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An Efficient Flow Rule Conflict Comprehensive Detection Scheme for SDN networks 22

An Efficient Flow Rule Conflict Comprehensive Detection Sche...

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22nd IEEE international symposium on Parallel and distributed Processing with Applications, ISPA 2024

作者： Han, Bo Liu, Yuan Zhou, Yongbin Gao, Yiwen Nanjing University of Science and Technology School of Cyber Science and Engineering Nanjing China

ISBN: (纸本)9798331509712

Software-Defined Networking (SDN) has introduced flexibility and efficiency to network management but also faces challenges from flow rule conflicts, including static, dynamic, and dependency conflicts. Existing detection algorithms often focus on a single conflict type, resulting in inefficiencies and high false positive rates. To address these issues, we propose a comprehensive flow rule conflict detection scheme that improves real-time detection of explicit conflicts (both static and dynamic) and reduces false positives in implicit (dependency) conflict detection. Specifically, we present a real-time explicit conflict detection algorithm based on the Protocol-Divided Trie (PDT), which categorizes flow rules by protocol type and uses a prefix tree for rapid matching. Experimental results show that this approach significantly reduces detection times by at least 39.2% and achieves 100% detection accuracy. Additionally, we propose a two-stage detection (TSD) algorithm that combines the precision of path-based detection (PBD) with the efficiency of alias set-based detection (ASD). Our experiments reveal a 51% reduction in false positives compared to ASD and a 48% reduction in detection time compared to PBD, while maintaining equivalent false positive rates. This approach provides a robust solution for conflict detection in SDN, improving network security and resource utilization efficiency. © 2024 IEEE.

关键词： Conflict Detection explicit conflicts implicit conflict Software-Defined networks

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A Simple and Lightweight Mutual Authentication Protocol for Wireless Sensor networks 17

A Simple and Lightweight Mutual Authentication Protocol for ...

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17th IEEE international Conference on Advanced networks and Telecommunications systems, ANTS 2023

作者： Vyas, Sauhard Jain, Usha Manipal University Jaipur Department of Computer & Communication Engineering Jaipur India Manipal University Jaipur Department of Computer Science Engineering Jaipur India

ISBN: (纸本)9798350307672

Wireless sensor networks (WSNs) are widely employed in a wide range of applications;because of technological improvements, extremely small sensors are now widely accessible, and their utilisation decreases costs. Because of power and computational limits, wireless network protocols cannot be used on networks with small nodes. We propose a node authentication for nodes in WSNs to avoid security threats and create secure communication between them, based on our understanding of the importance of security in WSNs and the resource constraints of small WSNs. In the proposed Protocol, a base station (S) creates and maintains a secret value and a random number is present in each sensor node. The sensor node authenticates itself by using a secret value and hash functions. Timestamps promotes freshness, efficiency, and durability. Because the proposed protocol is lightweight cryptography, it necessitates little computing, communication, and storage resources. The proposed mechanism's security analysis revealed no security flaws, and it was discovered to have lower storage, communication, and compute overheads. As a result, the proposed is best suited for WSNs with small nodes. © 2023 IEEE.

关键词： Sensor nodes

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Reputation systems for Supply Chains: The Challenge of Achieving Privacy Preservation 20th

Reputation Systems for Supply Chains: The Challenge of Achie...

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20th EAI international Conference on Mobile and Ubiquitous systems - Computing Networking and Services (EAI MobiQuitous)

作者： Bader, Lennart Pennekamp, Jan Thevaraj, Emildeon Spiss, Maria Kanhere, Salil S. Wehrle, Klaus Fraunhofer FKIE Cyber Anal & Def Wachtberg Germany Rhein Westfal TH Aachen Commun & Distributed Syst Aachen Germany Rhein Westfal TH Aachen Inst Ind Management Aachen Germany Univ New South Wales Sch Comp Sci & Engn Sydney NSW Australia

ISBN: (纸本)9783031639883;9783031639890

Consumers frequently interact with reputation systems to rate products, services, and deliveries. While past research extensively studied different conceptual approaches to realize such systems securely and privacy-preservingly, these concepts are not yet in use in business-to-business environments. In this paper, (1) we thus outline which specific challenges privacy-cautious stakeholders in volatile supply chain networks introduce, (2) give an overview of the diverse landscape of privacy-preserving reputation systems and their properties, and (3) based on well-established concepts from supply chain information systems and cryptography, we further propose an initial concept that accounts for the aforementioned challenges by utilizing fully homomorphic encryption. For future work, we identify the need of evaluating whether novel systems address the supply chain-specific privacy and confidentiality needs.

关键词： SCM confidentiality anonymity voter votee FHE

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Using Virtualization Approaches to Solve Deep Learning Problems in Voluntary distributed Computing Projects 9th

Using Virtualization Approaches to Solve Deep Learning Probl...

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9th Russian Supercomputing Days international Conference, RuSCDays 2023

作者： Kurochkin, Ilya Papanov, Valeriy Institute for Information Transmission Problems of Russian Academy of Sciences Moscow Russia The National University of Science and Technology MISIS Moscow Russia

ISBN: (纸本)9783031494345

The task of training deep neural networks on a large amount of data requires a lot of resources. The solution of such a problem is often impossible to carry out on one computing device in an adequate time. distributed computing systems can be used to solve deep learning problems. Such systems may consist of heterogeneous computing nodes with different computing power. To implement deep learning on a distributed heterogeneous system, it is necessary to solve the problem of utilization of all available resources. The solution to this problem is to configure the task delivery system of a distributed system. And to expand the number of computing nodes involved, it is necessary to use virtualization. The article discusses two types of virtualization for grid systems when solving deep learning problems. The features of the implementation of computational applications for training deep neural networks for solving the problem of image classification are discussed. The results of distributed deep learning on a public grid system are discussed. A comparative analysis of two virtualization approaches is given. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Virtualization

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Communication-Efficient Federated Learning in Drone-Assisted IoT networks: Path Planning and Enhanced Knowledge Distillation Techniques 34

Communication-Efficient Federated Learning in Drone-Assisted...

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IEEE 34th Annual international symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Gad, Gad Farrag, Aya Fadlullah, Zubair Md Fouda, Mostafa M. Lakehead Univ Dept Comp Sci Thunder Bay ON Canada Western Univ Dept Comp Sci London ON Canada Idaho State Univ Dept Elect & Comp Engn Pocatello ID USA Ctr Adv Energy Studies CAES Idaho Falls ID USA

ISBN: (纸本)9781665464833

As 5G and beyond networks continue to proliferate, intelligent monitoring systems are becoming increasingly prevalent. However, geographically isolated regions with sparse populations still face difficulties in accessing these technologies due to infrastructure deployment challenges. Additionally, the high cost and unreliability of satellite Internet services make them less appealing. This paper studies the challenges of droneaided networks and presents a communication-efficient Federated Learning (FL) system on a drone-aided Internet of Things (IoT) network to facilitate health analysis in rural areas over LoRa wireless links. The proposed approach consists of two primary components. Firstly, optimizing the drone's trajectory is theoretically formulated as a modified version of the Traveling Salesman Problem (TSP), with the Self-Organizing Map (SOM) algorithm employed for effective route planning. Secondly, the Knowledge Distillation (KD)-based FL algorithm is utilized to reduce communication overhead by leveraging soft labels. The quality of drone routes generated by the SOM is evaluated on multi-scale maps with pre-determined optimal paths. The experiments reveal SOM's ability to accurately represent node topologies and yield cost-effective Hamiltonian cycles. The KD-based FL proves to be more efficient in terms of communication than FedAvg as the former exchanges soft labels while the latter exchanges model weights, thus reducing drone waiting time and battery consumption. We showcase the performance of our KD-based FL algorithm using Human Activity Recognition (HAR) datasets, illustrating a communication-efficient alternative for distributed learning, offering competitive performance leveraging a shared dataset for knowledge transfer among IoT devices.

关键词： Deep learning UAV networks drone-aided LoRa networks edge devices Federated Learning (FL) digital health and well-being Knowledge Distillation Self-Organizing Maps.

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Optimizing Transformer Training Based on Computation and Accessing Memory Features with Deep Learning Processor 29

Optimizing Transformer Training Based on Computation and Acc...

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29th IEEE international Conference on Parallel and distributed systems, ICPADS 2023

作者： Lei, Zhou Fang, Qin Qian, Jingfeng Chen, Shengbo Xu, Qingguo Yang, Ninghua Shanghai University School of Computer Engineering and Science Shanghai200444 China Ningbo Zhongke Intelligent Computing Technology Co. Ltd Zhejiang315100 China

ISBN: (纸本)9798350330717

The Transformer model, which has significantly advanced natural language processing and computer vision, overcomes the limitations of recurrent neural networks and convolutional neural networks. However, it faces challenges with computational efficiency and memory management due to complex computations and variable-length inputs. Despite research efforts, these issues persist. This paper presents a novel optimization of the Transformer model, following an indepth analysis of its computational graph structure. Firstly, we utilize Deep Computing Unit (DCU) as our hardware platform. Secondly, we optimize element-wise and reduction operators through operator fusion and rewriting. Thirdly, we develop a fine-grained memory management algorithm using a greedy strategy. As a result, the training speed of the Transformer model increases by 1.2x - 1.4x without compromising accuracy. © 2023 IEEE.

关键词： computation optimization DCU memory management Transfomer

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LBCB: One-sided RDMA-based distributed B+ tree Index with Low Bandwidth Consumption 22

LBCB: One-sided RDMA-based Distributed B+ tree Index with Lo...

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22nd IEEE international symposium on Parallel and distributed Processing with Applications, ISPA 2024

作者： Liu, Jibo Xi, Rui Cao, Qinzhen Nie, Xiaowen Hou, Zhuohan University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331509712

Disaggregated memory architecture segregates computing and memory resources into distinct pools interconnected by a high-speed one-sided RDMA (Remote Direct Memory Access) network, enhancing memory utilization, reducing costs, and facilitating elastic scaling of computing and memory resources. However, optimizing index structures to maximize the benefits of this framework poses significant challenges. Despite progress in B+ tree indexes for disaggregated memory systems, they still suffer from severe write and read amplification issues, which impede latency and throughput *** this paper, we propose LBCB, a B+ tree index for disaggregated memory, which significantly reduces the bandwidth consumption of index operations. First, LBCB introduces an RDMA friendly B+ tree leaf node structure, which improves concurrency while reducing the bandwidth consumption. Second, LBCB designs a logical fusion FAA lock that synchronizes more information with a single RDMA communication, significantly reducing the number of RDMA network round trips. Finally, LBCB optimistically compresses the critical path of index operations, further optimizing latency and throughput. Evaluation results show that compared with the state-of-the-art distributed B+ tree index, the write performance of LBCB is improved by 1.42 times. © 2024 IEEE.

关键词： Memory architecture

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The Hardware and Software Part of a distributed Decentralized systems Detection Malicious Software 4

The Hardware and Software Part of a Distributed Decentralize...

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4th international Workshop on Intelligent Information Technologies and systems of Information Security, IntellTSIS 2023

作者： Savenko, Oleg Stetsyuk, Mykola Stetsyuk, Yuriy Kashtalian, Antonina Savenko, Bohdan Khmelnytskyi National University Institutska str. 11 Khmelnytskyi29016 Ukraine

The paper analyzes the problems that arise from ensuring the security and protection of information in corporate networks of enterprises. A variety of tools are used to detect malware in the computer of the power grids of enterprises. This approach makes it possible to improve the security and protection of information resources of enterprises. For corporate networks of enterprises, such tools are usually distributed. Attackers have knowledge of standard known tools detection of malware in corporate networks, and also have the appropriate means to carry out attacks by various methods. Therefore, to counter them in corporate networks, not only standard sets of network screens, intrusion detection systems, attack prevention systems, anti-virus tools, but also various means should be used. Such tools can baits developed individually for a specific corporate network, malware detection systems. In this case, it will be difficult for the attacker to go unnoticed when trying to invade the corporate network. The use of exclusively anti-malware or malware detection tools affects the effectiveness of the protection process and its effectiveness compared to hardware and software. The work proposes to improve the security and protection of information in corporate networks to use the individual means of users of the corporate network and, at the same time, they would be part of the malware detection system. These individual means of identifying users in a corporate network would become part of one large sensor at the same time. This sensor would be a decentralized distributed malware detection system. Computational actions would be sent to a certain part of the component, and they, in turn, would use hardware and software to support the operation of the system as a whole and would make calculations in them. If such a hardware and software element of the system components would give an incorrect result compared to the rest, then the system itself would respond to the actions of

关键词： Malware

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User Mobility Prediction in 5G networks Using Recurrent Neural networks

User Mobility Prediction in 5G Networks Using Recurrent Neur...

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2024 IEEE international Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Shrestha, Anubhav Sharma, Vikalp Hussein, Layth Aishwarya, M. Satyanarayana, A.N. Saimanohar, T. Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Ies College of Technology Department of Computer Science & Engineering M.P. Bhopal India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Sreenidhi Institute of Science and Technology Ece Department Hyderabad India Saveetha Institute of Medical and Technical Sciences Department CSE-AI Tamilnadu Chennai India

ISBN: (纸本)9798350390759

The use of new possibilities introduced by 5G networks also creates new problems and concerns, specifically in the field of user mobility in wireless communication systems. In this paper, the Authors investigate the effectiveness of Recurrent Neural networks (RNNs) in the mobility prediction context in order to exploit the temporal component in the users movement data for improved prediction performance. To drive this process, the input datasets are obtained from the GeoLife GPS Trajectories dataset, then follow the data pre-processing steps, constructing an LSTM-based RNN model, and using Python with TensorFlow and Keras. From the analyses, we detected that the accomplishment of using LSTM and hybrid RNN-CNN is significant and higher than the traditional approaches of EN, RFM, and CL. The test results demonstrate that the LSTM model can reduce the MAE and RMSE values by 19. 49% and 14. 82%, respectively. However, the execution of these models has some issues that include compute time, instantaneous prediction that is needed in many real-world applications, and case. Such problems can be solved with the help of such approaches as edge computing and distributed processing. Besides, it is pivotal to put measures in place to maintain the confidentiality and security of user information. This paper highlights the applicative potential of RNNs in redesigning user mobility prediction in 5G networks, and thus helping toward a better ownership of the network players and assets. © 2024 IEEE.

关键词： 5G mobile communication systems

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Cybersecurity Threat Detection in Smart Cities using Box Plot Sampling Isolation Forest 1

Cybersecurity Threat Detection in Smart Cities using Box Plo...

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1st international Conference on Intelligent systems and Computational networks, ICISCN 2025

作者： Shankari, S. Uma Mohameed, Haeedir Kulkarni, Megha Aravindh, S. Purushotham, N. Srm Institute of Science and Technology Department of Computer Science and Applications Ramapuram India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Civil Engineering Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Mohan Babu University Department of Computer Science and Engineering Tirupati India

ISBN: (纸本)9798331529246

Now a days, the standard of living in urban areas is advanced by leveraging technologies such as Cyber-Physical systems (CPS) and Internet of Things (IoT) with physical infrastructures. However, smart cities highly rely on digital ecosystems, which makes them vulnerable to various cyber threats such as distributed Denial of Service (DDoS) which leads in violations of privacy and threats to safety of citizens. To develop an effective and efficient system for detection of cybersecurity threats, Box plot Sampling Isolation Forest (BSIF) is proposed. Initially, the data was collected from the Botnet Traffic attack in Internet of Things (BoT-IoT) dataset which contains various normal traffic and Botnet traffic data. After that, Label Encoding (LE) was used for preprocessing the data, which assigns numerical labels to each category in the dataset. Furthermore, Recursive Feature Elimination (RFE) technique was utilized for iterative extraction and selection of features based on their weights is performed by Decision Tree (DT). Finally, the proposed BSIF model is used for detection of cybersecurity threats associated with smart cities, by leveraging box plot sampling method. The proposed obtain better results with 99.74% Precision, 99.92% Accuracy, 99.87% Recall and 99.84% F1-score, compared with Smart Attacks Learning Machine Advisor (SALMA) respectively. © 2025 IEEE.

关键词： Bot (Internet)

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