The water and carbon footprint of cryptocurrencies and conventional currencies

Siddik, Md Abu Bakar; Amaya, Maria; Marston, Landon

doi:10.1016/j.jclepro.2023.137268

Cited by 12 publications

(1 citation statement)

References 28 publications

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“…This results in increasingly negative impacts caused by cryptocurrencies. The high-performance computing resources required by cryptomining lead to large amounts of power consumption and environmental pollution [1][2][3]. Furthermore, many attackers hijack high-performance computers in networks to help them make illegal profits; malicious cryptomining has become one of the most common forms of cyberattacks [4].…”

Section: Introductionmentioning

confidence: 99%

Identification technique of cryptomining behavior based on traffic features

Dong,

Li,

et al. 2023

Front. Phys.

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Recently, the growth of blockchain technology and the economic benefits of cryptocurrencies have led to a proliferation of malicious cryptomining activities on the internet, resulting in significant losses for companies and institutions. Therefore, accurately detecting and identifying these behaviors has become essential. To address low accuracy in detecting and identifying cryptomining behaviors in encrypted traffic, a technique for identifying cryptomining behavior traffic is proposed. This technique is based on the time series characteristics of network traffic and introduces the feature of long-range dependence, and the recognition effect is not easily affected by the encryption algorithm. First, 48-dimensional features are extracted from the network traffic using statistical methods and the rescaled range method, of which 47 dimensions are statistical features and 1 dimension is a long-range dependence feature. Second, because there is much less cryptomining traffic information than normal network traffic information in the dataset, the dataset is processed using oversampling to make the two types of traffic data balanced. Finally, a random forest model is used to identify the type of traffic based on its features. Experiments demonstrate that this approach achieves good detection performance and provides an effective solution for identifying encrypted network traffic with malicious cryptomining behavior. The long-range dependence features introduced therein together with the statistical features describe a more comprehensive flow characteristics, and the preprocessing of the dataset improves the performance of the identification model.

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Section: Introductionmentioning

confidence: 99%

Identification technique of cryptomining behavior based on traffic features

Dong,

Li,

et al. 2023

Front. Phys.

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Carbon footprint of global Bitcoin mining: emissions beyond borders

Onat,

Jabbar,

Kucukvar

et al. 2024

Sustain Sci

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Expert and operator perspectives on barriers to energy efficiency in data centers

Newkirk,

Hanus,

Payne

2024

Energy Efficiency

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It was last estimated in 2016 that data centers (DCs) comprise approximately 2% of total US electricity consumption. However, this estimate is currently being updated to account for the massive increase in computing needs due to streaming, cryptocurrency, and artificial intelligence (AI). To prevent energy consumption that tracks with increasing computing needs, it is imperative we identify energy efficiency strategies and investments beyond the low-hanging fruit solutions. In a two-phased research approach, we ask: What non-technical barriers still impede energy efficiency (EE) practices and investments in the data center sector, and what can be done to overcome these barriers? In particular, we are focused on social and organizational barriers to EE. In Phase I, we performed a literature review and found that technical solutions are abundant in the literature, but fail to address the top-down cultural shifts that need to take place in order to adapt new energy efficiency strategies. In Phase II, reported here, we interviewed 16 data center operators/experts to ground-truth our literature findings. Our interview protocols focus on three aspects of DC decision-making: procurement practices, metrics and monitoring, and perceived barriers to energy efficiency. We find that vendors are the key drivers of procurement decisions, advanced efficiency metrics are facility-specific, and there is convergence in the design of advanced facilities due to the heat density of parallelized infrastructure. Our ultimate goals for our research are to design DC decarbonization policies that target organizational structure, empower individual staff, and foster a supportive external market.

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The water and carbon footprint of cryptocurrencies and conventional currencies

Cited by 12 publications

References 28 publications

Identification technique of cryptomining behavior based on traffic features

Identification technique of cryptomining behavior based on traffic features

Carbon footprint of global Bitcoin mining: emissions beyond borders

Expert and operator perspectives on barriers to energy efficiency in data centers

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