This document discusses a proposed Bitcoin transaction network using cache-based pattern matching rules. It begins with an introduction to existing systems for online transactions like physical cash, electronic payments, and e-cash. It then provides background on Bitcoin as a decentralized digital currency. The proposed system would parse Bitcoin transaction data from the blockchain into a database to build a Bitcoin Transaction Network. It would then cluster addresses and apply cache-based pattern matching rules to detect fraudulent transactions by matching new transactions to previously identified illegal addresses stored in the cache. Performance evaluation results suggest the proposed system could achieve higher detection rates and faster processing times compared to existing approaches.

1. A Bitcoin Transaction Network Using
Cache BasedPattern Matching Rules
Under esteemed guidance of
Dr. Jhansi Vazram. Bolla M.Tech Ph.D
professor
Submitted by
G Rajiv Trivedi
(20471D5804)

2. Online Transactions
• Physical cash
Non-traceable (well, mostly!)
Secure (mostly)
Low inflation
• Electronic credit or debit transactions
Bank sees all transactions
Merchants can track/profile customers

3. E-Cash
• Secure
• Single use
• Reliable
• Low inflation
• Privacy-preserving
• Various practical issues:
• Need for trusted central party
• Computationally expensive
• Etc.

4. Bitcoin
•A distributed, decentralized digital currency
system
•Released by Satoshi Nakamoto 2008
• Digital checks
• A distributed transaction log

5. Abstract
• Crypto currencies usage increasing every year around the world.
• Initially the work focused on development of bitcoin transaction network (BTN) using pattern matching rules
(PMR).
• The dataset preprocessing is carried out to identify the missed symbols, unknown characters from forensic
blockchain dataset.
• Petri-Net model applied on preprocessed dataset, which identifies the time stamp, transaction id, work tera
hash, and work error properties. Petri-Net model mainly used to parse and build the BTN model
• PMR conditions are developed to extract the transaction addresses extracted with time stamp details. So,
PMR detects the illegal payment addresses by matching the known data withillegal (spam) addresses.
• Further, cache based PMR (CPMR) isalso applied to detect the fraud transaction, which store all previous
detected illegal payment addresses.
• So, for every new transaction, CPMR will ignore all those previously stored (detected) illegal payment
addresses. This phenomenon causes reduction of fraud transaction detection time and processing becomes
faster.

6. Introduction
• Bitcoin has grown in popularity as an alternative from of currency and its rise has
been unstoppable since its inception by Satoshi Nakamoto
• Bitcoin is becoming largest and popular online financial transaction services
• As it not store any details of sender and receiver and is highly using for illegal
payments and consider as unlaw financial services
• Bitcoins are not typically associated with user identities such as usernames
,residence address, or other form of personal identification because of its
pseudonymous character
• Government finding difficulty in banning such currency
• Such situation it becomes mandatory to track address of Bitcoin payments by
analysing Blockchain Bitcoin Transaction Network.

7. Existing System
• In contrast to the previous approaches, our method seeks to create transaction patterns based on transaction
attributes
• To locate suspicious addresses on the basis of the patterns.
• The existing methods are aimed at discovering behaviour aspects that are underlying Bitcoin transactions

8. Proposed System
• The first step is to parse transaction data from Bitcoin Blockchain data and save parsed bitcoin transaction
information to a database. An open source tool called BitcoinDatabaseGenera-tor is used to save the data onto
the database;
• The second step is to read the transaction data from the database and to create a Bitcoin Transaction Net
(BTN);
• The third step is to cluster Bitcoin addresses and store the cluster information in the database.
• The input address clustering method used by [8, 10-14] is adopted in our framework. The pre-processing
procedure can be processed incrementally when new blocks are generated.

9. Software and Hardware Requirements
• Software Requirements
Operating system : Windows 7 Ultimate or above.
Coding Language : Python.
Front-End : Python
Back-End : Flask
.
• Hardware Requirements
Processor - P-IV
RAM - 2 GB (min)
Hard Disk - 40 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - SVGA 21"

10. Proposed BTN CPME blockchain

11. Analysis
• Pre prossing raw forensic blockchain data set contain noises missing
values
• Petri net model formal mathematical model it is used to explore
concurrent and asynchronous process
• Cache based patten matching rule to locate suspected address that
does not fit a predetermined pattern of Bitcoin Transaction
• Bitcoin Transaction has inside the data set
• Bitcoin Blockchain has Static and Dynamic Feature

12. Data set
• The Data Set associates actual entities with Bitcoin transactions that fall into the licit and illegal categories
• Suchas exchanges, wallet providers, miners, and other licit serviceproviders (scams, malware, terrorist
organizations,ransomware, Ponzi schemes, etc.). Sorting the graph's illegal
• Sorting the graph's illegal and legitimate nodes is the job at hand with this dataset.
• Sample data set

13. Performance evalution
• In below chart x-axis represents total withdraw from account0 to 1 and vice versa and y-axis represents
number of gather addresses for that withdrawal.
• Number of withdraw transactions

14. Performance evalution
In below chat x-axis represents number of account ID and y-axis represents
number of deposittransaction made by that account.
Further the below table shows thatthe proposed BTN-CPMR protocol
resulted in higher securitystandards compared to BAC [10], BitIodine [13],
and BlockChainVis [20]. Because, the proposed BTN-CPMR approach
reduced the TPT (ms), FTDT (ms), and increased the FTDA (%).
Method FTDA
(%)
TPT
(ms)
FTDT
(ms)
BAC [10] 91.056 43.614 42.516
BitIodine [13] 92.969 21.661 35.905
BlockChainVis
[20]
93.636 17.308 17.456
Proposed
BTN-CPMR
98.927 9.352 8.440

15. OUTPUT SCREEN SHOTS
Click on upload Blockchain Transaction

16. Selecting .csv file

17. Parse & Build BTN Petrinet Simulation

18. All Transaction Address

19. Suspected illegal Payment Address

20. Extension Cache List

21. Propose vs Extension Cache Execution Time

22. Conclusion
• The primary emphasis of this effort was placed on the construction of the BTN-
CPMP.
• The preprocessed dataset is then subjected to a Petri-Net model application, which
detects attributes such as the time stamp, transaction id, work tera hash, and work
error. .
• The Petri-Net model was primarily used in order to construct and parse the BTN
model.
• In addition, a CPMR is used in order to identify fraudulent transactions. This PMR
keeps a record of all unlawful payment addresses that have been identified in the
past.
• For every new Transaction , CPMR will be detected unlawful payment address

23. Feature scope
• This research presented an innovative methodology for the investigation of the Bitcoin transaction
network
• Bitcoin transactions are formalized as an expanded version of the Safe Petri net, which is referred
to as BTN.
• Static and dynamic aspects of a Bitcoin transaction may be understood by its structure and the
semantic qualities it has.
• There are many other transaction patterns that may be defined based on the qualities that have been
stated. It is possible to determine which addresses correspond to certain patterns.
• The approach that was developed has been shown to be an effective instrument for use in future
forensic investigations of Bitcoin transactions.

24. References
• [1] S. Nakamoto, "Bitcoin: A peer-to-peer electronic cash system," 2008.
• [2] D. Bryans, "Bitcoin and money laundering: mining for an effective solution," Indiana Law Journal, vol. 89, pp. 1-33,
2014.
• [3] M. J. Barratt, "SILK ROAD: EBAY FOR DRUGS: The journal publishes both invited and unsolicited letters," Addiction,
vol. 107, pp. 683-683, 2012.
• [4] M. Dittus, J. Wright, and M. Graham, "Platform Criminalism: The 'lastmile' geography of the darknet market supply
chain," in proceedings of the 2018 World Wide Web Conference on World Wide Web, 2018, pp. 277- 286.
• [5] G. White. UK company linked to laundered Bitcoin billions, BBC, (2018). Available:
https://www.bbc.com/news/technology-43291026
• [6] N. J. Ajello, "Fitting a Square Peg in a Round Hole: Bitcoin, Money Laundering, and the Fifth Amendment Privilege
Against SelfIncrimination," Brooklyn Law Review, vol. 80, p. 4, 2015.

25. ACCEPTANCE LETTER
Manuscript ID : ICSSIT – 363
Manuscript Title: A BITCOIN TRANSACTION NETWORK USING CACHE BASED
PATTERN MATCHING RULES
Author’s: G. Rajiv Trivedi, Jhansi Vazram, M. Sirisha, Narasaraopeta Engineering College. Dear
Author’s,
Greetings from Francis Xavier Engineering College!
5th International Conference on Smart Systems and Inventive Technology ICSSIT 2023
would like to congratulate you on the acceptance of your research manuscript to the
International Conference ICSSIT 2023 which will be held on 23-25, January 2023 at
Francis Xavier Engineering College, Tirunelveli , India. You have selected to deliver an
oral presentation on your research work at ICSSIT 2023 conference.
ICSSIT is the International IEEE recognized conference, where all the Manuscripts
included in the ICSSIT 2023 proceedings will be submitted for inclusion into IEEE Xplore.
In this regard, ICSSIT welcomes the wide range of research experts, academicians and
industrialists, to present and deliver potential research insights to the young research
minds.
In this regard, we appreciate if you could send the final Manuscript, copyright form and
other necessary documents to the conference at the earliest, to ensure a timely
publication of your research Manuscript. When submitting your final Manuscript, please
highlight the changes made to the research Manuscript according to the specified
reviewer comments.
NOTE: Please include your phone number in the reply email for important communication.
Yours’ Sincerely
Conference Chair Dr. G. Rajakumar

26. THANK YOU