BCEAD: A Blockchain-Empowered Ensemble Anomaly Detection for Wireless Sensor Network via Isolation Forest

Yi, Xiaolan; Chen, Yuling; Qian, Xiaobin; Li, Tao; Lv, Xiao Ren

doi:10.1155/2021/9430132

Cited by 15 publications

(9 citation statements)

References 25 publications

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“…Yang et al [24] addressed the issue of centralized anomaly detection in WSNs, and the authors describe a blockchain-based ensemble anomaly detection (BCEAD) system that stores the model of a common anomaly detection method on the distributed ledger. Once a suitable block structure and consensus method have been developed, the scheme changed iteratively to increase detection and classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

Blockchain-Based Secure Localization against Malicious Nodes in IoT-Based Wireless Sensor Networks Using Federated Learning

Gebremariam

Panda

Indu

2023

Wireless Communications and Mobile Computing

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Wireless sensor networks are the core of the Internet of Things and are used in healthcare, locations, the military, and security. Threats to the security of wireless sensor networks built on the Internet of Things (IoT-WSNs) can come from a variety of sources. This study proposes secure attack localization and detection in IoT-WSNs to improve security and service delivery. The technique used blockchain-based cascade encryption and trust evaluation in a hierarchical design to generate blockchain trust values before beacon nodes broadcast data to the base station. Simulation results reveal that cascading encryption and feature assessment measure the trust value of nodes by rewarding each other for service provisioning and trust by removing malicious nodes that reduce localization accuracy and quality of service in the network. Federated machine learning improves data security and transmission by merging raw device data and placing malicious threats in the blockchain. Malicious nodes are classified through federated learning. Federated learning combines hybrid random forest, gradient boost, ensemble learning, K -means clustering, and support vector machine approaches to classify harmful nodes via a feature assessment process. Comparing the proposed system to current ones shows an average detection and classification accuracy of 100% for binary and 99.95% for multiclass. This demonstrates that the suggested approach works well for large-scale IoT-WSNs, both in terms of performance and security, when utilizing heterogeneous wireless senor networks for the providing of secure services.

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Section: Related Workmentioning

confidence: 99%

Blockchain-Based Secure Localization against Malicious Nodes in IoT-Based Wireless Sensor Networks Using Federated Learning

Gebremariam

Panda

Indu

2023

Wireless Communications and Mobile Computing

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“…Numerous studies have explored the application of ML models in blockchain-based environments due to its potential for addressing security and privacy concerns. Most studies [39], [40], [41], [42] suggest that ML models can be trusted using blockchain technology, as there will be no duplicate, missing, or noisy data in blockchain networks, which is essential for ML models to function effectively. A suitable block structure and consensus mechanism can help establish a global framework for analyzing the technology's application.…”

Section: Litreture Reviewmentioning

confidence: 99%

“…However, these studies [39], [41], [43], [44] often disregard privacy-preserving concerns for ML models in blockchain-based environments. Compared to traditional standalone ML scenarios, privacy challenges are exacerbated in distributed environments, where ML models must protect data holders' privacy and be resilient against malicious attacks (e.g., Byzantine).…”

Section: Litreture Reviewmentioning

confidence: 99%

Efficient Privacy-Preserving Framework for Federated and Blockchain Networks

Khordadpour¹

2023

Preprint

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<p>Machine learning (ML) models, at the core of Artificial intelligence (AI), are widely applied in our digital world. To build these models, huge amounts of data must be collected, and many assets must be protected under privacy law. Data privacy is a critical issue when training and testing ML models. For privacy concerns to be adequately addressed in today’s ML systems, there needs to be considered privacy gaps in ML, as trained ML models can be vulnerable to adversarial attacks. In this regard, federated learning and blockchain networks are the new paradigms that have emerged with the promise of privacy-preserving by design while utilizing ML models. The new paradigms have promising privacy-preserving potential; however, they neglect several fundamental privacy and security issues the fact that adversaries can exploit shared gradients and global parameters, and the parameter server may drop gradients that have been mistakenly or deliberately updated. Also, enough data is available to train models. The proposed research addresses privacy concerns in federated and distributed environments. This is a general overview to establish a privacy-preserving framework that can provide privacy in federated and blockchain-based networks while address to fundamental privacy and security issues.</p>

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“…From the point of view of network devices, they are also not exempt from security threats. Thus, [43] has developed a blockchain-empowered ensemble anomaly detection (BCEAD) solution for threat detection on wireless sensor networks (WSN). This experiment was conducted using the isolation forest (IF) model using the KDD CUP'99 dataset to ensure its effectiveness.…”

Section: Literature Reviewmentioning

confidence: 99%

An interpretable ensemble model framework for real-time anomaly detection and prediction of Ethereum blockchain transactions

2023

IJATEE

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Blockchain is a growing technology nowadays. This technology is often associated with the terms "distributed" and "decentralized" technology that does not require a central authority (CA) to control certain processes [1]. Therefore, the acceptance of this technology is increasing with the introduction of some of the latest applications in the world of decentralized financial (DeFi), non-fungible tokens (NFT), and metaverse. Previously, blockchain technology was widely adopted in several popular domains, such as digital government [2], health [3], business management [4], and smart cities [5]. The understanding of blockchain is linked to several main functions: public key, private key, hash value, and peer-to-peer (P2P) network. *Author for correspondenceIn principle, blockchain operates based on generating different keys (public, private key) for the encryption and decryption process through cryptographic mechanisms. On the other hand, this key differs from the key method generated through the symmetric cryptographic method, which is guided by using the same key in the encryption and decryption process [6].In contrast, the hash value is generated from a hash generator consisting of several digit numbers with a fixed length. Therefore, the hash value is a unique id for blockchain application development. Technically, the same data input produces the same hash value. However, even a slight change in the data input will produce a different hash value [7]. The foundation of blockchain architecture is based on the concept of a P2P network [8]. Basically, this architecture allows sharing elements such as files, tasks, or work to be executed between several peers. Peers refer to

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BCEAD: A Blockchain-Empowered Ensemble Anomaly Detection for Wireless Sensor Network via Isolation Forest

Cited by 15 publications

References 25 publications

Blockchain-Based Secure Localization against Malicious Nodes in IoT-Based Wireless Sensor Networks Using Federated Learning

Blockchain-Based Secure Localization against Malicious Nodes in IoT-Based Wireless Sensor Networks Using Federated Learning

Efficient Privacy-Preserving Framework for Federated and Blockchain Networks

An interpretable ensemble model framework for real-time anomaly detection and prediction of Ethereum blockchain transactions

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