We created a setting to apply deep learning to the data spanning a period of 2. As the first step, we standardised the data to improve its applicability to the learning model. An example of applicable input data is provided in Table 2. Subsequently, to use the input data for prediction, we set up a deep learning model.
Multiple hidden layers were accumulated for learning to identify deep data structures. Specifically, 1, 2, 3, and 5 hidden layers were constructed to select the layer structure that returned the best possible prediction result. The number of neurons that were allocated to each hidden layer was 1, As for the input layers, based on the input data provided in Table 2 , 15 input data points were represented as serial vectors to allocate neurons based on the cumulative number of days spent on learning, i.
Fig 3 shows the concept derived from the concept building phase and the words constituting the concept. We focused on a general phenomenal analysis of the meanings of the concept, rather than analysing all the words constituting the concept.
Because mining is a means of earning Bitcoin, many users share their opinions about its efficiency. Other than mining, Bitcoin can also be earned by transactions. Security therefore not only became a popular issue on the Bitcoin forum but also resulted in social problems, leading to the closure of the site. Although the situation was resolved when the site was closed towards the end of , words regarding related exchange markets and companies attracted considerable attention from users.
Since the emergence of Bitcoin, many types of similar cryptocurrencies have been developed and are in use. In view of the after-effects of the Granger causality test, the null hypothesis was rejected. This suggests that the time series of the gathered data failed to forecast the fluctuation in Bitcoin transaction volume and price—i. Tables 3 and 4 list the test results. In addition, the Pearson Correlation Coefficient between the rating of each concept and Bitcoin price and transaction is shown in Table 5.
The foregoing results are partially indicative of the significance of the extracted keyword data. However, this process was only used for the purpose of verification. The entire data set was used to build the actual deep learning model for prediction. We built and applied the deep learning model based on the gathered and KDE-based rating data to predict the Bitcoin transaction and price. The accuracy rate, the Matthews correlation coefficient MCC , and the F-measure were used to evaluate the performance of the proposed model.
Table 6 presents the prediction results. Table 4 presents the results relative to the layer and learning data structures. Both three or more hidden layers and cumulative learning data for 12 days or longer resulted in negligible differences. Less than two hidden layers and cumulative learning data for less than 7 days proved to be insufficient for learning and compromised the prediction accuracy. Conversely, overfitting could possibly occur with the prediction accuracy failing to significantly improve, if more than five hidden layers and cumulative data for over 12 days were used.
We analysed the user comments posted on a Bitcoin online forum to predict the fluctuation in the Bitcoin price and transaction count. Moreover, online user postings influenced Bitcoin transactions. The causality test result indicated some topics associated with Bitcoin transactions. These findings suggest China exerts a strong influence on the Bitcoin price. This finding suggests that topics related to the circulation and transaction of other types of cryptocurrencies have an impact on the Bitcoin transaction volume.
Hence, the experimental findings revealed some user comments that had the most significant relationship with and effects on the fluctuation in Bitcoin price and transactions. That said, the proposed method has a limitation in terms of its broader applicability due to the fact that the concepts were constructed for a long period of time.
Thus, appropriate subdivision of the sample period would help to obtain a more accurate understanding of the users for topic modelling and to refine the analysis with additional approaches including sentiment analysis. Moreover, the present findings warrant further studies on the analysis of user comments relative to the characteristics of Bitcoin forums. To increase the accuracy of prediction, it is necessary to address a few challenges. The present work is focused on analysing online forum user comments and adds some formal or structured data to predict the fluctuation in the Bitcoin price and transactions.
However, it may add to the reliability of the findings if the search results and relevant content on search engines were quantitatively analysed or if the social network data were analysed as they did in some comparable previous studies[ 21 , 40 ]. Furthermore, it may be an efficient preliminary study to analyse and classify online forum users per se[ 41 — 45 ]. In addition, the postings may be worth filtering more meticulously [ 46 — 50 ] to more accurately corroborate the findings.
Information derived from online forum users seems to be well-suited for extensive research on cryptocurrencies as well as Bitcoin. In the same vein, keywords manifested in online forum user comments could be used for further in-depth analysis and understanding of cryptocurrency transactions. Moreover, online forums are great sources of abundant informal and formal information, which serves to appreciate cryptocurrencies from diverse perspectives including money laundering, which is closely associated with cryptocurrencies [ 51 — 54 ].
With the increasing circulation of Bitcoin, its acceptability has drawn much attention in many ways [ 2 , 3 , 5 , 14 ]. The present study is noteworthy in that it analysed the topics often mentioned by Bitcoin users and linked their meanings to Bitcoin transactions. The proposed method for predicting the fluctuation in the Bitcoin price and transactions based on user opinions on online forums is conducive to understanding a range of cryptocurrencies other than Bitcoin and increasing their usability, although it needs to be reinforced.
In addition, the present approach to the salience of user comments on online forums is likely to yield more significant results in many other fields. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract Bitcoin is an online currency that is used worldwide to make online payments. Introduction The advancement of the ubiquitous Internet has resulted in the emergence of unprecedented types of currencies that are distinct from the established currency system.
Related work Research on cryptocurrencies, particularly on Bitcoin, has been extensively conducted from diverse perspectives, e. Methods System overview This section provides an overview of the proposed method. Download: PPT. Data crawling Data crawling was the first step in our analysis.
Analysis of user comment data Our intention was to extract significant keywords used in Bitcoin transactions from the aforementioned crawled data. Concept building. Topic modelling for initial lexicon building. Expanding the lexicon via word recommendation. Computation of document relevance to concept. Prediction modelling Granger causality test. Deep learning model. Results Concept building results Fig 3 shows the concept derived from the concept building phase and the words constituting the concept.
Results of Granger causality test and correlation test In view of the after-effects of the Granger causality test, the null hypothesis was rejected. Table 3. Statistical significance p -values of bivariate Granger causality correlation between Bitcoin price and concepts of forum opinions. Table 4. Statistical significance p -values of bivariate Granger causality correlation between Bitcoin transaction and concept of forum opinions.
Prediction results We built and applied the deep learning model based on the gathered and KDE-based rating data to predict the Bitcoin transaction and price. Table 6. Experimental results of predicted Bitcoin fluctuation. Discussion We analysed the user comments posted on a Bitcoin online forum to predict the fluctuation in the Bitcoin price and transaction count.
Conclusion With the increasing circulation of Bitcoin, its acceptability has drawn much attention in many ways [ 2 , 3 , 5 , 14 ]. Supporting information. S1 File. Results of crawling Bitcoin forum. S2 File. Python-based crawler source code for Bitcoin forum data collection. References 1. Nakamoto S. Bitcoin: A peer-to-peer electronic cash system. Bitcoin: Economics, technology, and governance.
The Journal of Economic Perspectives. View Article Google Scholar 3. Grinberg R. Bitcoin: An innovative alternative digital currency. View Article Google Scholar 4. Bitter to better—how to make bitcoin a better currency. View Article Google Scholar 5. Reid F, Harrigan M. An analysis of anonymity in the bitcoin system. Security and privacy in social networks. View Article Google Scholar 6.
Tensorflow: Large-scale machine learning on heterogeneous distributed systems. Deep learning. UMAP Workshops; Kaminski J. Nowcasting the Bitcoin Market with Twitter Signals. Kristoufek L. Scientific reports. View Article Google Scholar What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis.
PloS one. Yelowitz A, Wilson M. Characteristics of Bitcoin users: an analysis of Google search data. Applied Economics Letters. Bitcoin pricing, adoption, and usage: Theory and evidence. Why would online gamers share their innovation-conducive knowledge in the online game user community? Integrating individual motivations and social capital perspectives. Computers in Human Behavior. Virtual world currency value fluctuation prediction system based on user sentiment analysis.
Patterns and dynamics of users' behavior and interaction: Network analysis of an online community. An analysis of interaction and participation patterns in online community. Bitcoin transaction graph analysis. Mood and the market: can press reports of investors' mood predict stock prices? Twitter mood predicts the stock market. Journal of Computational Science.
Latent dirichlet allocation. Journal of machine Learning research. Learning the parts of objects by non-negative matrix factorization. Wang C, Blei DM. Collaborative topic modeling for recommending scientific articles. TM-LDA: efficient online modeling of latent topic transitions in social media. Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon. Knowledge-Based Systems. Bohr J, Bashir M.
Who uses bitcoin? Choi H, Varian H. Predicting the present with Google Trends. Economic Record. Quantifying trading behavior in financial markets using Google Trends. Can Google Trends search queries contribute to risk diversification? Using google trends for influenza surveillance in South China. Quantifying Wikipedia usage patterns before stock market moves. Early prediction of movie box office success based on Wikipedia activity big data.
Efficient estimation of word representations in vector space. The report suggests that cybercriminals have shifted more to ransomware, which is seen as more profitable. In January , researchers discovered the Smominru cryptomining botnet, which infected more than a half-million machines, mostly in Russia, India, and Taiwan. The simple reason why cryptojacking is becoming more popular with hackers is more money for less risk.
WIth ransomware, a hacker might get three people to pay for every computers infected, he explains. With cryptojacking, all of those infected machines work for the hacker to mine cryptocurrency. The risk of being caught and identified is also much less than with ransomware. The cryptomining code runs surreptitiously and can go undetected for a long time. Hackers tend to prefer anonymous cryptocurrencies like Monero and Zcash over the more popular Bitcoin because it is harder to track the illegal activity back to them.
Most are not new; cryptomining delivery methods are often derived from those used for other types of malware such as ransomware or adware. It first uses spear phishing to gain a foothold on a system, and it then steals Windows credentials and leverages Windows Management Instrumentation and the EternalBlue exploit to spread.
It then tries to disable antivirus software and competing cryptominers. In October, Palo Alto Networks released a report describing a cryptojacking botnet with self-spreading capabilities. Graboid, as they named it, is the first known cryptomining worm. It spreads by finding Docker Engine deployments that are exposed to the internet without authentication.
Palo Alto Networks estimated that Graboid had infected more than 2, Docker deployments. In June , Palo Alto Networks identified a cryptojacking scheme that used Docker images on the Docker Hub network to deliver cryptomining software to victims' systems.
Placing the cryptomining code within a Docker image helps avoid detection. It can detect mouse movement and suspend mining activities. This avoids tipping off the victim, who might otherwise notice a drop in performance. A few months ago, Comodo Cybersecurity found malware on a client's system that used legitimate Windows processes to mine cryptocurrency. Dubbed BadShell it used:.
At the EmTech Digital conference earlier this year, Darktrace told the story of a client , a European bank, that was experiencing some unusual traffic patterns on its servers. A physical inspection of the data center revealed that a rogue staffer had set up a cryptomining system under the floorboards. In March, Avast Software reported that cryptojackers were using GitHub as a host for cryptomining malware. They find legitimate projects from which they create a forked project. The malware is then hidden in the directory structure of that forked project.
Using a phishing scheme, the cryptojackers lure people to download that malware through, for example, a warning to update their Flash player or the promise of an adult content gaming site. Cryptojackers have discovered an rTorrent misconfiguration vulnerability that leaves some rTorrent clients accessible without authentication for XML-RPC communication. They scan the internet for exposed clients and then deploy a Monero cryptominer on them.
F5 Networks reported this vulnerability in February, and advises rTorrent users to make sure their clients do not accept outside connections. Initially Facexworm delivered adware. Earlier this year, Trend Micro found a variety of Facexworm that targeted cryptocurrency exchanges and was capabile of delivering cryptomining code. It still uses infected Facebook accounts to deliver malicious links, but can also steal web accounts and credentials, which allows it to inject cryptojacking code into those web pages.
In May, Total Security identified a cryptominer that spread quickly and proved effective for cryptojackers. WinstarNssmMiner does this by first launching an svchost. Since the computer sees as a critical process, it crashes once the process is removed. Cryptojacking has become prevalent enough that hackers are designing their malware to find and kill already-running cryptominers on systems they infect. CoinMiner is one example. It then kills those processes.
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Even celebrities like Mike Tyson have gotten involved; the former pro boxer has launched both a bitcoin ATM and a bitcoin wallet app. Bitcoin is the first decentralized digital currency that allows peer-to-peer transfers without any intermediaries such as banks, governments, agents or brokers, using the underlying technology of blockchain. Anyone around the world on the network can transfer bitcoins to someone else on the network regardless of geographic location; you just need to just open an account on the Bitcoin network and have some bitcoins in it, and then you can transfer those bitcoins.
How do you get bitcoins in your account? You can either purchase them online or mine them. Bitcoin can be used for online purchases and can be used as an investment instrument. Compared to traditional fiat currencies, assets can be transferred faster on the bitcoin network. Plus, all the information is available on a public ledger, so anyone can view the transactions. As mentioned, blockchain is the underlying technology of bitcoin.
Blockchain is a public distributed ledger in which transactions are recorded in chronological order. Any record or transaction added to the blockchain cannot be modified or altered, meaning transactions are safe from hacking. A block is the smallest unit of a blockchain, and it is a container that holds all the transaction details. A block has four fields, or primary attributes:. SHA is a cryptographic hash algorithm that produces a unique bit alphanumeric hash value for any given input, and that is the unique feature of this cryptographic algorithm: Whatever input you give, it will always produce a bit hash.
Bitcoin mining is the process of verifying bitcoin transactions and recording them in the public blockchain ledger. In blockchain, the transactions are verified by bitcoin users, so basically the transactions have to be verified by the participants of the network. Those who have the required hardware and computing power are called miners. We will talk more about them later, but the important concept to understand here is that there is nothing like a centralized body—a regulatory body, a governing body, a bank—to make bitcoin transactions go through.
Any user with mining hardware and Internet access can be a participant and contribute to the mining community. The process is solved based on a difficult mathematical puzzle called proof of work. The proof of work is needed to validate the transaction and for the miner to earn a reward.
All the miners are completing amongst themselves to mine a particular transaction; the miner who first solves the puzzle gets the reward. Miners are the network participants who have the necessary hardware and computing power to validate the transactions.
To understand bitcoin mining, you have to first understand the three major concepts of blockchain. In the bitcoin network, as mentioned, users called miners are trying to solve a mathematical puzzle. The puzzle is solved by varying a nonce that produces a hash value lower than a predefined condition, which is called a target. As of today, Bitcoin miners who solve a puzzle get a reward of Once a block is added to the blockchain, the bitcoins associated with the transactions can be spent and the transfer from one account to the other can be made.
To generate the hash, Bitcoin miners use the SHA hashing algorithm and define the hash value. If it is less than the defined condition the target , then the puzzle is deemed to be solved. If not, then they keep modifying the nonce value and repeat the SHA hashing function to generate the hash value again, and they keep doing this process until they get the hash value that is less than the target. To do that, what would the steps be? First, transaction data is shared with bitcoin users from the memory pool.
The transaction sits in an unmined pool of memory transactions. In a memory pool, unconfirmed transactions wait until they are verified and included in a new block. Bitcoin miners compete to validate the transaction using proof of work. The miner who solves the puzzle first shares the result across the other nodes.
Once the block has been verified, the nonce has been generated, then the nodes will start granting their approval. If maximum nodes grant their approval, the block becomes valid and is added to the blockchain. The miner who has solved the puzzle will also receive a reward of The 10 bitcoins for which the transaction was initiated now will be transferred from Beyonce to Jennifer. In proof of work, a predefined condition the target is adjusted for every 2, blocks, which is approximately every 14 days.
The average time to mine a block is 10 minutes, and to keep the time frame for block generation within 10 minutes, the target keeps adjusting itself. The difficulty of the puzzle changes depending on the time it takes to mine a block. This is how the difficulty of a block is generated: It is the hash target of the first block divided by the hash target of the current block. This is the difficulty being changed after every 2, blocks, so basically it is very hard to generate the proof of work—but it is very easy for the miners to verify once someone have solved the puzzle.
And once the majority of the miners reach a consensus, the block gets validated and added to the blockchain. What if someone tries to hack the data? Each block has solved a puzzle and generated a hash value of its own, which is its identifier. Now suppose a person tries to tamper with block B and change the data. The data is aggregated in the block, so if the data of the block changes, then the hash value that is the digital signature of the block will also change.
It will therefore corrupt the chain after it—the blocks ahead of block B will all get delinked, because the previous hash value of block C will not remain valid. Once the mining difficulty is increased, the average mining time returns to normal and the cycle repeats itself about every 2-weeks.
Wallets can be downloaded for free as can miner programs and once downloaded its ready to go. The reality is that your desktop computer or laptop will just not cut it in the mining world, so the options are to either make a sizeable investment and create a mining rig, or joining a mining pool or even subscribe to a cloud mining service, the latter requiring some degree of due diligence as is the case with any type of investment. In mining pools, the company running the mining pool charges a fee, whilst mining pools are capable of solving several blocks each day, giving miners who are part of a mining pool instant earnings.
While you can try to mine with GPUs and gaming machines, income is particularly low and miners may, in fact, lose money rather than make it, which leaves the more expensive alternative of dedicated ASICs hardware. Miners make Bitcoin by finding proof of work and creating blocks, with the current number of Bitcoins the miner receives per block creation standing at Can you get rich off the mining process?
Crypto Hub. Economic News. Expand Your Knowledge. Forex Brokers Filter. Trading tools. Macro Hub. Corona Virus. Stay Safe, Follow Guidance. World ,, Confirmed. Fetching Location Data…. Get Widget. Bitcoin Mining for Dummies: How to Mine Bitcoin Bitcoin mining is the validation of transactions that take place on each Bitcoin block. Bob Mason. What is Bitcoin Mining? What is Bitcoin Mining Difficulty? Miners will then receive transaction fees in the form of newly created Bitcoins. From Start to Finish: Bundle Transactions, Validation, Proof of Work, Blockchains and the Network The end to end process can perhaps be best described by the following chart that incorporates the various steps involved from mining to ultimately receiving well-earned Bitcoins and transaction fees: Bitcoin Mining Step-by-Step Verify if transactions are valid.
Transactions are bundled into a block The header of the most recent block is selected and entered into the new block as a hash. Proof of work is completed. A new block is added to the blockchain and added to the peer-to-peer network.
Proof of Work Step-by-Step A new block is proposed. A header of the most recent block and nonce are combined and a hash is created. A Hash number is generated. The miner receives the reward in Bitcoins and transaction fees. If the Hash is not less than the Target Value, the calculation is repeated and that takes the process of mining difficulty.
Mining Difficulty Step-by-Step More miners join the peer-to-peer network. The rate of block creation increases. Average mining times reduce. Mining difficulty increases. The rate of block creation declines. Average mining time returns to the ideal average mining time of 10 minutes. The cycle continues to repeat at an average 2-week cycle. What is Bitcoin Cloud Mining? No ASIC vendor endorsement. If there are no advertisements from the ASIC vendor, the mining company may not even own the hardware.
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