Seeing is understanding

Bogner, Andreas

doi:10.1145/3123024.3123157

Cited by 32 publications

(5 citation statements)

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“…More related to our work is the anomaly detection research done by Bogner et al [25]. They presented an IoT solution for monitoring interdependent components.…”

Section: Machine Learning and Blockchainsmentioning

confidence: 98%

Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study

et al. 2018

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The adoption of machine learning and deep learning is on the rise in the cybersecurity domain where these AI methods help strengthen traditional system monitoring and threat detection solutions. However, adversaries too are becoming more effective in concealing malicious behavior amongst large amounts of benign behavior data. To address the increasing time-to-detection of these stealthy attacks, interconnected and federated learning systems can improve the detection of malicious behavior by joining forces and pooling together monitoring data. The major challenge that we address in this work is that in a federated learning setup, an adversary has many more opportunities to poison one of the local machine learning models with malicious training samples, thereby influencing the outcome of the federated learning and evading detection. We present a solution where contributing parties in federated learning can be held accountable and have their model updates audited. We describe a permissioned blockchain-based federated learning method where incremental updates to an anomaly detection machine learning model are chained together on the distributed ledger. By integrating federated learning with blockchain technology, our solution supports the auditing of machine learning models without the necessity to centralize the training data. Experiments with a realistic intrusion detection use case and an autoencoder for anomaly detection illustrate that the increased complexity caused by blockchain technology has a limited performance impact on the federated learning, varying between 5 and 15%, while providing full transparency over the distributed training process of the neural network. Furthermore, our blockchain-based federated learning solution can be generalized and applied to more sophisticated neural network architectures and other use cases.

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“…More related to our work is the anomaly detection research done by Bogner et al [25]. They presented an IoT solution for monitoring interdependent components.…”

Section: Machine Learning and Blockchainsmentioning

confidence: 98%

Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study

et al. 2018

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“…Therefore, a study by [31][32][33] has analysed the smart contract source code based on the ML approach to detect Ponzi schemes. Most recent studies have begun to explore artificial intelligence (AI) [34,35] and deep learning (DL) [36,37] to detect anomalies in smart contracts.…”

Section: *Author For Correspondencementioning

confidence: 99%

Anomaly detection in smart contracts based on optimal relevance hybrid features analysis in the Ethereum blockchain employing ensemble learning

2023

IJATEE

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Blockchain 2.0 has revolutionized the domain by introducing blockchain as a decentralized application (DApp) development platform, previously recognized mainly in the cryptocurrency sphere. Consequently, the rise of DApp development has inadvertently camouflaged fraudulent activities within smart contracts, leading to substantial losses for investors. Implementing machine learning (ML) approaches can significantly enhance the efficacy of anomaly detection. However, many studies still grapple with selecting the most pertinent features to optimize anomaly detection levels. This challenge intensifies when managing the high-dimensional raw data extracted directly from the Ethereum blockchain network, which falls under the category of big data. Smart contracts, the core of blockchain that governs DApp logic, have increasingly become a haven for fraud. This study focuses on analyzing three primary characteristic components based on contract source code (operation code (opcode), application binary interface (ABI) code, and contract transaction) to develop anomaly detection models in smart contracts using an ensemble hybrid feature strategy. The approach involves two key stages: firstly, reducing the initial feature size through constant, quasi-constant, and variant validation; and secondly, identifying the most relevant feature set using the searching for uncorrelated list of variables (SULOV) method, grounded in the minimum redundancy maximum relevance (MRMR) principle. The anomaly detection model employs a voting ensemble technique, harnessing a dataset of the most pertinent features. The model's effectiveness is gauged by comparing its performance with individual models, including random forest (RF), k-nearest neighbor (KNN), decision tree (DT), linear discriminant analysis (LDA), and stochastic gradient descent (SGD). The findings indicate that the proposed model achieves superior anomaly detection levels, with a determination value measurement rate of 92.99%, outperforming individual classifiers using the 44 most relevant features while minimizing classification time. The model's efficiency is further corroborated through comparative analysis with previous studies and alternative methodologies using the same contract dataset. The proposed ensemble-based model significantly improves anomaly detection in contract source code analysis, employing a minimal and relevant set of features refined through the SULOV method.

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“…Sayadi et al detect abnormal electronic transactions in the Bitcoin system [ 34 ], and they extract Bitcoin transaction data, block size, difficulty, hash rate, transaction volume, median time for transaction confirmation, and the number of unique Bitcoin transactions per day. In addition, in [ 35 ], the authors extract the number of transactions and the volume of gas consumption.…”

Section: Proposed Detection Schemementioning

confidence: 99%

Visualization with Prediction Scheme for Early DDoS Detection in Ethereum

Park,

Kim

2023

Sensors

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Blockchain technologies have gained widespread use in security-sensitive applications due to their robust data protection. However, as blockchains are increasingly integrated into critical data management systems, they have become attractive targets for attackers. Among the various attacks on blockchain systems, distributed denial of service (DDoS) attacks are one of the most significant and potentially devastating. These attacks render the systems incapable of processing transactions, causing the blockchain to come to a halt. To address the challenge of detecting DDoS attacks on blockchains, existing visualization schemes have been developed. However, these schemes often fail to provide early DDoS detection since they typically display only past and current system status. In this paper, we present a novel visualization scheme that not only portrays past and current values but also forecasts future expected system statuses. We achieve these future predictions by utilizing polynomial regression with blockchain data. Additionally, we offer an alternative DDoS detection method employing statistical analysis, specifically the coefficient of determination, to enhance accuracy. Through our experiments, we demonstrate that our proposed scheme excels at predicting future blockchain statuses and anticipating DDoS attacks with minimal error. Our work empowers system managers of blockchain-based applications to identify and mitigate DDoS attacks at an earlier stage.

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Seeing is understanding

Cited by 32 publications

References 0 publications

Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study

Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study

Anomaly detection in smart contracts based on optimal relevance hybrid features analysis in the Ethereum blockchain employing ensemble learning

Visualization with Prediction Scheme for Early DDoS Detection in Ethereum

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