Cooperative Detection Method for DDoS Attacks Based on Blockchain

Cheng, Jieren; Yao, Xinzhi; Li, Hui; Lu, Hao; Xiong, Naixue; Luo, Ping; Liu, Le; Guo, Hao; Feng, Wen

doi:10.32604/csse.2022.025668

Cited by 6 publications

(3 citation statements)

References 41 publications

(41 reference statements)

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“…The correlation was computed, followed by the removal of invalid columns. The decision tree filtering method was then employed to eliminate 14 features from the initial 87 feature quantities in the CIC-DDoS2019 dataset [35]. The features are categorized based on their level of significance.…”

Section: Data Pre-processmentioning

confidence: 99%

An Adaptive DDoS Detection and Classification Method in Blockchain Using an Integrated Multi-Models

Li,

Cheng,

Ruan

et al. 2023

CMC

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With the rising adoption of blockchain technology due to its decentralized, secure, and transparent features, ensuring its resilience against network threats, especially Distributed Denial of Service (DDoS) attacks, is crucial. This research addresses the vulnerability of blockchain systems to DDoS assaults, which undermine their core decentralized characteristics, posing threats to their security and reliability. We have devised a novel adaptive integration technique for the detection and identification of varied DDoS attacks. To ensure the robustness and validity of our approach, a dataset amalgamating multiple DDoS attacks was derived from the CIC-DDoS2019 dataset. Using this, our methodology was applied to detect DDoS threats and further classify them into seven unique attack subcategories. To cope with the broad spectrum of DDoS attack variations, a holistic framework has been proposed that seamlessly integrates five machine learning models: Gate Recurrent Unit (GRU), Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM), Deep Neural Networks (DNN), and Support Vector Machine (SVM). The innovative aspect of our framework is the introduction of a dynamic weight adjustment mechanism, enhancing the system's adaptability. Experimental results substantiate the superiority of our ensemble method in comparison to singular models across various evaluation metrics. The framework displayed remarkable accuracy, with rates reaching 99.71% for detection and 87.62% for classification tasks. By developing a comprehensive and adaptive methodology, this study paves the way for strengthening the defense mechanisms of blockchain systems against DDoS attacks. The ensemble approach, combined with the dynamic weight adjustment, offers promise in ensuring blockchain's enduring security and trustworthiness.

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Section: Data Pre-processmentioning

confidence: 99%

An Adaptive DDoS Detection and Classification Method in Blockchain Using an Integrated Multi-Models

Li,

Cheng,

Ruan

et al. 2023

CMC

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“…Wei Ren et al [ 28 ], MING LI et al [ 29 ] used blockchain + IPFS to store unstructured data, so as to enhance the security and trustworthiness of data, including agricultural sampling data protection for IoT networks and image big data protection based on compressed sensing. Jieren Cheng et al [ 30 ], Chin-Ling Chen et al [ 31 ] and have proposed applications of blockchain + IPFS systems from the perspective of data storage and transmission, including application scenarios such as collaborative DDoS attack detection, anonymization protocols, and secure data transmission schemes. In the research direction of enhancing the credibility of deposition, the research of Randhir Kumar et al [ 32 ], Jin Sun et al [ 33 ], Shaojing Fu et al [ 34 ], and Moses Arhinful Acquah et al [ 35 ] use the advantages of blockchain + IPFS technology to solve the existing trust problem of data deposition.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Credible Carbon Footprint Traceability System for Low Carbon Economy Using Blockchain Technology

Shen

Bao

et al. 2022

IJERPH

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To achieve the goal of carbon neutrality, many countries have established regional carbon emission trading markets and tried to build a low-carbon economic system. At present, the implementation of carbon emission trading and low-carbon economic systems faces many challenges such as manipulation, corruption, opacity, lack of trust, and lack of data tracking means. The application of blockchain technology can perfectly solve the above problems. However, the data recorded on a blockchain are often multi-type and heterogeneous, and users at different levels such as regulators, enterprises, and consumers have different requirements for data types and granularity. This requires a quick and trustworthy method for monitoring the carbon footprint of enterprises and products. In this paper, the carbon footprint traceability of enterprises and products is taken as an application scenario, and the distributed traceability concept of “traceability off the chain and verification on the chain” is adopted. By reconstructing the pointer of the file structure of the distributed storage, an interactive traceability structure supporting type filtering is constructed, which enables fast retrieval and locating of carbon emission data in the mixed data on the chain. The experimental results show that using the interactive traceability structure that supports type filtering for traceability not only releases the computing power of full nodes but also greatly improves the traceability efficiency of the long-span transaction chain. The proposed carbon footprint traceability system can rapidly trace and track data on an enterprise’s and a product’s carbon footprint, as well as meet the needs of users at all levels for traceability. It also offers more advantages when handling large amounts of data requests.

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“…The rapid advancement of deep learning algorithms in processing point cloud data enables the characterization of 3D particle shapes through artificial intelligence (AI) [23][24][25]. Deep learning offers a distinct advantage by allowing computers to comprehend and process the angular and spherical characteristics of sand particles in a manner akin to human understanding.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional sand particle shape characterization by PointConv-Transformer deep learning

Xi,

Zheng,

Gao

et al. 2024

Preprint

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Angularity and sphericity of sand particles significantly influence the shear strength, compressibility, void structure, and deformation behavior of soils. However, current computational geometry (CG) algorithms face challenges in simultaneously charactering angularity and sphericity, as well as handling defective granular three-dimensional(3D) mesh. To address these efficiency and robustness limitations, this paper introduces the PointConv-Transformer deep learning algorithm for characterization and classification of sand particles point cloud. The PointConv efficiently captures local features of angularity and sphericity. Subsequently, the Transformer integrates these local features into global features to form the judgement basis for classification. The 4800 particle point clouds are labeled with 12 angularity-sphericity classes. The PointConv-Transformer model, trained on the produced dataset, achieves an automatic classification accuracy of 96.65%. Furthermore, we explore the impact of normal vectors and point cloud size on the performance of the PointConv-Transformer model. Experimental results demonstrate that the optimal performance of the trained model is achieved when the point cloud size is 2000 and includes normal vectors. Finally, compared to traditional 3D CG, the classification results align closely in volume, surface area, and convex hull volume metrics. As the number of classified particles increases, the advantage in classification efficiency becomes more pronounced.

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Cooperative Detection Method for DDoS Attacks Based on Blockchain

Cited by 6 publications

References 41 publications

An Adaptive DDoS Detection and Classification Method in Blockchain Using an Integrated Multi-Models

An Adaptive DDoS Detection and Classification Method in Blockchain Using an Integrated Multi-Models

A Novel Credible Carbon Footprint Traceability System for Low Carbon Economy Using Blockchain Technology

Three-dimensional sand particle shape characterization by PointConv-Transformer deep learning

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