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Approaching Solutions to Blockchain Security Trilemma and Consensus Mechanisms

Approaching Solutions to Blockchain Security Trilemma and Co...

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Advances in computing, Communication Control and networking (ICAC3N), international conference on

作者： Dipanshu Parashar Madhav Sharma Vishnu Sharma Prama Nand CSE Department GCET Greater Noida School of Engg. &Tech. Sharda University Greater Noida India

Blockchains have been referred to as revolutions that will enable the processing of data and transactions to move from a centralized party to a high-trust decentralized network of different verifying nodes. This paper represents multiple security factors involved in a successful blockchain and reviews blockchain scalability trilemma of decentralization, security, and speed in consideration with multiple consensus mechanisms with emphasis on performance and security. Further, the paper attempts to introduce Proof of Parity as a better way to run blockchain networks in multiple real-time scenarios. This also sheds light on security concerns in Hyperledger fabric or permissioned blockchains.

关键词： Privacy distributed ledger Scalability Smart contracts Proof of Work Fabrics Real-time systems

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Implementation of Hyperparameter Tuning Random Forest Algorithm in Machine Learning for SDN Security: An Innovative Exploration of DDoS Attack Detection

Implementation of Hyperparameter Tuning Random Forest Algori...

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Artificial Intelligence, Blockchain, Cloud computing, and Data Analytics (ICoABCD), international conference on

作者： Hijrah Nisya Sofia Naning Hertiana Yudha Purwanto School of Electrical Engineering Telkom University Bandung Indonesia

ISBN: (数字)9798350368451

ISBN: (纸本)9798350368468

Software Defined Network (SDN) represents a novel approach to network programming for the design, construction, and administration of computer networks. This approach entails the separation of the control plane from the data plane, with the objective of centralizing the network and consolidating all settings in the control plane. Network design frequently encounters challenges pertaining to network security and network attacks. SDN offers a number of advantages. One such advantage is that it allows the implementation of control functions at the controller level, thus positioning the controller as a key element of SDN. The centralized nature of SDN renders it susceptible to attacks, including distributed Denial of Service (DDo $S$ ) attack. Consequently, a security system is required to address the issue of attacks directed towards the SDN controller. DDoS attack detection system is created using a machine learning algorithm, namely Random Forest, whose algorithm performance is optimized using Hyperparameter Tuning to achieve high accuracy in the detection process. The InSDN dataset, comprising a total of 56 classes, was employed in this research, and the SelectFromModel feature selection method was utilized to identify crucial features within the InSDN dataset. The method was found to be effective in detecting DDoS attacks, with an accuracy value of 99.99%. The results demonstrate that Hyperparameter Tuning Random Forest is an effective method for detecting DDoS attacks on SDN.

关键词： Machine learning algorithms Accuracy Software algorithms Programming Network security Denial-of-service attack Software defined networking Computer crime Tuning Random forests

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Utilization of Augmented Reality Technology in Campus Promotion Using the Marker Based Tracking Method

Utilization of Augmented Reality Technology in Campus Promot...

引用

international conference on Informatics and computing (ICIC)

作者： Heny Pratiwi Muhammad Ibnu Sa’Ad Oktadius Giantoro STMIK Widya Cipta Dharma Information System Samarinda Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

This study aims to develop an Augmented Reality (AR) application as a more attractive and interactive promotional media for the STMIK Widya Cipta Dharma campus. By utilizing AR technology, it is hoped that information about facilities, study programs, and campus activities can be presented in a more attractive and easily understood way by prospective students and the general public. In addition to using conventional promotional methods such as social media and print media (brochures, posters, and billboards), the application of AR aims to enrich and increase the effectiveness of campus promotions. The methods used in this study include the development of an Android-based AR application with Canva software for interface design, Unity 2022 as the main program, and SketchUp for 3D modeling. The application development process follows a multimedia development model consisting of six stages: Concept, Design, Material Collection, Assembly, Testing, and Distribution. System testing is carried out using the Black Box and Beta Testing methods to ensure the application functions as expected. The results of this study indicate that the Implementation of Augmented Reality as a Promotional Media for the STMIK Widya Cipta Dharma Campus can be successfully completed using Unity 2022. This application is then distributed to the public who need information about STMIK Widya Cipta Dharma.

关键词： System testing Three-dimensional displays Social networking (online) Operating systems Two-dimensional displays Media User interfaces Three-dimensional printing Augmented reality Assembly

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Welcome Message from ICDCN 2022 conference Chairs

ACM International Conference Proceeding Series

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ACM international conference Proceeding Series 2022年 III页

作者： Singh, Pushpendra Wattenhofer, Roger Wu, Jie IIIT Delhi India ETH Zurich Switzerland Temple University United States

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From Federated Learning to Quantum Federated Learning for Space-Air-Ground Integrated Networks

From Federated Learning to Quantum Federated Learning for Sp...

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IEEE conference on Standards for Communications and networking (CSCN)

作者： Vu Khanh Quy Nguyen Minh Quy Tran Thi Hoai Shaba Shaon Md Raihan Uddin Tien Nguyen Dinh C. Nguyen Aryan Kaushik Periklis Chatzimisios Faculty of Information Technology Hung Yen University of Technology and Education Hungyen Vietnam College of Sciences and Engineering University of Tasmania Sandy Bay Hobart TAS Australia Department of Electrical and Computer Engineering University of Alabama in Huntsville USA Department of Electrical and Electronics Engineering Lac Hong University Vietnam Department of Computing & Mathematics Manchester Metropolitan University UK International Hellenic University Thessaloniki Greece University of New Mexico Albuquerque USA

ISBN: (数字)9798331507428

ISBN: (纸本)9798331507435

6G wireless networks are expected to provide seamless and data-based connections that cover space-air-ground and underwater networks. As a core partition of future 6G networks, Space-Air-Ground Integrated Networks (SAGIN) have been envisioned to provide countless real-time intelligent applications. To realize this, promoting AI techniques into SAGIN is an inevitable trend. Due to the distributed and heterogeneous architecture of SAGIN, federated learning (FL) and then quantum FL are emerging AI model training techniques for enabling future privacy-enhanced and computation-efficient SAGINs. In this work, we explore the vision of using FL/QFL in SAGINs. We present a few representative applications enabled by the integration of $F L$ and QFL in SAGINs. A case study of QFL over UAV networks is also given, showing the merit of quantum-enabled training approach over the conventional FL benchmark. Research challenges along with standardization for QFL adoption in future SAGINs are also highlighted.

关键词： 6G mobile communication Training Quantum computing Federated learning Wireless networks Computer architecture Space-air-ground integrated networks Real-time systems Artificial intelligence Standards

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STGCN-Based Link Flooding Attack Detection and Mitigation in Software-Defined Network

STGCN-Based Link Flooding Attack Detection and Mitigation in...

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Yue Li Runcheng Fang Qipeng Song Xilei Yang School of Cyber Engineering Xidian University Xi’an China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Link Flooding Attacks (LFA) are increasingly challenging the availability and stability of Software-Defined Networks (SDN), leveraging their distributed and covert nature to escape detection. Existing methods such as packet analysis and behavioral patterns struggle with pinpointing specific target attack paths due to the dynamic routing and distribution of attack traffic across multiple entry points, making it difficult to implement defensive measures effectively. This paper introduces a novel detection method using a Spatial-Temporal Graph Convolutional Network (STGCN), which combines the strengths of Convolutional Neural Networks (CNN) for temporal pattern recognition and Graph Neural Networks (GNN) for spatial topology analysis. Unlike traditional approaches, our model leverages In-Band Network Telemetry (INT) for real-time traffic and topology monitoring, enhancing our ability to pinpoint and mitigate LFAs. We innovatively transform the detection of attacked links into a dual-point anomaly detection problem, significantly increasing the accuracy of identifying compromised links. Experimental results demonstrate that our method not only achieves high detection accuracy but also ensures the efficient use of network resources, making it particularly effective for resource-constrained environments.

关键词： Accuracy Network topology Transforms Routing Stability analysis Real-time systems Topology Security Telemetry Software defined networking

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Enhanced Feature Selection for Superior Ensemble DDoS Detection in Software-Defined Networks

Enhanced Feature Selection for Superior Ensemble DDoS Detect...

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Future Machine Learning and Data Science (FMLDS), IEEE international conference on

作者： Abdussalam Ahmed Alashhab Aisha Edrah Maythem K. Abbas Mohamed Alashhab Mohamed Alngib Muhammad Yunis Daha Department of Computer Science Faculty of Information Technology Alasmarya Islamic University Zliten Libya Department of Electrical and Computer Engineering Faculty of Engineering Western University London Canada Asia Pacific University of Technology and Innovation School of Computing Kuala Lumpur Malaysia Faculty of Management and Information Technology Universiti Sultan Azlan Shah Kuala Kangsar Malaysia School of Engineering Ulster University Belfast United Kingdom

ISBN: (数字)9798350391213

ISBN: (纸本)9798350391220

distributed Denial-of-Service (DDoS) attacks disrupt networks by flooding systems with traffic from multiple sources, making real-time detection essential. Integrating Machine Learning (ML) with Software-Defined networking (SDN) presents a viable solution; however, existing ML methods often face challenges such as high false positive rates and difficulty detecting sophisticated attacks like low-rate and zero-day DDoS attacks due to inadequate training features. As DDoS attack methods evolve, continuous retraining of ML models becomes necessary. This paper introduces a feature selection approach for ensemble classifiers to improve DDoS detection in SDNs. Through correlation analysis and principal component analysis (PCA), the most relevant features are identified, leading to substantial improvements in model performance. Our evaluation, using diverse DDoS datasets, demonstrates that the proposed method achieves near-perfect accuracy, a 100% true positive rate, and minimal error rates, offering a highly effective solution for DDoS detection in SDNs.

关键词： Analytical models Adaptation models Accuracy Computational modeling Denial-of-service attack Feature extraction Real-time systems Computer crime Software defined networking Principal component analysis

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METER: An Ensemble DWT-based Method for Identifying Low-rate DDoS Attack in SDN 19

METER: An Ensemble DWT-based Method for Identifying Low-rate...

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19th IEEE international conference on Embedded and Ubiquitous computing (EUC)

作者： Wang, Cong Cui, Yunhe Qian, Qing Shen, Guowei Gao, Hongfeng Li, Saifei Guizhou Univ Sch Comp Sci & Technol Guizhou Prov Key Lab Publ Big Data Guiyang Peoples R China GuiZhou Univ Finance & Econ Sch Informat Guiyang Peoples R China Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu Peoples R China

ISBN: (纸本)9781665400367

As one of the next generation of network architectures, Software-Defined networking (SDN) decouples the forwarding and control function of the traditional network device. However, it also faces new threats from network attacks, such as the Low-rate distributed Denial of Service (L-DDoS) attack. To resist L-DDoS attack in SDN, this work proposes METER, an enseMble discrEte wavelet Transform-based method for idEntifying low-Rate DDoS attack in SDN. The rationale for METER is to identify L-DDoS attacks based on a new metric referred to as the Ensemble Wavelet Energy Entropy set (EWEEs), which is calculated by a novel ensemble DWT method. Firstly, the ensemble wavelet coefficients matrix is attained by METER using the ensemble DWT method. After that, METER combines the related entropy values with wavelet energy of the ensemble wavelet coefficients matrix to obtain EWEEs. Furthermore, to increase the precision of the L-DDoS detection method, machine learning methods are used to mine the correlation between EWEEs and L-DDoS attacks for identifying L-DDoS. The experiments were implemented on the RYU controller and Mininet, which demonstrates that METER outperforms the baseline method, in terms of precision, accuracy, F-score, recall, ROC curve, and P-R curve.

关键词： Software-Defined networking Low-rate distributed Denial of Service Ensemble Wavelet Energy Entropy set Ensemble DWT

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CTS Sketch: A Sketch Scheme for Precise Identification of Top-k Flows Combined with SDN

CTS Sketch: A Sketch Scheme for Precise Identification of To...

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international conference on Advanced Cloud and Big Data (CBD)

作者： Xiaolin Yang Kai Yang Hao Zhang Gaoming Jin Yifei Lu Changzhou Power Supply Branch of State Grid Jiangsu Electric Power Co. Ltd. China School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (数字)9798331511074

ISBN: (纸本)9798331511081

Identifying elephant flows in a network is crucial for network traffic measurement, significantly impacting congestion control optimization, anomaly detection, and traffic engineering. With increasing network link rates and limited on-chip storage space, existing algorithms face severe challenges in maintaining measurement accuracy. To address these issues, Sketch data structures have emerged, allowing the recording of all traffic characteristics within limited on-chip storage. However, due to the approximate nature of Sketch, its measurement accuracy often falls short of high precision requirements. In this paper, we propose a Sketch algorithm combined with an SDN (Software-defined networking) controller, termed CTS (Combine the Two-Stage SDN) Sketch, to enhance the accuracy of identifying top-k elephant flows through additional filtering. Furthermore, we conduct measurements exclusively at edge switches, uploading the distributed Sketch measurement results to the controller to reduce the load on central devices. By leveraging the advantages of edge computing, we capture and process traffic information promptly. Experimental results show that CTS Sketch achieves an accuracy rate of 99.99% with relatively small memory capacity, reducing error by approximately three orders of magnitude compared to existing algorithms.

关键词： Technological innovation Accuracy Flow production systems Telecommunication traffic Approximation algorithms System-on-chip Topology Recording Software defined networking Optimization

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Peak Shaving Approach of Distribution Network

Peak Shaving Approach of Distribution Network

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Smart Generation computing, Communication and networking (SMART GENCON), international conference on

作者： Rajib Baran Roy Sanath Alahakoon School of Engineering and Technology Central Queensland University Gladstone Australia

The modern power system network comprises distributed generations of renewable sources. The intermittent nature of renewable sources may not always support the peak demand of the electricity network; therefore the integration of storage batteries can support the peak demand for retaining power system stability. The battery of fast charging loads like electric vehicles (EV), electric ferries (EF) and others can also be used for peak shaving of the power system. The storage elements can provide FCAS (frequency control ancillary service) to the power system for restoring the system's stability. To justify the peak shaving option of storage battery, a 9-bus system electrical network is created by OpenDSS which analyzes the charging and discharging characteristics of the battery. The storage controller controls the storage batteries in two modes, one follow-time (discharging and charging) mode and another load-shape (charging and discharging) mode. The storage controller controls the sequence of operation of the batteries to meet the load demand of the electrical network. The proposed approach can be used for a large electrical network to manage the peak demand to retain power system stability.

关键词： Renewable energy sources Voltage fluctuations Distribution networks Power system stability Stability analysis Batteries distributed power generation

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