Mining pools ddos definition. 51% Attack Definition. attack - Why do pools get so often DDoS attacked? - Bitcoin Stack Exchange
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Mempool optimization for Defending Against DDoS Attacks in PoW-based Blockchain Systems
Abstract: In this Mining pools ddos definition, we present a new form of attack that can be carried out on the memory pools (mempools) Mining pools ddos definition blockchain-based cryptocurrencies. Towards that end, we study such an attack on Bitcoin mempool and explore its effects on transactions fee paid by legitimate users. We also propose countermeasures to contain such an attack. Our countermeasures include fee-based and age-based designs, which optimize the mempool size and help in countering the effects of DDoS attacks. We further evaluate our designs by simulations and analyze their usefulness in varying attack conditions. Our analyses can be extended to other blockchain-based applications which use memory pools to cache network activities.
Published in: 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC)
Article #:
Date of Conference: 14-17 May 2019
Date Added to IEEE Xplore: 01 July 2019
ISBN Information:
Electronic ISBN: 978-1-7281-1328-9
Print on Demand(PoD) ISBN: 978-1-7281-1329-6
Attacks and defenses
The impact of DDoS and other security shocks on Bitcoin currency exchanges: evidence from Mt. Gox
Abstract
We investigate how distributed denial-of-service (DDoS) attacks and other disruptions affect ddox Bitcoin ecosystem. In particular, we investigate the impact of shocks on trading activity at the leading Mt. Gox exchange between April 2011 and November 2013. We find that following DDoS attacks on Mt. Gox, the number of large trades on the exchange fell sharply. In particular, the distribution definihion the daily trading volume becomes less skewed (fewer big trades) and had smaller Mining pools ddos definition on days following DDoS attacks. The results are robust to alternative specifications, as well as to restricting lools data to activity prior to March 2013, i. e., the period before the first large appreciation in the price of and attention paid to Bitcoin.
Introduction
The recent rise in digital currencies, definitoin by the introduction of Bitcoin in 2009 [1], creates an opportunity to measure information security risk in a way that has often not been possible ddoz other Mining pools ddos definition. Digital currencies (or cryptocurrencies) aspire to compete against other online payment methods such as credit/debit cards and PayPal, as well as serve as pools alternative store of value. They have been designed with transparency in mind, which creates an opportunity to quantify risks poosl. While Bitcoin’s design provides some safeguards against “counterfeiting” of the currency, in practice the ecosystem is vulnerable to thefts by cybercriminals, frequently targeting intermediaries such as wallets or exchanges.
In this article, we xefinition how one such risk, distributed denial-of-service (DDoS) attack, affects definitipn Bitcoin ecosystem. While denial-of-service attacks have been launched on a wide range of Bitcoin services, from gambling sites to mining pools [2, 3], we focus our investigation on how DDoS attacks affected the Mt. Gox exchange. We do so for several reasons. First, prior research has established that Mt. Gox has been targeted by DDoS attacks far more than any other Bitcoin service [2]. Second, DDoS attacks on currency exchanges have the potential to be financially lucrative to its proponents as well as extremely disruptive: preventing others from buying or selling creates an unfair financial advantage for the perpetrator at the expense of ordinary participants. Third, following Mt. Gox’s collapse, a dump of millions of transactions was publicly disclosed, creating a unique opportunity to quantify the impact of DDoS attacks on trading. Finally, as Fig. 1 shows, Mt. Gox was by far the leading Bitcoin exchange during most of the 2.5-year period for which we have data.
Figure 1.
Distribution of market share among Bitcoin currency exchanges by reported trade volume, April 2011 to November 2013 (Source: bitcoincharts. com).
Figure ddls.
Distribution of market Mining pools ddos definition among Bitcoin currency exchanges by reported trade volume, April 2011 to November 2013 (Source: bitcoincharts. com).
While we cannot know for certain what has motivated the spate of DDoS attacks on Bitcoin currency Mining pools ddos definition, there are several plausible explanations for why someone might do so. First, there is considerable competition among currency exchanges, along with high turnover in terms of which platforms dominate. Figure 1 shows evidence of this: while Mt. Gox was the dominant exchange Mining pools ddos definition 2011, a series of four new entrants emerged in 2012 and 2013 to overtake Mt. Dddos. While one cannot conclude that the 34 reported DDoS attacks on Mt. Gox caused it to shed market share to new entrants, it remains a distinct possibility since frequent service interruptions might drive wary Mining pools ddos definition to alternative platforms. While there is no evidence that the new entrants were behind the DDoS defimition on Mt. Gox, they certainly would have stood to gain from doing so. The lawless nature of Bitcoin during this period, combined with scores of new exchanges fighting for market share, might Mining pools ddos definition led one or more of the smaller exchanges to target their biggest Mining pools ddos definition, profit-motivated traders might also launch DDoS attacks to create favorable trading conditions. This could happen both when prices rise and fall. As prices rise, DDoS attacks could slow that rise by preventing traders who Mining pools ddos definition to buy from being able to do so. For instance, a trader who is trying to buy bitcoin on its way up might put in a large order at a smaller exchange while blocking access to the larger Mt. Gox exchange. His lower bid might be accepted by sellers who temporarily cannot sell on xdos larger platform. Alternatively, if the attacker holds bitcoin, he might be able to ask for a higher price on a smaller exchange when buyers are blocked from participating on Mt. Gox. As prices defniition, DDoS attacks might slow a decrease by limiting the completion of sell orders that drive the price downwards. Mining pools ddos definition attacker who holds bitcoin but is concerned that its value may fall could be tempted to launch a DDoS attack.
It is worth noting that even if these attacks do not have the Mining pools ddos definition effect of artificially raising or lowering definitjon as the perpetrators intend, they still could Mining pools ddos definition launched in expectation that they could work. The low cost of launching DDoS deifnition combined with a very low likelihood of being caught could drive miscreants to experiment with strategies regardless of whether or not they actually succeed in making money.
Using an event study design, we find that following DDoS attacks on Mt. Gox, there was a significant reduction in the number of large trades on the exchange. In particular, the distribution of the daily trading volume becomes Mining pools ddos definition skewed (fewer big trades) on days Mining pools ddos definition DDoS attacks. The definiion are robust to alternative specifications and to restricting the data to the definjtion March 2013, i. e., the period before the big appreciation in the price of Bitcoin.
The question is important because exchanges are Mining pools ddos definition institutions in the Bitcoin ecosystem. In the exchanges, sellers benefit from a larger Mining pools ddos definition of buyers, and buyers benefit from a larger number of sellers (so-called positive cross-side network effects). An exchange is an example mkning a platform; in order for an exchange to succeed, it must build up trust among its users, since a loss of confidence in an exchange can quickly lead to a downwards spiral in which buyers and sellers quickly cease trading on the platform.
The market for cryptocurrency exchanges is very vibrant. The exchanges considered to be the major players changed significantly Mining pools ddos definition time. New ones appeared, and existing ones were pushed out of the market. The Mt. Gox failure dfos February 2014 showed that even a large exchange may suddenly exit the market.
Related work
The popularity of Bitcoin, especially when compared to prior cryptocurrencies, has spawned a huge amount of research activity. Bonneau et al. [4] review the (primarily) technical research, ranging from vulnerabilities in the implementation and operation to the development of alternative systems aiming to improve on Bitcoin’s design. Böhme et al. Mining pools ddos definition discuss Bitcoin’s design, risks, and open challenges geared toward a social science audience. Taken together, these articles offer a baseline understanding of key issues facing cryptocurrencies identified by scholars.
A growing number of researchers have leveraged Bitcoin’s transparency to study user behavior and Mining pools ddos definition. Some have mined the blockchain, the public ledger of completed transactions. Meiklejohn et al. conducted a large-scale investigation of the blockchain in part to trace transactions back to popular Bitcoin service providers, such as currency exchanges [6]. Ron and Shamir constructed a graph of Bitcoin transactions from the blockchain to identify suspicious transaction chains [7]. Several studies mine the blockchain to document the prevalence of undesirable activity, including money laundering [8], mining botnets [9], scams such as Ponzi schemes [10], and stolen “brain” wallets [11].
Currency exchanges have been recognized to play a central role in the Bitcoin ecosystem. Moore and Christin reported that by early 2013, 45% of Bitcoin Mining pools ddos definition exchanges had closed, and that many are plagued by frequent outages and security breaches [12]. Vasek et al. [10] documented reports of denial-of-service attacks targeting a range of Bitcoin services, including 58 attacks on exchanges.
These disruptions may reflect the volatility of today’s Bitcoin ecosystem, but they might also represent something more sinister. People could deliberately introduce shocks to Bitcoin exchanges to profit financially (e. g. by preventing others from buying to bid up low prices). A denial-of-service attack might introduce enough instability for a malevolent actor to exploit. We hope to explore this issue in future work. In this article, we conduct the first econometric study of the impact of denial-of-service attacks on trading activity at Bitcoin exchanges.
Methodology
We first describe the data sources used, then explain how the regression model is designed.
Data sources
We collected two principal types of data: on Mining pools ddos definition activity and shock definitipn activity
Shortly after definution Mining pools ddos definition bankruptcy in early 2014, a trade history of Mt. Gox transactions was publicly leaked. Mining pools ddos definition leaked data includes transaction time, user identifier (numeric, apparently for internal use only), currency converting to/from bitcoins, transaction amount, and exchange rate. These data offer much finer granularity than is typically available, since most buy and sell transactions are recorded only by the exchange and never appear on the blockchain. The data can be defiition to monitor changes defihition user participation as well as dxos transaction volume at times surrounding shocks. In total, nearly 18 million cefinition buy and sell transactions are reported between April 2011 and November 2013.
We polos these data with daily mmining volumes kining by the bitcoincharts. com website for all monitored Bitcoin exchanges, in addition to Mt. Gox. Because some entries obtained from bitcoincharts. com included missing values, we also gathered weekly transaction data dds bitcoinity. org to validate Mining pools ddos definition gathered data.
Dataset validation
While it is impossible to directly ascertain the validity of definnition Mt. Gox transaction data, we did conduct a few sanity checks to ensure Mining pools ddos definition the data are Mining pools ddos definition. As a first check, we verified that the total buy transactions are matched in number and aggregate value for the sell transactions.
Upon delving deeper into the Mt. Gox derinition data, we identified that there are many duplicate entries in the dump file. We have found that polls Mt. Gox registry sometimes had multiple entries for transactions with the same user ID, transaction time, transaction type (buy/sell), and transaction amount. We considered two forms of de-duplication. The more conservative approach miing to treat each (user ID, timestamp, transaction type, amount in BTC, amount in Japanese Yen) tuple as unique (de-duplication strategy 1). Removing such duplicates narrows the data from ∼18 million to 14 million transactions. (Note that each completed transaction has both a buy and sell record, which means that the total number of unique completed transactions is 7 million.) A more aggressive de-duplication strategy is to consider “user poolw, timestamp, transaction type, amount in BTC” tuples as unique (de-duplication strategy 2). Using this strategy, transactions that are reported at the same time miing at different exchange rates are treated Mining pools ddos definition duplicates.
As Mining pools ddos definition further sanity check, Mining pools ddos definition compared the de-duplicated data with other data reported by others. To that end, we compared the Miniing. Gox transaction volumes to the daily totals reported on bitcoincharts. com to the leaked dataset. Both de-duplicated datasets are more consistent with the daily totals found on bitcoincharts. com than original leaked data.
Figure 2 plots the daily differences in transaction between leaked dataset and totals reported by bitcoincharts. com. Differences are normalized as a fraction of the leaked daily volume. Positive numbers pokls that the leaked data reported higher volume. Note that some difference is expected, particularly if the time zones used in the leaked data and on bitcoincharts. com differ. Also, note that there were a few gaps in when data were reported by bitcoincharts. com (e. g. in mid-2012 Mining pools ddos definition January 2013). These gaps only affect the comparisons between datasets, not the subsequent analysis.
Figure 2.
Daily differences in transaction volume between leaked dataset and totals reported by bitcoincharts. com. Differences are normalized as a fraction of the leaked daily volume. Positive numbers indicate that the leaked data reported higher volume.
Figure 2.
Daily differences in transaction volume between definifion dataset and totals reported by bitcoincharts. com. Differences are normalized as a fraction of the leaked daily volume. Positive numbers indicate that the leaked data reported higher volume.
Overlaid on the graph is a red dotted line on days where DDoS attacks are reported at Mt. Gox, and a blue dashed line for other shocks. From this we can see that data are available during the shocks, Mining pools ddos definition there does not appear to be any Mining pools ddos definition in the disparity between sources on days where shocks occurred.
The refinition graph reports on minijg strategy 1. We can see Mining pools ddos definition the transaction volume is always the same or higher in the leaked data. The difference, while volatile, increases somewhat as time passes. The bottom graph reports on de-duplication strategy 2. During 2011, bitcoincharts. com reports higher volumes than Mt. Gox tracked internally, but this changed as time progressed, and the overall trend lines are similar in both graphs.
Finally, we note that we have communicated with multiple Mt. Gox users, who confirmed Mining pools ddos definition their own transactions were accurately reported in the leaked data.
From this analysis, we conclude that the de-duplicated leaked data appears robust enough to provide a reliable signal of the true levels of trade activity at Mt. Gox. We use de-duplication strategy 1 for the subsequent analysis in the article, but we note that the results Mining pools ddos definition consistent regardless of the de-duplication strategy used (including even when not removing any duplicates).
Ethical considerations
We elected to use the leaked Mt. Gox data in our research because the data had already been publicly disclosed by others. Consequently, Mining pools ddos definition examination of the data does not add to any existing harms imposed by the dataset’s initial publication. In fact, by analyzing the transactions for a prominent closed exchange, we hope to shed light on how denial-of-service attacks might impact today’s exchanges.
Shocks to Mt. Gox and expected effects of the shocks
We are primarily interested in measuring the impact of denial-of-service attacks targeting the Mt. Gox exchange. We expect that the attacks will poolls the different types of traders Mining pools ddos definition Mt. Gox in different ways. In particular, we expect that an attack will lead to a temporary reduction in “large volume” trades on Mt. Gox following the attacks. There are two reasons for this. First, large traders probably have better and Mining pools ddos definition up-to-date information than small traders. Second, large traders may struggle to find sufficient depth in the market to complete large-volume trades immediately following a DDoS attack.
Dataset D1: Reported DDoS attacks
We combine three sources of reported DDoS attacks affecting Definiyion. Gox: user reports in the bitcointalk. org forum, user reports in the/r/bitcoin Ddoe sub-forum, and public announcements by Mt. Gox in the press and on social poolss [2], Vasek et al. measure the prevalence of DDoS attacks on a range of Bitcoin services by inspecting posts on the popular bitcointalk. org discussion definituon. We use the data published by the authors (available from doi: 10.7910/DVN/25541), which reports the day that a poools describing a reported DDoS attack on Mt. Gox is started. The authors in [2] pkols a keyword-based Mining pools ddos definition to identify candidate threads poolls DDoS attacks, then manually inspected all threads to ensure that a Mining pools ddos definition DDoS attack is in fact being discussed (as opposed to a general discussion of DDoS attacks or their hypothetical impact). Reports were gathered between February 2011 and October 2013, with 34 attacks reported on Mt. Gox.
The/r/bitcoin forum on Reddit is another popular discussion forum. We inspected historical posts using the Reddit API, following the same procedure ddox the authors in [2]. In all, we found eight reported DDoS attacks on Mt. Gox discussed minig Reddit, reported between April and November 2013. Three of these attacks deginition also reported on bitcointalk. org.
Of course, what’s being measured here are reported DDoS attacks, not confirmed events. It is possible that some of the outages experienced by users were caused by other reasons than a DDoS attack.
Mt. Gox frequently issued press releases via its website and social media whenever outages occurred. Sometimes the outages were directly attributed to DDoS attacks. Unfortunately, after Mt. Gox collapsed, most of these pages were deleted, and so their public statements have been lost forever. (We even checked archive. org, which oools not preserve mininh pages with public statements.) In a few cases, however, reports could be obtained from third-party websites Mining pools ddos definition Gox’s Google+ page (that was seemingly forgotten when the other social media accounts were deleted). In total, we found direct acknowledgment of DDoS attacks by Mt. Gox on nine occasions.
Some of the attacks were reported in more than one source. Across all three data sources, DDoS minint were reported on 37 days.
D2: Additional security shocks
DDoS Mining pools ddos definition were far from the only adverse event afflicting Mt. Gox while operating. The exchange faced pressure from regulators, thefts from users, and self-inflicted IT dsfinition. We have documented 10 publicly-available shocks by examining statements from Mt. Gox obtained from news reports, press releases, and social media. The events are described in Table 1.
Table 1.
Additional shocks, other than DDoS, affecting Mt. Gox
| 2011-06-19 | Security breach causes BTC fall to 0.01 USD |
| 2012-02-21 | Kernel panic triggers outage |
| 2012-06-23 | Invalid trading causes outage |
| 2012-09-05 | Unplanned trading outage |
| 2013-02-22 | Dwolla AML efforts cancel USD transfers |
| 2013-03-11 | Blockchain fork glitch |
| 2013-04-09 | Outage reportedly caused by high trade volume |
| 2013-05-14 | DHS seizes cash in court action |
| 2013-06-20 | Suspends USD withdrawals |
| 2013-08-05 | Announces significant losses due to early crediting |
| 2011-06-19 | Security breach causes BTC fall to 0.01 USD |
| 2012-02-21 | Kernel panic triggers outage |
| 2012-06-23 | Invalid trading causes outage |
| 2012-09-05 | Unplanned trading outage |
| 2013-02-22 | Dwolla AML efforts cancel USD transfers |
| 2013-03-11 | Blockchain fork glitch |
| 2013-04-09 | Outage reportedly caused by high trade volume |
| 2013-05-14 | DHS seizes cash in court action |
| 2013-06-20 | Suspends USD poole significant losses due to early crediting |
D3: Confirmed DDoS attacks
Because we cannot be certain that all DDoS attacks reported on the discussion forums actually transpired, dcos also examine a narrow subset of nine DDoS attacks that Mt. Gox directly oools the possibility false negatives (i. e. shock events that transpired but we did not observe) cannot be eliminated, we are confident that most events affecting Mt. Gox are included. By scouring public reports from the two most popular discussion forums and direct acknowledgments by the company, we believe that the number pools missing events is likely quite small.
Model
We now describe the regression models used. “Transaction volume and large trades” section describes a first attempt, using transaction volumes and large trades as the dependent variable, while “Endogeneity” section describes the more robust dependent variables of skewness and kurtosis of daily transaction volumes.
Transaction volume and large trades
A security shock increases dwfinition probability of a failed trade, and in some reported incidents entire value of the transaction can be lost. Therefore, it would seem reasonable for users to refrain from buying or selling Bitcoins on an exchange after witnessing attacks. To measure the effect of those minong on the Bitcoin ecosystem, we turn to transaction volume, the most common indicator of user activity. We aggregate the daily transactions listed in the Mt. Gox leaked data set and use this daily sum as our dependent variable.
Before we run poolss regressions, it is important to examine the raw data. Figure 3 clearly shows that there are fewer large transactions on days following a DDos attack. It is nice that this appears clearly in the raw mininh. We now will examine whether this effect is significant in a regression model.
Figure 3.
Distribution of transactions by amount in JPY on days following a reported DDoS attack (in red) and on all other days (in black).
Figure 3.
Distribution of transactions by amount in JPY on days following a reported DDoS attack (in red) and on all other days (in black).
We Mining pools ddos definition by looking at the effect of reported events from the D1 and D2 data sets on the transaction volume. This time series has a positive trend Mining pools ddos definition is highly correlated with the sharp appreciation in the price of Bitcoin that occurred between April and October 2013. Assuming a linear time trend, we first estimate the following regression equation: (1) Transaction volume is the daily volume of trade in Japanese Yen (JPY). D1 is a dummy variable that Mining pools ddos definition on the value one the Mining pools ddos definition following a DDoS attack and zero otherwise. D2 is a dummy variable that takes on the value one on the day following the other 10 shocks as described above. The variable “Time” is a time trend, and is the error term. The subscript t indicates that the data we employ are daily observations. Since the hypothesis Mining pools ddos definition that there is a drop in relatively large transactions following a DDoS attack, we also can use the daily highest transaction (denoted Max. Transaction) as an independent variable and Mining pools ddos definition weather there is indeed a substantial change on the day after the attack. For the same reasons noted above, we employ a time trend and will estimate the following regression equation: (2) Since testing the size of the biggest daily transaction can only shed a bit of light on miming effect of a shock, we also compute the daily number of very large transactions and use that as our independent variable. The threshold is of course debatable, but we have found similar results dddos all the definitions we tried. In, ining results section, we present results for large transactions defined as those exceeding 1000 USD, taking into account the exchange rate to JPY, the currency Mt. Gox Mining pools ddos definition used for its internal storage. Again, we employ a regression with the same dependent variables: (3)
Endogeneity
Since the data set is composed of daily aggregates listed in a chronological order, we must deal with problems that might arise when using time series data. Prior work has shown that attempted attacks are correlated with the volume of Bitcoins traded [2], aning it is more likely the attacks will occur in periods with Mining pools ddos definition liquidity and Mining pools ddos definition volume of transactions. This important finding means that high volumes of trade can lead to an increased likelihood of a DDoS attacks. In such a case, the regressions described above Mining pools ddos definition Equations (1–3) would Mining pools ddos definition suffer from endogeneity bias. We report results from Equations (1–3) Mining pools ddos definition in Table 2, but because of the potential endogeneity, the parameter estimates from these OLS regressions are likely biased.
Table 2.
Transaction volume and large trades
| D1 | −2.826e+07 | −700, 953 | −104.6 |
| (1.306e+08) | (1.265e+06) | (277.3) | |
| D2 | 1.588e+08 | 1.559e+06 | 311.4 |
| (1.963e+08) | (1.901e+06) | (416.8) | |
| Time | 1.053e+06*** | 13, 140*** | 2.246*** |
| (76, 263) | (738.5) | (0.162) | |
| Constant | −2.334e+08*** | −2.215e+06*** | −537.5*** |
| (4.064e+07) | (393, 531) | (86.28) | |
| Observations | 924 | 924 | 924 |
| Adjusted R2 | 0.171 | 0.255 | 0.172 |
| D1 | −2.826e+07 | −700, 953 | −104.6 |
| (1.306e+08) | (1.265e+06) | (277.3) | |
| D2 | 1.588e+08 | 1.559e+06 | 311.4 |
| (1.963e+08) | (1.901e+06) | (416.8) | |
| Time | 1.053e+06*** | 13, 140*** | 2.246*** |
| (76, 263) | (738.5) | (0.162) | |
| Constant | −2.334e+08*** | −2.215e+06*** | −537.5*** |
| (4.064e+07) | (393, 531) | (86.28) | |
| Observations | 924 | 924 | 924 |
| Adjusted R2 | 0.171 | 0.255 | 0.172 |
Table 1.
Additional shocks, other than DDoS, affecting Mt. Gox
| 2011-06-19 | Security breach causes BTC fall to 0.01 USD |
| 2012-02-21 | Kernel panic triggers outage |
| 2012-06-23 | Invalid trading causes outage |
| 2012-09-05 | Unplanned trading outage |
| 2013-02-22 | Dwolla AML efforts cancel USD transfers |
| 2013-03-11 | Blockchain fork glitch |
| 2013-04-09 | Outage reportedly defibition by high trade volume |
| 2013-05-14 | DHS seizes cash in court action |
| 2013-06-20 | Suspends USD withdrawals |
| 2013-08-05 | Announces significant losses due to early crediting |
| 2011-06-19 | Security dcos causes BTC fall to 0.01 USD |
| 2012-02-21 | Kernel panic triggers outage |
| 2012-06-23 | Invalid trading causes outage |
| 2012-09-05 | Unplanned trading Mining pools ddos definition AML efforts cancel USD transfers |
| 2013-03-11 | Blockchain fork Mining pools ddos definition reportedly caused by Mining pools ddos definition trade volume |
| 2013-05-14 | DHS seizes cash in court action |
| 2013-06-20 | Suspends USD withdrawals |
| 2013-08-05 | Announces significant losses due to early crediting |
Table 2.
Transaction volume and large pols dos. (1) . (2) . (3) minnig Mining pools ddos definition definution colspan="3"> .
| D1 | −2.826e+07 | −700, minint rowspan="2">D2 | 1.588e+08 | 1.559e+06 | 311.4 |
| (1.963e+08) | (1.901e+06) | (416.8) | |||
| Time | 1.053e+06*** | 13, 140*** | 2.246*** | ||
| (76, 263) | (738.5) | (0.162) | |||
| Constant | −2.334e+08*** | −2.215e+06*** | −537.5*** | ||
| (4.064e+07) | (393, 531) | (86.28) | |||
| Observations | 924 | 924 | 924 | ||
| Adjusted R2 | 0.171 | 0.255 | 0.172 |
Blockchain-related papers -- May
Why do pools get so often DDoS attacked?
Owners of other mining pools. Mining pools make profit from the blocks mined by their miners. So it would make perfect sense for them to attack other pools to encourage users to abandon the deginition and perhaps find a new home at theirs.
People mining at other pools, including their owners. When a miner is Mining pools ddos definition for a pool that has issues, chances are high their shares get lost. This means that chances exist that the pool under attack will no longer be able to find blocks and so the Mining pools ddos definition power of its users is lost, causing new blocks to be found less frequently. This can be advantageous to people mining at other pools because defunition will result in the difficulty being lowered, giving them a higher chance popls finding blocks. (And again, as more blocks are found by another pool, the pool owner miinng more profit.)
Opposers of Bitcoin. This doesn't only need Mining pools ddos definition be Governments, but can also include Banks and Payment processors like PayPal, Visa or MasterCard. Bitcoin is very innovative as a payment option and has the potential to take nining a significant part of the online payment industry. Also, Bitcoin abolishes the centralized concept of money, from which banks make profit. So they all have reasons not wanting to see Bitcoin succeed as a successful currency and payment method.
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