AI-Enabled Blockchain: An Outlier-Aware Consensus Protocol for Blockchain-Based IoT Networks

Salimitari, Mehrdad; Joneidi, Mohsen; Chatterjee, Mainak

doi:10.1109/globecom38437.2019.9013824

Cited by 47 publications

(23 citation statements)

References 19 publications

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“…AI-enabled blockchain is a state-of-the-art topic suggested by several researchers. Machine learning algorithms, deep learning, and reinforcement learning appear very apt to address the deficiencies of the discussed consensus methods [81]. Gupta et al postulate the possibility of leveraging these techniques to reach an easier and faster consensus [82].…”

Section: Practical Blockchain-based Iot Networkmentioning

confidence: 99%

A survey on consensus methods in blockchain for resource-constrained IoT networks

2020

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The success of blockchain as the underlying technology for cryptocurrencies has opened up possibilities for its use in other application domains as well. The main advantages of blockchain for its potential use in other areas are its inherent security mechanisms and immunity to data manipulation attacks. A blockchain relies on a consensus method for agreeing on any new data. Most of the consensus methods which are currently used for the blockchain of different cryptocurrencies require high computational power and thus are not suitable for resource-constrained systems.In this article, we survey the various blockchain-based consensus methods that are applicable to resource-constrained IoT devices and networks. In a typical IoT network, there exist several devices with limited computational and communication capabilities. Most often, these devices cannot perform intensive computations and are starved for bandwidth. Therefore, we discuss the possible measures that can be taken to reduce the computational power and convergence time for the underlying consensus methods. We also talk about some of the alternatives to the public blockchain, such as private blockchain and tangle, along with their potential adoption for IoT networks. Furthermore, we review the existing consensus methods that have been implemented and explore the possibility of utilizing them to realize a blockchainbased IoT network. Some of the open research challenges including AI-enabled blockchains are also put forward.

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Section: Practical Blockchain-based Iot Networkmentioning

confidence: 99%

A survey on consensus methods in blockchain for resource-constrained IoT networks

2020

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“…Employing the entire closed-set data during the training procedure leads to inclusion of untrustworthy samples of the closed-set. Regularized or underfitting models (such as low-rank representations [70,71,72]) still suffer from memorizing effect of such samples, which exacerbate the separation between open and closed-set by adding ambiguity to the decision boundary between the closed and openset classes. To resolve this issue, we utilize our proposed selection method, KSP, which selects the core representatives.…”

Section: Open-set Identificationmentioning

confidence: 99%

Select to better learn: Fast and accurate deep learning using data selection from nonlinear manifolds

Joneidi¹,

Vahidian²,

Esmaeili³

et al. 2020

Preprint

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Finding a small subset of data whose linear combination spans other data points, also called column subset selection problem (CSSP), is an important open problem in computer science with many applications in computer vision and deep learning. There are some studies that solve CSSP in a polynomial time complexity w.r.t. the size of the original dataset. A simple and efficient selection algorithm with a linear complexity order, referred to as spectrum pursuit (SP), is proposed that pursuits spectral components of the dataset using available sample points. The proposed non-greedy algorithm aims to iteratively find K data samples whose span is close to that of the first K spectral components of entire data. SP has no parameter to be fine tuned and this desirable property makes it problem-independent. The simplicity of SP enables us to extend the underlying linear model to more complex models such as nonlinear manifolds and graph-based models. The nonlinear extension of SP is introduced as kernel-SP (KSP). The superiority of the proposed algorithms is demonstrated in a wide range of applications.

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“…Solution Approach Attack DU DC S. Iyer [24] storage off-chain S. Sayadi [25] storage on-chain Y. Mirsky [26] storage off-chain M. Li [27] storage off-chain O. Alkadi [28] storage off-chain S. Morishima [29] other off-chain Z. Il-Agure [30] other off-chain M. Salimitari [31] framework off-chain X. Wang [32] framework on-chain B. Podgorelec [33] framework on-chain BAD (Our solution) framework on-chain…”

Section: Ads Challengesmentioning

confidence: 99%

BAD: A Blockchain Anomaly Detection Solution

et al. 2020

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Anomaly detection tools play a role of paramount importance in protecting networks and systems from unforeseen attacks, usually by automatically recognizing and filtering out anomalous activities. Over the years, different approaches have been designed, all focused on lowering the false positive rate. However, no proposal has addressed attacks specifically targeting blockchain-based systems. In this paper we present BAD: Blockchain Anomaly Detection. This is the first solution, to the best of our knowledge, that is tailored to detect anomalies in blockchain-based systems. BAD is a complete framework, relying on several components leveraging, at its core, blockchain meta-data in order to collect potentially malicious activities. BAD enjoys some unique features: (i) it is distributed (thus avoiding any central point of failure); (ii) it is tamper-proof (making it impossible for a malicious software to remove or to alter its own traces); (iii) it is trusted (any behavioral data is collected and verified by the majority of the network); and, (iv) it is private (avoiding any third party to collect/analyze/store sensitive information). Our proposal is described in detail and validated via both experimental results and analysis, that highlight the quality and viability of our Blockchain Anomaly Detection solution.

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AI-Enabled Blockchain: An Outlier-Aware Consensus Protocol for Blockchain-Based IoT Networks

Cited by 47 publications

References 19 publications

A survey on consensus methods in blockchain for resource-constrained IoT networks

A survey on consensus methods in blockchain for resource-constrained IoT networks

Select to better learn: Fast and accurate deep learning using data selection from nonlinear manifolds

BAD: A Blockchain Anomaly Detection Solution

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