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Performance Evaluation of Consensus Algorithms for Permissioned Blockchain networks

Performance Evaluation of Consensus Algorithms for Permissio...

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international conference on Computing and Networking Technology (ICCNT)

作者： Triveni Kalwad Vinuta Aramani Needa Fatima Attar Neha Raikar Narayan D G Manjula Pawar Pooja Shettar Department of Computer Science and Engineering KLE Technological University Hubballi India

The ever-evolving technological landscape demands systems that can ensure trust, security, transparency, and traceability. To meet these requirements, blockchain technology has emerged that uses decentralized ledgers to keep transaction records. It utilizes cryptography to interconnect devices, and once recorded, data stored on a blockchain cannot be altered by any unauthorized parties, thereby enhancing its security features. Consensus in blockchain refers to the process of achieving agreement among a distributed network of nodes on the current state of the blockchain ledger. No centralized body is in charge of validating and approving transactions in a decentralized blockchain network. To guarantee that every node in the network has a consistent and accurate copy of the ledger, the consensus mechanism is used. By simulating the time to attain consensus with the NS-3 network simulator, we assess the performance of the consensus algorithms Raft and PBFT in this paper. This paper considers the effectiveness of the Raft and PBFT consensus algorithms by simulating the time taken to reach consensus using the NS-3 network simulator. Based on the evaluation results, it was found that the performance of PBFT is on average 30% better than RAFT with varying message sizes, transaction speeds, and the number of nodes.

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Detecting DDoS Attacks in Software-Defined Networking through the Utilization of Supervised and Unsupervised Learning Techniques

Detecting DDoS Attacks in Software-Defined Networking throug...

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international conference on Computing and Networking Technology (ICCNT)

作者： T Sathwik Tadi Sai Vishruth Reddy Venkat Rohith Gangavarapu Sreebha Bhaskaran Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Given the dynamic nature of SDN environments, it is necessary to quickly recognize distributed Denial of Service (DDoS) attacks to maintain our network’s integrity and ensure uninterrupted service delivery. However, the centralized control plane of SDN makes it particularly vulnerable, as a compromise here can allow attackers to manipulate network traffic and configurations extensively. The traditional detection methods often have trouble keeping up with these attacks’ dynamic nature, resulting in high false positive rates and poor response. This paper proposes an approach that uses supervised and unsupervised learning techniques to enhance DDoS attack detection capabilities in SDN infrastructures. Using Mininet and Ryu, our method involves collecting network traffic data, extracting relevant features, and constructing classification models based on labelled datasets that can identify typical attack patterns. Additionally, these initiatives are supported by unsupervised learning approaches like clustering or anomaly detection, which help reveal new types of attack signatures not seen before and abnormal behaviours within networks caused by them. As a result, what we have done here enables real-time detection against DDoS through seamless integration into the Ryu controller, thus strengthening the security posture against emerging threats in cyberspace targeting SDNs.

关键词： Supervised learning Denial-of-service attack Feature extraction Real-time systems Topology computer crime Software defined networking Unsupervised learning Random forests Long short term memory

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Smart Mining and Consensus Protocols in Blockchain-enabled Industrial Cyber-Physical systems: A Survey 23

Smart Mining and Consensus Protocols in Blockchain-enabled I...

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Proceedings of the 7th international conference on Future networks and distributed systems

作者： Salah Eddine Elgharbi Samir Ouchani Abderrahim Ait Wakrime Yassine Ouhammou La Rochelle University France CESI Lineact France Computer Science Department Mohammed V University in Rabat Morocco LIAS/ISAE France

ISBN: (纸本)9798400709036

The integration of emerging technologies into industrial systems is essential for facilitating collaboration among diverse stakeholders and optimizing the management of extensive assets. Efficient decision-making processes in contemporary autonomous systems greatly benefit from the substantial volumes of collected data. Addressing the challenges of decentralized data collection, the incorporation of blockchains emerges as a viable solution. Blockchains, in their decentralized structure, facilitate the trustworthy and transparent sharing of data. A sophisticated blockchain implementation has the potential to address real-world industrial challenges, spanning resource management, data collection, and analysis. This survey paper has dual objectives. Firstly, it aims to investigate existing decentralized architectures within industrial and production systems, primarily influenced by Industry 4.0 principles, ensuring the security and data privacy of various stakeholders. Secondly, the study delves into the diverse strategies employed in mining algorithms, classifying them based on the underlying architecture. These objectives complement each other through a comprehensive survey of decentralized architectures in industrial systems, an in-depth analysis and comparison of mining algorithms and protocols, and the practical application of the most effective strategy within a real industrial system. This work contributes to the understanding and advancement of smart mining and consensus protocols in Blockchain-enabled Industrial Cyber-Physical systems, providing insights for future developments in research area.

关键词： Blockchain

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Big Data Framework to Detect and Mitigate distributed Denial of Service(DDoS) Attacks in Software-Defined networks (SDN)

Big Data Framework to Detect and Mitigate Distributed Denial...

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Electrical, computer, Communications and Mechatronics Engineering (ICECCME), international conference on

作者： Amirreza Fazely Hamedani Muzzamil Aziz Philipp Wieder Ramin Yahyapour Department of Computer Science Göettingen University Göttingen Germany eScience Group Gesellschaft für Wissenschaftliche Datenverarbeitung mbH Göttingen (GWDG) Göttingen Germany

The software-defined network is in recent years come into the sight of so many network designers as a successor to the traditional network. This type of network is vulnerable to DDoS attacks, targeting a different part of the SDN network architecture by continuously injecting fake flows. It imposes substantial processing on the controller, and the result ultimately leads to the inaccessibility of the controller and the lack of network service to legitimate *** paper proposes a scalable and reliable real-time DDoS attack detection and mitigation framework using machine learning incorporated with a big data pipeline infrastructure for the SDN environment. The framework supports the detection of DDoS attacks with the ability of processing and analyzing the huge amount of network traffic in near real-time. The framework employs the power of big data solutions, such as Apache Kafka, and Apache Spark in order to realize a scalable pipeline for big data *** main objective of this research is to offer a vital supporter which can assist network security systems in addressing the DDoS threats in the SDN environment. For this purpose, machine learning algorithms are also utilized to provide a viable opportunity for classifying network data and detecting DDoS attacks in this regard.

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Various Modulation Types Classification on the Air by Convolutional Neural Network 23

Various Modulation Types Classification on the Air by Convol...

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Proceedings of the 7th international conference on Future networks and distributed systems

作者： Yousif I Hammadi Mokhalad Abdulameer Kadhim Alsaeedi Omar Abdulkareem Mahmood Aws Zuhair Sameen Mohammed Saleh Ali Muthanna Ahmed Aziz Ammar Muthanna Dep. of Medical Instruments Engineering Techniques Bilad Alrafidain University College Iraq Department of Communications Engineering University of Diyala Iraq Department of Medical Instrumentation Engineering Techniques Al-Farahidi University Iraq Institute of Computer Technologies and Information Security Southern Federal University Russia Department of computer science Benha university Egypt and International Business Management Department Tashkent State University of Economics Uzbekistan Department of Telecommunication Networks and Data Transmission The Bonch Bruevich Saint Petersburg State University of Telecommunications Russia and Department of Applied Probability and Informatics Peoples Friendship University of Russia Russia

ISBN: (纸本)9798400709036

Deep learning (DL), a relatively recent AI technique, has been successfully applied to the problem of automated modulation categorization (AMC), with promising results. An essential part of developing the spectrum-sensing capabilities needed by software-defined radio is carrying out radio signal classification jobs, which we did in this research by introducing a better deep neural architecture. Different detection techniques were utilized in the literature; however, DL neural networks are recently utilized in this field. Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) which is one variation of the RNN network, and other techniques are some of the employed DL architectures in the field of AMC. In this paper, Convolutional Neural Network (CNN) model has been built to automatically classify ten-modulation formats correctly. The suggested CNN network is constructed mainly of six-Convolutional layers. The overall number of trainable parameters in the proposed network is 1,757,962. Thus, the network accuracy, which was achieved during the training reached more than 90%. Python programming packages have been used to implement the suggested approach using *** platform, which provides wide-range of facilities.

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DDoS Flood Detection and Mitigation using SDN and Network Ingress Filtering - an Experiment Report

DDoS Flood Detection and Mitigation using SDN and Network In...

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Electronic Communications, Internet of Things and Big Data (ICEIB), international conference on

作者： Sebastien Marleau Prianna Rahman Chung–Horng Lung Department of Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

distributed Denial of Service (DDoS) attacks are a common security threat to overwhelm a target server. IP source spoofing is a common approach used for DDoS attacks. It is claimed that all large DDoS attacks require IP spoofing and around 20% of the Internet still allows IP spoofing. DDoS attacks can also be launched much more easily using low-cost Internet of Things (IoT) devices. DDoS attacks originating from IoT devices have increased 5-fold in a year. Software-defined networking (SDN) has been proposed as a new paradigm to reduce complexity. However, security issues are still challenging for SDN, as DDoS attacks on the controller could fail the entire network. A mechanism that can detect and mitigate DDoS attacks on SDN is crucial. Ingress filtering has been adopted to reduce the probability of exploiting a network to launch an attack. This, among other things, mandates the dropping of packets if the source IP address of incoming packets is inconsistent with the configured one. Best Current Practice 38 (BCP 38) is a network ingress filtering technique to prevent source IP spoofing. By validating incoming packets, their IP addresses, and possibly other attributes at the ingress devices, it provides a first line of defense against suspicious traffic. We present an approach to DDoS detection and mitigation using BCP 38 and SDN. To evaluate the effectiveness of the proposed approach, a simple method was designed to detect and mitigate DDoS floods using BCP 38 and SDN. Experiments were conducted using Mininet and the Ryu controller. The result demonstrated that the proposed approach corresponded to DDoS attacks with IP spoofing efficiently and effectively.

关键词： Prevention and mitigation Denial-of-service attack Information filters IP networks Internet of Things security Servers

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Digitization and Secure Process of Forensic Data Management Using Blockchain

Digitization and Secure Process of Forensic Data Management ...

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IEEE international conference on System, Computation, Automation and Networking (ICSCAN)

作者： R. Indhumathi P. Mathivanan K. Mahalakshmi K. Subha Nanda S. Krishna Department of Computer Science and Engineering Manakula Vinayagar Institute of Technology Puducherry India Department of Computer Science and Business Systems KIT - Kalaignarkarunanidhi Institute of Technology Coimbatore India Department of Computer Science and Engineering Surya Group of Institutions Villuppuram India

Cyber threats and data manipulation are the main concerns in this technological society. The investigator finds it challenging to submit evidence to the court because of the information breach as continuity documentation of records is very critical for safeguarding against criminal events. Blockchain-based digital forensic data management systems have been developed to deal with this issue of Privacy preservation for evidence items. Blockchain technology provides authentication, immutability, traceability, resilience, and distributed trust for forensic data. In order to ensure security and ease of use, the system utilizes onion architecture. Data can be stored and analysed whenever required. They provide trust between the examiners and the system. The system is a multi-platform system that provides effective usage where it can be accessed and used from anywhere. The honest information is make sure to follow the crime investigator of the steps. if we use the propose system of block chain, it is can't be altered the evidence during the submission of court. The storing data of each block is verify and added to the blockchian. The creation of safe database was implemented by a decentralised network with bitcoin and networks of cryptocurrency. In proposed system, the blockchain technology helps in can't able to access and altered the data during the investigation periods. This system is used to implements the easily accessed and used in anywhere.

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Comparative Analysis of Communication Protocols in Blockchain-IoT systems for Optimizing IoT Data Flow Efficiency

Comparative Analysis of Communication Protocols in Blockchai...

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Advanced Innovations in Smart Cities (ICAISC), international conference on

作者： Mnar Alnaghes Hong Shen School of Computer Science University of Adelaide Adelaide Australia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

The Internet of Things (IoT) is considered an evolving technology, consisting of different types of connected devices that have sensors, specialized hardware, and software. However, considerable challenges related to user data privacy, security, and device integrity are presented by this swift expansion, with digital identity being identified as a significant concern in the online landscape. Established by the World Wide Web Consortium (W3C) [2], Decentralized Identifiers (DIDs) represent a standardized format for defining identity. A peer-to-peer model for managing identity and credentials through self-sovereign identity is facilitated by DIDs, in conjunction with blockchain technology and public-key cryptography. The development of verifiable credentials is enabled by the combination of DIDs and blockchain technology, allowing for the verification of the identities of transacting parties through distributed identity registration and the identification of cryptographic keys necessary for validating electronic authentication during transactions. This integrated framework is regarded as both reliable and trusted. Device registration, verification, and credential revocation are enabled by it. In addition, security is ensured, the privacy of participating nodes is respected, and data integrity is upheld to address several existing challenges, such as data scalability and confidentiality. Trusted management and transaction of IoT data are ensured by the presented IoT data platform. Blockchain, decentralized identifiers, and public key cryptography are integrated by the authors to develop this platform. Self-sovereign identity is granted to each participating device, and dynamic changes in components like consensus algorithms and databases are supported by key features. Different IoT communication protocols are evaluated within a decentralized blockchain infrastructure for identity verification by the authors, focusing on interactions between IoT devices and networked se

关键词： Wireless communication Data privacy Wireless sensor networks Protocols W3C Public key cryptography Blockchains Peer-to-peer computing Internet of Things Optimization

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distributed Collaborative Anomaly Detection for Trusted Digital Twin Vehicular Edge networks 1

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16th international conference on Wireless Algorithms, systems, and Applications (WASA)

作者： Liu, Jiawen Zhang, Shuaipeng Liu, Hong Zhang, Yan East China Normal Univ Sch Software Engn Shanghai Peoples R China Univ Oslo Dept Informat Oslo Norway

ISBN: (数字)9783030861308

ISBN: (纸本)9783030861308;9783030861292

The vehicular networks are vulnerable to cyber security attacks due to the vehicles' large attack surface. Anomaly detection is an effective means to deal with this kind of attack. Due to the vehicle's limited computation resources, the vehicular edge network (VEN) has been proposed provide additional computing power while meeting the demand of low latency. However, the time-space limitation of edge computing prevents the vehicle data from being fully utilized. To solve this problem, a digital twin vehicular edge networks (DITVEN) is proposed. The distributed trust evaluation is established based on the trust chain transitivity and aggregation for edge computing units and digital twins to ensure the credibility of digital twins. The local reachability density and outlier factor are introduced for the time awareness anomaly detection. The curl and divergence based elements are utilized to achieve the space awareness anomaly detection. The mutual trust evaluation and anomaly detection is implemented for performance analysis, which indicates that the proposed scheme is suitable for digital twin vehicular applications.

关键词： Anomaly detection Digital twin Trust evaluation

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ISecIT 2021 - Proceedings of the 3rd international conference on Information security and Information Technologies, co-located with 1st international Forum "Digital Reality", DRForum 2021

ISecIT 2021 - Proceedings of the 3rd International Conferenc...

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3rd international Scientific and Practical conference on Information security and Information Technologies, ISecIT 2021

The proceedings contain 43 papers. The topics discussed include: development of a method for detecting deviations in the nature of traffic from the elements of the communication network;method of recognition sarcasm in English communication with the application of information technologies;using the sum of real type functions to encrypt messages;method of calculation of information protection from clusterization ratio in social networks;modification of query processing methods in distributed databases using fractal trees;peculiar properties of creating a system of support to make anti- crisis decisions by experts of the situational center at the cyber protection object;the basic principles of the compact video frames representation technology, which are presented in a differential form in computer systems;and methodical approach to the development of a mathematical model for calculating the combat potentials of strike unmanned aircraft.

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