily based cloud networks are composed of more than one distributed computing gadget consisting of sensors, gateways, and edge servers that accumulate, examine, and save massive volumes of information. It is important to make certain that facts are accumulated comfortably and stored in an at-ease manner. this paper discusses diverse protection mechanisms that may be used to secure information collection and control within IoT-primarily-based cloud networks. Firstly, cozy identity management strategies along with identity authentication, token-primarily based authentication, and digital certificates are mentioned to ensure the relaxed acquisition and transmission of records. Secondly, encryption techniques such as secure encryption algorithms, key control strategies, and comfortable channel protocols are mentioned to enable storage and switching of the facts in a comfy way. Eventually, safety rules and great practices, together with identification and getting the right of entry to manipulate, data anonymization, and device logging, are supplied to ensure relaxed facts control in cloud networks. Its miles understood that comfortable facts control within IoT-based totally cloud networks is essential to guarantee confidentiality, integrity, and availability of information. As such, the security techniques discussed in this paper can assist in offering comfortable surroundings for the collection and management of facts in IoT-based totally cloud networks. © 2024 IEEE.

关键词： Collection Iot-Based Management Networks Protocols Strategies Techniques

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Secure, Transparent, Supply Chain Supporting Agri-Food Provenance and Sustainability 15th

Secure, Transparent, Supply Chain Supporting Agri-Food Prove...

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15th international conference on Ubiquitous computing and Ambient Intelligence

作者： Martin, thomas Cowderoy, Tom Rafferty, Joseph Cadden, Trevor McIvor, Ronan Brennan, Lorraine Ulster Univ Belfast Antrim North Ireland Ulster Coll Dublin Dublin Ireland

ISBN: (纸本)9783031486418;9783031486425

the protein-I project is a cross Ireland initiative that takes a food systems approach to enhancing the sustainability of protein production across the island of Ireland, as part of the protein-I project, this study aims to produce a smart agricultural supply chain solution. there is a need for the requirements of the system to be tailored for the specific use case, agriculture on the island of Ireland is different from agriculture in other parts of the world. the average size of a farm in the Republic of Ireland is 32.4 ha [8], compare this to Australia where the average is 4,331 ha [7], therefore farm practices in Australia will be different. To develop a solution that is catered to the needs of the Agri-food sector on the island of Ireland this study gathered requirements from a series of workshops and interviews with stakeholders. Several use cases were presented by the stakeholders to identify the deficiencies in the current supply chain, and to address these challenges, a custom solution has been designed and implemented.

关键词： Secure supply chains blockchain distributed ledger technology carbon calculation sustainable living smart agriculture internet of things Industry 5.0 protein-I project

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