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Secured Voting System based on Blockchain 2

Secured Voting System based on Blockchain

引用

2nd international conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Yamini, B. Renuka, K. Anuradha, M. Justus, J Jean Nalini, M. Chitra Devi, D. Dept of Networking and Communications School of Computing India Teegala Krishna Reddy Engineering College Dept of Aiml India S.A. Engineering College Dept of CSE India St Joseph's College of Engineering Dept of CSE India

ISBN: (纸本)9798350325799

Online voting is the current trend in modern society. It eliminates the usage and printing cost of ballot papers, this technology also reduces the amounts of paperwork, human resources, and time. But it has disadvantages like the security of data and potential attacks such as DoS and DDoS attacks. The best way to solve this security issue is using blockchain technology. The e-voting is expanded with blockchain technology and that could be the solution to mitigate the present concerns in e-voting. In blockchain every block is secured and connected with each other using hashing technology, it protects from being tampered by an unauthorized person. Blockchain technologies offer an infinite range of application benefiting from sharing economies. This study proposes a blockchain-based voting system, named e- Vote that preserves voter privacy and increases accessibility while keeping the voting system transparent, secure, and cost-effective e-Vote. In addition, e-Vote utilizes a few cryptographic techniques, including homomorphic encryption, to promote voter privacy. Proposed implementation was deployed on Ethereum's Test net to demonstrate usability, scalability, and efficiency to evaluate the application of blockchain as a service to implement the distributed electronic voting system. © 2023 IEEE.

关键词： Smart contract

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AoDNN: An Auto-Offloading Approach to Optimize Deep Inference for Fostering Mobile Web 41

AoDNN: An Auto-Offloading Approach to Optimize Deep Inferenc...

引用

41st IEEE conference on Computer Communications (IEEE INFOCOM)

作者： Huang, Yakun Qiao, Xiuquan Dustdar, Schahram Li, Yan Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Tech Univ Wien Distributed Syst Grp A-1040 Vienna Austria Shanxi Transportat Planning Survey & Design Inst Taiyuan 030012 Peoples R China

ISBN: (纸本)9781665458221

Employing today's deep neural network (DNN) into the cross-platform web with an offloading way has been a promising means to alleviate the tension between intensive inference and limited computing resources. However, it is still challenging to directly leverage the distributed DNN execution into web apps with the following limitations, including (1) how special computing tasks such as DNN inference can provide fine-grained and efficient offloading in the inefficient JavaScript-based environment? (2) lacking the ability to balance the latency and mobile energy to partition the inference facing various web applications' requirements. (3) and ignoring that DNN inference is vulnerable to the operating environment and mobile devices' computing capability, especially dedicated web apps. This paper designs AoDNN, an automatic offloading framework to orchestrate the DNN inference across the mobile web and the edge server, with three main contributions. First, we design the DNN offloading based on providing a snapshot mechanism and use multi-threads to monitor dynamic contexts, partition decision, trigger offloading, etc. Second, we provide a learning-based latency and mobile energy prediction framework for supporting various web browsers and platforms. Third, we establish a multi-objective optimization to solve the optimal partition by balancing the latency and mobile energy.

关键词： Deep learning conferences Neural networks Browsers Servers Task analysis Optimization

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Chipmunk: Simple distributed Object Storage for Named Data networking 13

Chipmunk: Simple Distributed Object Storage for Named Data N...

引用

13th international conference on Information and Communication Technology Convergence, ICTC 2022

作者： Park, Sae Hyong Shin, Yong Yoon Ko, Namseok Data-Centric Network Research Section ETRI Daejeon Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

In NDN, data should be kept as close as possible to edge so that it can be processed and analyzed closer to its source as in IP-based edge computing. That will help reduce the traffic and meet the latency requirements of emerging applications more easily. NDN is inherently equipped with the feature by supporting caching of data temporarily in network nodes. But persistent storage is still required to store data. As of today, a number of persistent storage solutions have already been proposed and used for diverse applications. In this paper, we present a novel distributed object storage in NDN, Chipmunk, where the actual file storage of data is separated from its metadata. We show through the tests on the prototype implementation that Chipmunk provides easier and simpler than and comparable object storage to existing ones. The paper also discusses how to improve the effectiveness of Chipmunk as a future work. © 2022 IEEE.

关键词： Digital storage

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Analysis of DDoS Attacks in Software Defined networking using Machine Learning

Analysis of DDoS Attacks in Software Defined Networking usin...

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Intelligent and Innovative Technologies in computing, Electrical and Electronics (IITCEE), international conference

作者： Anshika Sharma Harshvardhan Singh Chauhan Harleen Kaur Himanshi Babbar Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India

The most practical configurable network is Software Defined networking (SDN). SDN delineates the network's control layer from the data layer, while operationally integrating them. With global visibility into network status, the's logical centralized control improves network management. However, centralized management introduces new security risks. One of the most attractive victims of distributed denial of service (DDoS) attacks is the SDN control platform. Due to machine learning (ML) performance and unavoidable advantages against fingerprint vulnerabilities, this article proposes and considers a ML solution for protecting against DDoS attacks in SDN. ML techniques are evaluated in both, a theoretical format and a practical setup such that SDN controllers are disclosed to DDoS-attacks in order to reach significant deductions about ML-based cybersecurity of future networking protocols.

关键词： Measurement Protocols Machine learning Fingerprint recognition Denial-of-service attack Software defined networking Computer crime

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Building Energy Management System Using Coalition-Based Particle Swarm Optimization and Fog computing Infrastructure

Building Energy Management System Using Coalition-Based Part...

引用

international Middle-East Power System conference, MEPCON

作者： Dina M. Gado I. Hamdan Salah Kamel Almoataz Y. Abdelaziz Department of Electrical Power and Machines Faculty of Engineering Ain Shams University Cairo Egypt Department of Electrical Engineering College of Engineering and Information Technology Buraydah Private Colleges Buraydah Kingdom of Saudi Arabia Department of Electrical Engineering Faculty of Engineering South Valley University Qena Egypt Department of Electrical Engineering Faculty of Engineering Aswan University Aswan Egypt

ISBN: (数字)9798350379648

ISBN: (纸本)9798350379655

An Energy Management System (EMS) based on fog computing is introduced to enhance energy management. This system utilizes a streamlined model to address social networking issues by maintaining a balance between generated and required energy. The networking challenge is framed as a coalition problem tackled using particle swarm optimization. As the number of power customers grows, EMS efficiency in real-time environments faces challenges due to increasing computational complexity. To mitigate this, several service providers are shifting their computing and storage resources to cloud and fog environments. Fog computing, positioned between the cloud and customers, improves system speed compared to the cloud. In this scenario, a multi-unit residential building is considered, with each apartment acting as a player and excess electricity serving as compensation. The surplus power is distributed to apartments with energy shortages using Particle Swarm Optimization (PSO), which allocates electricity based on demand. Simulation results show that a surplus of 13.9 kWh of electricity is conserved and allocated to energy-deficient apartments at various times throughout the day.

关键词： Social networking (online) Electricity Simulation Real-time systems Smart grids Power systems Particle swarm optimization Energy management systems Faces Edge computing

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Task Scheduling for ICN-Based computing First Network: A Deep Reinforcement Learning Approach 8

Task Scheduling for ICN-Based Computing First Network: A Dee...

引用

8th IEEE international conference on Computer and Communications, ICCC 2022

作者： Zou, Zhuang Xie, Renchao Ren, Yuzheng Yu, F. Richard Huang, Tao Beijing University of Posts and Telecommunications State Key Lab. of Networking and Switching Technology Beijing China Carleton University Depart. of Systems and Computer Eng. OttawaON Canada

ISBN: (纸本)9781665450515

The computing first network (CFN) combines heterogeneous computing force information with network information and improves resource utilization and task execution efficiency through resource perception, service positioning, and task scheduling. However, since the heterogeneous computing force is difficult to express and perceive, and it is distributed on each node of the edge network, it is difficult to schedule tasks uniformly. Therefore, this paper proposes a CFN architecture based on information-centric network (ICN), which uses the naming mechanism of ICN to characterize the computing force and tasks, and the caching mechanism and routing and forwarding mechanism to improve the computing efficiency in the network. Under this network architecture, we study the scheduling problem of multi-user tasks in a period, model it as a multi-objective optimization model, and transform it into a Markov decision process (MDP) to enable control nodes to prioritize tasks and then schedule them to computing nodes for execution, which takes into account the task queue status and node resources. To solve the proposed problem, we use the deep reinforcement learning algorithm to approximate the optimal task scheduling solution. Finally, extensive simulation experiments are conducted to validate the effectiveness and superiority of the proposed scheme. © 2022 IEEE.

关键词： Reinforcement learning

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DDoS Attacks Mitigation: A Review of AI-Based Strategies and Techniques

DDoS Attacks Mitigation: A Review of AI-Based Strategies and...

引用

international conference on computing and networking Technology (ICCNT)

作者： Nachaat Mohamed Homeland Ssecurity Department Rabdan Academy Abu Dhabi UAE

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In the evolving landscape of cyber threats, distributed Denial of Service (DDoS) attacks represent a significant challenge for online infrastructure security. This review paper delves into the efficacy of Artificial Intelligence (AI)-based strategies and techniques in mitigating DDoS attacks. It synthesizes current research and developments in the field, emphasizing the adaptability and resilience of AI methods against the dynamic nature of these attacks. The paper categorizes various AI approaches, including machine leaming algorithms, deep learning frameworks, and heuristic methods, analyzing their strengths and limitations in real-world scenarios. Additionally, it explores the integration of AI with traditional security protocols to enhance defense mechanisms. Through a comprehensive examination of case studies and experimental results, this paper highlights the transformative impact of AI in detecting, analyzing, and neutralizing DDoS threats, thereby offering valuable insights for researchers and practitioners in cybersecurity. The review concludes with a discussion on future trends and potential research directions, underscoring the importance of continuous innovation in AI technologies to combat the evolving sophistication of DDoS attacks.

关键词： Deep learning Technological innovation Ethics Reviews Prevention and mitigation Denial-of-service attack Artificial intelligence Computer crime Vehicle dynamics Software defined networking

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Prevention Against DDoS 12

Prevention Against DDoS

引用

12th international conference on Computer Communication and Informatics, ICCCI 2022

作者： Venkatesh, K. Subashini, B. Kumar, P. Vinoth School of Computing College of Engineering Srm Institute of Science and Engineering Department of Networking and Communications Kattaknulathur India School of Computing College of Engineering Srm Institute of Science and Engineering India

ISBN: (纸本)9781665480352

The current time is totally reliant upon Internet. Web gives a worldwide data source to every one of its clients;thus the availability of web is of much significance. The distributed Denial of Service (DDoS) assaults have become an enormous scope issue or inconvenience for the clients of PC frameworks which are associated with the Internet. DDoS aggressors take over on the optional casualty frameworks and thus utilizing them to wage an organized huge scope assault against target frameworks. As of now the new countermeasures are being made to forestall DDoS assaults to help the frameworks that are casualties of such an assault. The aggressors are constantly building up the new programming and adjusting more established DDoS assault instruments to evade or keep away from these new countermeasures. In this paper our principle center is around the DDoS assault which squares and damages the organization accessibility by flooding the objective with high measure of illicit traffic usurping its data transfer capacity, overburdening it to stop authentic traffic to get past. Different strategies to turn away and decrease these assaults alongside their benefits and inconveniences are additionally talked about. © 2022 IEEE.

关键词： Denial-of-service attack

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Investigation and System Design of distributed Communication Radios

Investigation and System Design of Distributed Communication...

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2021 international conference on networking, Communications and Information Technology, NetCIT 2021

作者： Ding, Ting Shanghai Aircraft Design and Research Institute Shanghai China

ISBN: (纸本)9781665400701

The communication distributed radio concept distributes the RADIO frequency transceiver (TX/RX) sensor portion and most of the processing portion of the system to the aircraft area near the antenna. The rest of the digital portion of the system can be hosted on a common computing platform, such as the Integrated Modular Avionics System (IMA). © 2021 IEEE.

关键词： Antennas

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A Method for Parameter Updating in distributed Deep Learning

A Method for Parameter Updating in Distributed Deep Learning

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Next Generation Data-driven Networks (NGDN), international conference on

作者： Ming He Xi Guo Dexuan Wang Shouhao Qin Research Institute China United Network Communications Corporation Beijing China GuoChuang Software Co. Ltd. Hefei China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

The improvement of distributed training performance of deep learning models involves multiple aspects. This article proposes a parameter update method for distributed training of deep learning models from the perspective of training methods. The method mainly includes two stages of model training. In the first stage, the synchronization period and differential update parameters are mainly obtained; in the second stage, each worker node conducts distributed training based on the synchronization period and differential update parameters obtained in the first stage. This method can reduce the synchronization waiting time between worker nodes, reduce the communication pressure between parameter servers and worker nodes, and improve the performance of distributed training of deep learning models while improving the utilization of computing power resources.

关键词： Training Deep learning Computational modeling Computer architecture Synchronization Servers Resource management Frequency synchronization Next generation networking Convergence

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