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Decentralized Implementations of SDN-controllers in SDN-FANETs: A Survey 4

Decentralized Implementations of SDN-controllers in SDN-FANE...

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4th international conference on distributed Sensing and Intelligent systems, ICDSIS 2023

作者： Eljack, Aseel Talib, Manar Abu Abbas, Sohail Nasir, Qassim Department of Computer Science University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9781839539954

The implementation of Software-Defined Networking (SDN) in Flying Ad-hoc networks (FANETs), known as SDN-FANET, has gained significant traction in the past decade. This is particularly due to its eccentric capabilities and features which effectively enhance the operation and performance of Unmanned Aerial Vehicles (UAVs) in various applications. SDN-FANETs introduces adaptability and programmability to the network by centralizing its control. A single entity known as the SDN controller, carries out the network control’s management and operation. However, this centralized approach introduces a significant risk: a Single Point of Failure (SPoF) associated with the SDN controller. The failure of the controller can lead to severe network disruptions. Therefore, to tackle this challenge, recent research has focused on decentralizing the SDN controller. This survey presents, compares, and analyzes the latest work done in mitigating the centralization issue. Various approaches to decentralization, such as multi-controller architectures, distributed control, and hybrid models, are discussed based on their effectiveness in improving network scalability, reliability, energy efficiency, routing, security, and cost of implementation. In addition, we categorized these schemes in terms of the main technologies utilized: Cloud & Edge, Blockchain, a hybrid of both, and miscellaneous ones. Our work also highlights important findings from the discussed solutions, and open research issues in the context of SDN-FANETs along with some possible future directions. By identifying these research gaps, our survey contributes to the understanding of future research directions in the field of SDN-FANETs. © The Institution of Engineering & Technology 2023.

关键词： Unmanned aerial vehicles (UAV)

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Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural networks

Deception Detection from Linguistic and Physiological Data S...

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Information Science, Parallel and distributed systems (ISPDS), international conference on

作者： Panfeng Li Mohamed Abouelenien Rada Mihalcea Zhicheng Ding Qikai Yang Yiming Zhou Department of Electrical and Computer Engineering University of Michigan Ann Arbor USA Department of Computer Science University of Michigan Ann Arbor USA Fu Foundation School of Engineering and Applied Science Columbia University New York USA Department of Computer Science University of Illinois Urbana-Champaign Urbana USA Department of Engineer Sciences Saarland University of Applied Science Saarland Germany

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

Deception detection is gaining increasing interest due to ethical and security concerns. This paper explores the application of convolutional neural networks for the purpose of multimodal deception detection. We use a dataset built by interviewing 104 subjects about two topics, with one truthful and one falsified response from each subject about each topic. In particular, we make three main contributions. First, we extract linguistic and physiological features from this data to train and construct the neural network models. Second, we propose a fused convolutional neural network model using both modalities in order to achieve an improved overall performance. Third, we compare our new approach with earlier methods designed for multimodal deception detection. We find that our system outperforms regular classification methods; our results indicate the feasibility of using neural networks for deception detection even in the presence of limited amounts of data.

关键词： Information science Ethics Neural networks distributed databases Linguistics Feature extraction Physiology

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A Multi-Path Orchestration Method for Cloud-Edge Data Transmission Based on DRL

A Multi-Path Orchestration Method for Cloud-Edge Data Transm...

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international conference on Parallel and distributed systems (ICPADS)

作者： Zhihui Wang Xubing Dou Shuyun Li Huan Ma Xinchang Zhang Maoli Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Normal University Jinan China QuFu normal university Jining China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

In addressing the multi-path problem of cloud-edge data transmission, this paper introduces a multi-path orchestration method based on deep reinforcement learning (DRL) within the context of software-defined networks (SDN), termed Deep Q-Routing (DQR). This method establishes optimal data transmission paths between the cloud and multiple edges, based on available bandwidth and transmission delay, while avoiding path congestion to improve bandwidth utilization and reduce transmission delay. The optimal routing in SDN is defined through the design of state space sets, reward functions, and neural networks. Simulation experiments were conducted to evaluate the DQR method. Compared to existing routing methods, the proposed DQR method, which includes a reward function with weighted parameters, demonstrates superior overall network performance.

关键词： Cloud computing Scalability Neural networks distributed databases Bandwidth Routing Deep reinforcement learning Delays Data communication Software defined networking

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Dynamic Adaptive Reinforcement Learning based Resource Allocation in Industrial Internet of Things

Dynamic Adaptive Reinforcement Learning based Resource Alloc...

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

作者： Kai Zhang Jing Li Xiande Zhuo Xi Wang Weicen Lv Xiaojun Li Jin Huang School of Information Engineering Chengdu Industry & Trade College Chengdu China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The resource allocation in distributed environment issue in energy efficient information to ward in resource constraint Industrial Internet of Things (IIoT). Though, because of lacking the flexibility, classical duty cycling mechanism causes addition collision of information and re-transfer particularly in challenging and dynamic environments, resulted in high energy consumption. To overcome these drawbacks, the Dynamic Adaptive Reinforcement Learning (DARL) algorithm for allocating the resources in IIoT with less energy consumption. The dynamic adaptive process is included in the process of RL algorithm, which enhances the performance of RL for resource allocation. The RL method is the dynamic program that addressed the issue through formulating the optimization issue of Multiple Agent RL (MARL), where every Base Station (BS) considered as the learning agent. The developed DARL algorithm evaluates the First Node Death Time (FNDT) in terms of number of nodes. The developed DARL reached 12s on 20 nodes, 14s on 40 nodes, 19s on 60 nodes, 20s on 80 nodes and 23s on 100 nodes while comparing with Adaptive RL (ARL) algorithm.

关键词： Energy consumption Adaptive systems Heuristic algorithms Reinforcement learning Dynamic scheduling Energy efficiency Resource management computer security Optimization Industrial Internet of Things

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Trust-Chain-Based Certificate Revocation Control in Autonomous Vehicle networks 5

Trust-Chain-Based Certificate Revocation Control in Autonomo...

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5th international conference on Information and Communications Technology, ICOIACT 2022

作者： Erceylan, Gizem Akcayol, M. Ali Informatics Institute Gazi University Ankara Turkey Gazi University Department of Computer Engineering Ankara Turkey

ISBN: (数字)9781665451406

ISBN: (纸本)9781665451406

One of the biggest problems in V2X communication is the inclusion of faulty or illegal nodes in the network. In V2X systems using PKI, the primary approach is to revoke the certificates of malicious and compromised vehicles and add them to a CRL. The issue of CRL distribution from the central server without high latency and high traffic is a subject of research. In this paper, a semi-distributed certificate revocation control system called trust-chain is proposed, which reduces the demand for CRL servers. In the simulation results, the number of requests to the central CRL server is reduced by 35% on average using the proposed semi-distributed structure. Additionally, in cases where vehicles cannot communicate with the RSU or central server, vehicles can communicate securely owing to the semi-distributed framework of the proposed system. © 2022 IEEE.

关键词： Network security

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Agricultural Intelligence: Federated Learning CNN's Models for Jute Leaf Disease Analysis 11

Agricultural Intelligence: Federated Learning CNN's Models f...

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11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Rajput, Kapil Garg, Ashish Kukreja, Vinay Mehta, Shiva Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350350357

The research study performs an experimental analysis of a federated learning system that uses convolutional neural networks (CNNs) to detect and classify jute leaf diseases from many distributed data sources. We focus on five specific categories of jute leaf diseases, using local datasets from five individual clients to generate personalized models. The models are ensembled using federated averaging to create a robust global algorithm. Federated learning harnesses privacy and data security but enables the model to learn from various illness symptoms. The findings are promising, indicating that the federated learning model not only preserves but also improves the accuracy of illness categorization when moved from local to global data sets. The model's performance was assessed using precision, recall, F1-score, support, and accuracy parameters. The average accuracy, recall, and F1score increased from 78.86%, 78.7%, and 78.71% for mq_1 to 94.21%, 94.19%, and 94.2% for mq_5 for the five clients. The weighted means showed a similar improvement, with mq_1 starting at 79.38% and mq_5 reaching 94.22%. Micro averages had the same trend of increase, growing from 79.27% for mq_1 to 94.22% for mq_5. The statistical measures reveal increasing diagnostic precision of the model as the federated learning process advances. The research also examines the performance of federated averaging in aggregating local data sets for a global model. The global model accuracy achieves the best accuracy of local clients, up to 0.98. This shows how federated learning improves the global model by adding local data contributions. The research emphasizes the efficiency of federated learning and CNNs in solving the complex problem of jute leaf disease classification. © 2024 IEEE.

关键词： Learning systems

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Reproduction Mechanism with Squirrel Search Algorithm for Cloud Computing Resource Management

Reproduction Mechanism with Squirrel Search Algorithm for Cl...

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

作者： Jinming Zhang Department of Information Technology Yantai Vocational College Yantai China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The cloud computing introduced to generate user requirements to access resource computing to users for purchasing cloud service as essential concept of on-demand resource sharing by highly internet-based applications. The resource management is significant in high-scale distributed computing which tackles complex optimization issues. Therefore, this research proposes a Reproduction Mechanism with Squirrel Search Algorithm (RMSSA) for cloud computing resource management. Primarily, multiple users access the shared resources and then RMSSA optimizes those resource through evaluation phase to estimate results. Based on this estimation, resources are updated and reallocated. This process is repeated iteratively for ensuring constant resource usage in optimization. The RMSSA enhances both efficiency and precision of resource allocation thereby enhancing overall performance. In RMSSA, the exploration and exploitation allow to discover unexplored opportunities while preventing local optima. It also determines the number of allocated resources based on fitness solutions that available cloud resources are used optimally. The makespan, cost, resource utilization, power consumption and throughput metrics are considered with different no. of tasks like 100, 300, 500, 700 and 900. The RMSSA obtains 420s, 920$ and 96.26% of makespan, cost and resource utilization for number of tasks of 500.

关键词： Measurement Cloud computing Costs Power demand Estimation Throughput Resource management distributed computing computer security Optimization

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A Survey on Machine Learning Approaches for Intrusion Detection in Cloud Computing Environments for Improving Routing Payload security and Network Privacy

A Survey on Machine Learning Approaches for Intrusion Detect...

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2024 IEEE international conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024

作者： Vivek, Vullikanti Veeravalli, Bharadwaj Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (纸本)9798350353464

distributed denial-of-service (DDoS) attacks can significantly impair the network’s availability, and recent statistics show that 30% of cyber-attacks are based on network intrusion, with 27% of incidents occurring in the financial sector. To effectively monitor large volumes of sensitive data in real-time and actively defend against these attacks, an efficient intrusion detection system (IDS) is essential. Considering the high volume of data as data-centric and Intrusion Detection (ID) as a data analysis process, Machine Learning (ML) models are capable of detecting nonconforming/ atypical network traffic through the application of signature and anomaly-based methodologies. Signature based method is prone to false negatives while anomaly-based method suffers from false positive rates. As attackers continuously evolve and create innovative malware injection techniques with new malware patterns and behaviors, there is a high demand for adaptive rule-based ML models for anomaly detection. In this paper, we attempt to rigorously review the latest trends in supervised and semi-supervised ML approaches for network traffic analysis to detect anomaly-based DDoS attacks. As the volume of the ML literature is overwhelming, our interest is specifically centered around eliciting some of the best suitable ML models that are in practice for traditional networks, IoT, Cloud, and SDN environments and can be adopted for the ensemble approach to construct adaptive rule-based ML algorithms. We believe such a consolidated overview for a broad set of platforms will help readers to quickly make a choice on the required platforms, datasets involved, and methods that suit their applications. ©2024 IEEE.

关键词： Network intrusion

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Maximize Leader Election Algorithm Performance in Bi-Directional Ring networks With Both Links Down Between Two Nodes 25

Maximize Leader Election Algorithm Performance in Bi-Directi...

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25th international Arab conference on Information Technology, ACIT 2024

作者： Al Refai, Mohammed N. Fawareh, Hamed Aljawawdeh, Hamzeh Alshraiedeh, Fuad Sameh Al-Quraan, Mohammad Al-Odat, Hisham Ali Otoom, Ahmed Jamhawi, Zeyad M. Kanan, Mohammad Zarqa University Software Engineering Dep. Zarqa Jordan Zarqa University Zarqa Jordan Applied Science Private University Department of Cyber Security Amman Jordan Applied Science Private University Department of Computer Science Amman Jordan University of Business and Technelogy Dep. of Industry Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

The process of selecting a leader to avoid instability in distributed system network after leader failure is called Leader Election Algorithm (LEA). This process becomes more complicated when it faces intermittent or complete links disconnected. the network becomes a one-way behavior if both lines of communication between two points are disrupted and thus the network becomes very slow, especially in systems where time is a critical issue, and the process of communication between points becomes much slower if the network leader is next to the fault or close to it, so the criteria will be established to ensures that the system leader in the Bidirectional ring communication network is in a place that allows next leader to communicate with all points as quickly as possible, and this will be during the election *** research presents a solution for leader failure problem in bidirectional ring network. This research takes into account the existence of intermittent or disconnection links between nodes. It will produce three algorithms to determine a new leader in the network with maximum network performance. It uses the least probability of time steps and the number of messages needed to elect the new leader in bidirectional ring network with N communicated nodes. so that the system remains able to work and communicate with its best capacity during this problem with the number of message complexity O(N) in the worst case. The third scenario minimizes the value of proper leader ID IDCurrent/IDCurrent-idStepCount', and maximizes the value of proper leader place N - (N - nStepCount). © 2024 IEEE.

关键词： Artificial intelligence

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security Enhancement in Network Based Intrusion Detection Using Natural Language Processing with GAN concept and BEGAN Model

Security Enhancement in Network Based Intrusion Detection Us...

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

作者： Anjali Sanjivanrao More Kaustubh Harishchandra Nimbalkar Nikhileshwar Vijayprakash Jatale Aniket Hanumant Kale Hrishikesh Ashokkumar Nagargoje Department of Computer Engineering Suman Ramesh Tulsiani Technical Campus FoE MH India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

Cyber-attacks, particularly in recent times represent a major problem demanding novel measures consistent with modern digital structures. Some of the dangers that cybercrime entails encompass phishing, ransomware, DDoS assaults, as well as state-enabled cyber espionage. With growing digital infrastructures in countries and businesses moving to digital transformation, securing in the digital arena is more challenging. The number of lines of code nowadays generated daily makes it necessary to have enhanced vulnerability identification procedures. Cyber security can be advanced through the Natural Language Processing (NLP) subfield of Artificial Intelligence towards achieving computer-human language interaction. Cybercrime has seen a widespread utilization of GANs based on IDSs. Despite this, GAN-based IDS has still faced criticism. This consideration is based on real-time observation fueled by made strides cautioning and clarification of peculiarities utilizing GAN improved with NLP. Moreover, we suggest some future research works that will help resolve the deficiencies encountered by GANs based on IDS.

关键词： Adaptation models Phishing Network intrusion detection Machine learning Generative adversarial networks Natural language processing Real-time systems Ransomware Protection Cyberattack

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