Bitcoin Concepts, Threats, and Machine-Learning Security Solutions

The concept of Bitcoin was first introduced by an unknown individual (or a group of people) named Satoshi Nakamoto before it was released as open-source software in 2009. Bitcoin is a peer-to-peer cryptocurrency and a decentralized worldwide payment system for digital currency where transactions take place among users without any intermediary. Bitcoin transactions are performed and verified by network nodes and then registered in a public ledger called blockchain, which is maintained by network entities running Bitcoin software. To date, this cryptocurrency is worth close to U.S. $150 billion and widely traded across the world. However, as Bitcoin’s popularity grows, many security concerns are coming to the forefront. Overall, Bitcoin security inevitably depends upon the distributed protocols-based stimulant-compatible proof-of-work that is being run by network entities called miners, who are anticipated to primarily maintain the blockchain (ledger). As a result, many researchers are exploring new threats to the entire system, introducing new countermeasures, and therefore anticipating new security trends. In this survey paper, we conduct an intensive study that explores key security concerns. We first start by presenting a global overview of the Bitcoin protocol as well as its major components. Next, we detail the existing threats and weaknesses of the Bitcoin system and its main technologies including the blockchain protocol. Last, we discuss current existing security studies and solutions and summarize open research challenges and trends for future research in Bitcoin security.

[1]  Vukosi N. Marivate,et al.  Unsupervised learning for robust Bitcoin fraud detection , 2016, 2016 Information Security for South Africa (ISSA).

[2]  Ravikiran Vatrapu,et al.  A first estimation of the proportion of cybercriminal entities in the bitcoin ecosystem using supervised machine learning , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[3]  Xiaochun Cao,et al.  Double-Spending Detection for Fast Bitcoin Payment Based on Artificial Immune , 2017, NCTCS.

[4]  Aggelos Kiayias,et al.  Blockchain Mining Games , 2016, EC.

[5]  Andreas Bogner,et al.  Seeing is understanding: anomaly detection in blockchains with visualized features , 2017, UbiComp/ISWC Adjunct.

[6]  Fergal Reid,et al.  An Analysis of Anonymity in the Bitcoin System , 2011, PASSAT 2011.

[7]  Steve Y. Yang,et al.  Bitcoin Market Return and Volatility Forecasting Using Transaction Network Flow Properties , 2015 .

[8]  Hui Xiong,et al.  K-means clustering versus validation measures: a data distribution perspective , 2006, KDD '06.

[9]  Hans-Arno Jacobsen,et al.  Blockchain Technology , 2018, Blockchain Regulation and Governance in Europe.

[10]  Emin Gün Sirer,et al.  Majority Is Not Enough: Bitcoin Mining Is Vulnerable , 2013, Financial Cryptography.

[11]  Sushmita Ruj,et al.  Bitcoin Block Withholding Attack: Analysis and Mitigation , 2017, IEEE Transactions on Information Forensics and Security.

[12]  Ravikiran Vatrapu,et al.  Breaking Bad: De-Anonymising Entity Types on the Bitcoin Blockchain Using Supervised Machine Learning , 2018, HICSS.

[13]  Nick Szabo,et al.  Formalizing and Securing Relationships on Public Networks , 1997, First Monday.

[14]  Laurent Vanbever,et al.  Hijacking Bitcoin: Routing Attacks on Cryptocurrencies , 2016, 2017 IEEE Symposium on Security and Privacy (SP).

[15]  Somdip Dey,et al.  Securing Majority-Attack in Blockchain Using Machine Learning and Algorithmic Game Theory: A Proof of Work , 2018, 2018 10th Computer Science and Electronic Engineering (CEEC).

[16]  Satoshi Nakamoto.  Bitcoin : A Peer-to-Peer Electronic Cash System , 2009 .

[17]  Meni Rosenfeld,et al.  Analysis of Hashrate-Based Double Spending , 2014, ArXiv.

[18]  Miguel Castro,et al.  Practical byzantine fault tolerance and proactive recovery , 2002, TOCS.

[19]  Dejan Vujičić,et al.  Blockchain technology, bitcoin, and Ethereum: A brief overview , 2018, 2018 17th International Symposium INFOTEH-JAHORINA (INFOTEH).

[20]  Amandeep Bagga,et al.  Evolution of bitcoin and security risk in bitcoin wallets , 2017, 2017 International Conference on Computer, Communications and Electronics (Comptelix).

[21]  Damon McCoy,et al.  Stressing Out: Bitcoin "Stress Testing" , 2016, Financial Cryptography Workshops.

[22]  Xingtong Liu,et al.  Unlinkable Coin Mixing Scheme for Transaction Privacy Enhancement of Bitcoin , 2018, IEEE Access.

[23]  Rich Caruana,et al.  An empirical comparison of supervised learning algorithms , 2006, ICML.

[24]  Artsiom Holub,et al.  COINHOARDER: Tracking a ukrainian bitcoin phishing ring DNS style , 2018, 2018 APWG Symposium on Electronic Crime Research (eCrime).

[25]  Jennifer Xu,et al.  Are blockchains immune to all malicious attacks? , 2016 .

[26]  Sushmita Ruj,et al.  Retricoin: Bitcoin based on compact proofs of retrievability , 2016, ICDCN.

[27]  Amos Fiat,et al.  Untraceable Electronic Cash , 1990, CRYPTO.

[28]  Deepak Puthal,et al.  Everything You Wanted to Know About the Blockchain: Its Promise, Components, Processes, and Problems , 2018, IEEE Consumer Electronics Magazine.

[29]  Tatsuaki Okamoto,et al.  Universal Electronic Cash , 1991, CRYPTO.

[30]  Joshua A. Kroll,et al.  The Economics of Bitcoin Mining, or Bitcoin in the Presence of Adversaries , 2013 .

[31]  Deepak Zambre.  Analysis of Bitcoin Network Dataset for Fraud , 2013 .

[32]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[33]  Maria Zhdanova,et al.  No Smurfs: Revealing Fraud Chains in Mobile Money Transfers , 2014, 2014 Ninth International Conference on Availability, Reliability and Security.

[34]  Rémy Cazabet,et al.  Tracking Bitcoin Users Activity Using Community Detection on a Network of Weak Signals , 2017, COMPLEX NETWORKS.

[35]  Mauro Conti,et al.  A Survey on Security and Privacy Issues of Bitcoin , 2017, IEEE Communications Surveys & Tutorials.

[36]  Vukosi N. Marivate,et al.  A Multifaceted Approach to Bitcoin Fraud Detection: Global and Local Outliers , 2016, 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA).

[37]  Nan Meng,et al.  From Bitcoin to cybersecurity: A comparative study of blockchain application and security issues , 2017, 2017 4th International Conference on Systems and Informatics (ICSAI).

[38]  Björn Scheuermann,et al.  Bitcoin and Beyond: A Technical Survey on Decentralized Digital Currencies , 2016, IEEE Communications Surveys & Tutorials.

[39]  Fouad Amine Guenane,et al.  Bitcoin transaction: From the creation to validation, a protocol overview , 2017, 2017 1st Cyber Security in Networking Conference (CSNet).

[40]  Zhang Zhe,et al.  A review on consensus algorithm of blockchain , 2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[41]  David Chaum,et al.  Blind Signatures for Untraceable Payments , 1982, CRYPTO.

[42]  Maxim Panov,et al.  Automatic Bitcoin Address Clustering , 2017, 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA).

[43]  Steven Lee,et al.  Anomaly Detection in the Bitcoin System - A Network Perspective , 2016, ArXiv.

[44]  Aviv Zohar,et al.  Secure High-Rate Transaction Processing in Bitcoin , 2015, Financial Cryptography.

[45]  Xingtong Liu,et al.  An efficient method to enhance Bitcoin wallet security , 2017, 2017 11th IEEE International Conference on Anti-counterfeiting, Security, and Identification (ASID).

[46]  Sadia Afroz,et al.  Backpage and Bitcoin: Uncovering Human Traffickers , 2017, KDD.

[47]  A. Besir Kurtulmus,et al.  Trustless Machine Learning Contracts; Evaluating and Exchanging Machine Learning Models on the Ethereum Blockchain , 2018, ArXiv.

[48]  Tyler Moore,et al.  The impact of DDoS and other security shocks on Bitcoin currency exchanges: evidence from Mt. Gox , 2017, J. Cybersecur..

[49]  Abhi Shelat,et al.  Socially Optimal Mining Pools , 2017, WINE.

[50]  Jason Hirshman,et al.  Unsupervised Approaches to Detecting Anomalous Behavior in the Bitcoin Transaction Network , 2013 .

[51]  Massimo Bartoletti,et al.  Data Mining for Detecting Bitcoin Ponzi Schemes , 2018, 2018 Crypto Valley Conference on Blockchain Technology (CVCBT).

[52]  Ghassan O. Karame,et al.  Misbehavior in Bitcoin: A Study of Double-Spending and Accountability , 2015, TSEC.

[53]  Jeremy Clark,et al.  SoK: Research Perspectives and Challenges for Bitcoin and Cryptocurrencies , 2015, 2015 IEEE Symposium on Security and Privacy.

[54]  Aron Laszka,et al.  When Bitcoin Mining Pools Run Dry - A Game-Theoretic Analysis of the Long-Term Impact of Attacks Between Mining Pools , 2015, Financial Cryptography Workshops.

[55]  Prateek Saxena,et al.  On Power Splitting Games in Distributed Computation: The Case of Bitcoin Pooled Mining , 2015, 2015 IEEE 28th Computer Security Foundations Symposium.

[56]  Vitalik Buterin.  A NEXT GENERATION SMART CONTRACT & DECENTRALIZED APPLICATION PLATFORM , 2015 .

[57]  Steven Lee,et al.  Anomaly Detection in Bitcoin Network Using Unsupervised Learning Methods , 2016, ArXiv.

[58]  Subutai Ahmad,et al.  Evaluating Real-Time Anomaly Detection Algorithms -- The Numenta Anomaly Benchmark , 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).

[59]  Vinay J. Ribeiro,et al.  RansomWall: A layered defense system against cryptographic ransomware attacks using machine learning , 2018, 2018 10th International Conference on Communication Systems & Networks (COMSNETS).

[60]  Jeffrey S. Rosenschein,et al.  Bitcoin Mining Pools: A Cooperative Game Theoretic Analysis , 2015, AAMAS.

[61]  Elaine Shi,et al.  Bitter to Better - How to Make Bitcoin a Better Currency , 2012, Financial Cryptography.

[62]  T. Maugh.  Why buy when you can rent? , 1984, Science.

[63]  Stephanie Forrest,et al.  A Machine Learning Evaluation of an Artificial Immune System , 2005, Evolutionary Computation.

[64]  Ghassan O. Karame,et al.  Is Bitcoin a Decentralized Currency? , 2014, IEEE Security & Privacy.