Guzmán Llambías, Bruno Bradach, Juan J. Nogueira, Laura González · 5 authors
Blockchain is a distributed ledger technology (DLT) to manage data in a decentralised way. During the last years, interoperability has become one of the main challenges within blockchain research as blockchains increasingly require integration between each other. Indeed, blockchains work by design in silos of information as interoperability is not a native feature. The main efforts in the field are focused on blockchains, such as Bitcoin and Ethereum. However, interoperability in DLT remains as an almost untouched area of work as they introduce additional requirements focusing on privacy and identity. Although there are some interoperability solutions for DLT, they are either high-level design proposals not providing concrete implementations or focus on interoperability issues between business applications and blockchain platforms. In this paper we propose a gateway-based platform-to-platform interoperability solution for DLT, which comprises a detailed solution design and a reference implementation. The proposal was assessed through the development of a social security case scenario and the evaluation through two interoperability frameworks. A reference implementation was built using two DLT: Hyperledger Fabric and Corda. The experimental results shows that it is possible to achieve technical interoperability between two heterogeneous DLT platforms using a gateway-based interoperability solution, relaxing decentralisation, data privacy, identity and authorisation management properties.
We develop a general and practical framework to address the problem of the optimal design of dynamic fee mechanisms for multiple blockchain resources. Our framework allows to compute policies that optimally trade-off between adjusting resource prices to handle persistent demand shifts versus being robust to local noise in the observed block demand. In the general case with more than one resource, our optimal policies correctly handle cross-effects (complementarity and substitutability) in resource demands. We also show how these cross-effects can be used to inform resource design, i.e. combining resources into bundles that have low demand-side cross-effects can yield simpler and more efficient price-update rules. Our framework is also practical, we demonstrate how it can be used to refine or inform the design of heuristic fee update rules such as EIP-1559 or EIP-4844 with two case studies. We then estimate a uni-dimensional version of our model using real market data from the Ethereum blockchain and empirically compare the performance of our optimal policies to EIP-1559.
Rad istražuje problem scalpinga u kontekstu preprodaje ulaznica za događaje i predstavlja rješenje putem web3 aplikacije. Scalping, praksa brze preprodaje ulaznica s ciljem ostvarivanja profita, postala je široko rasprostranjena pojava koja negativno utječe na kupce i ozbiljno narušava integritet industrije događanja. Ova web3 aplikacija koristi pametne ugovore na Ethereum blockchainu i koncept nedjeljivih žetona (eng. non-fungible token) kako bi stvorila transparentno i pošteno tržište ulaznica.
With the economic development of Ethereum, the frequent security incidents involving smart contracts running on this platform have caused billions of dollars in losses. Consequently, there is a pressing need to identify the vulnerabilities in contracts, while the state-of-the-art (SOTA) detection methods have been limited in this regard as they cannot overcome three challenges at the same time. (i) Meet the requirements of detecting the source code, bytecode, and opcode of contracts simultaneously; (ii) reduce the reliance on manual pre-defined rules/patterns and expert involvement; (iii) assist contract developers in completing the contract lifecycle more safely,e.g., vulnerability repair and abnormal monitoring. With the development of machine learning (ML), using it to detect the contract runtime execution sequences (called instances) has made it possible to address these challenges. However, the lack of datasets with fine-grained sequence labels poses a significant obstacle, given the unreadability of bytecode/opcode. To this end, we propose a method named VulHunter that extracts the instances by traversing the Control Flow Graph built from contract opcodes. Based on the hybrid attention and multi-instance learning mechanisms, VulHunter reasons the instance labels and designs an optional classifier to automatically capture the subtle features of both normal and defective contracts, thereby identifying the vulnerable instances. Then, it combines the symbolic execution to construct and solve symbolic constraints to validate their feasibility. Finally, we implement a prototype of VulHunter with 15K lines of code and compare it with 9 SOTA methods on five open source datasets including 52,042 source codes and 184,289 bytecodes. The results indicate that VulHunter can detect contract vulnerabilities more accurately (90.04% accurate rate and 85.60% F1 score), efficiently (only took 4.4 seconds per contract), and robustly (0% analysis failed rate) than the SOTA methods. Also, it can focus on specific metrics such as precision and recall by employing different baseline models and hyperparameters to meet the various user requirements,e.g., vulnerability discovery and misreport mitigation. More importantly, compared with the previous ML-based arts, it can not only provide classification results, defective contract source code statements, key opcode fragments, and vulnerable execution paths, but also eliminate misreports and facilitate more operations such as vulnerability repair and attack simulation during the contract lifecycle.
Selfish Mining is strategic rule-breaking to maximize rewards in proof-of-work protocols [3] and Markov Decision Processes (MDPs) are the preferred tool for finding optimal strategies in Bitcoin [4, 10] and similar linear chain protocols [12]. Protocols increasingly adopt non-sequential chain structures [11], for which MDP analysis is more involved [2]. To date, researchers have tailored specific attack spaces for each protocol [2, 4, 5, 7, 10, 12]. Assumptions differ, and validating and comparing results is difficult. To overcome this, we propose a generic attack space that supports a wide range of DAG protocols, including Ethereum, Fruitchains, and Parallel Proof-of-Work. Our approach is modular: we specify each protocol as one program, and then derive the Selfish Mining MDPs automatically.
With the rapid development of information technologies, industrial Internet has become more open, and security issues have become more challenging. The endogenous security mechanism can achieve the autonomous immune mechanism without prior knowledge. However, endogenous security lacks a scientific and formal definition in industrial Internet. Therefore, firstly we give a formal definition of endogenous security in industrial Internet and propose a new industrial Internet endogenous security architecture with cost analysis. Secondly, the endogenous security innovation mechanism is clearly defined. Thirdly, an improved clone selection algorithm based on federated learning is proposed. Then, we analyze the threat model of the industrial Internet identity authentication scenario, and propose cross-domain authentication mechanism based on endogenous key and zero-knowledge proof. We conduct identity authentication experiments based on two types of blockchains and compare their experimental results. Based on the experimental analysis, Ethereum alliance blockchain can be used to provide the identity resolution services on the industrial Internet. Internet of Things Application (IOTA) public blockchain can be used for data aggregation analysis of Internet of Things (IoT) edge nodes. Finally, we propose three core challenges and solutions of endogenous security in industrial Internet and give future development directions.
This paper presents secure mempool designs under asymmetric DoS attacks. We formulate safety definitions under two abstract DoSes, namely eviction- and locking-based attacks. We propose a safe transaction admission framework for securing mempools, named saferAd, that achieves both eviction- and locking-safety. The proven security stems from an upper bound of the attack damage under locking DoSes and a lower bound of the attack cost under eviction DoSes. The evaluation by replaying real transaction traces shows saferAd incurs negligible latency or insignificant change of validator revenue.
Michael Demmler, Universidad Autónoma de Querétaro-Facultad de Contaduría y Administración
This study explores the financial performance of cryptocurrencies during the COVID-19 pandemic.In particular, the research objective is to compare the market price movements of the leading cryptocurrencies Bitcoin, Ethereum, BNB and XRP before and during the COVID-19 pandemic based on a longitudinal, exploratory, and quantitative research design which is centered on the analysis of the statistical moments of logarithmic return distributions, tests for structural changes combined with stationarity tests and portfolio optimization strategies.Results of the analysis show a clear change of the medium-to long-term return behavior of the analyzed cryptocurrencies during the pandemic, although not immediately after the pandemic announcement of the WHO in March 2020.Especially Bitcoin, BNB and Ethereum show comparable and even more favorable return characteristics in most samples compared to traditional investment alternatives.Furthermore, the diversification potential of cryptocurrency portfolios appears to be quite limited.
In recent years the adoption of smart contracts, in blockchain platforms, has increased substantially. One of the main applications of smart contracts are the so called Decentralized Autonomous Organizations (DAO), which originated from an idea envisaged by Buterin, in his Ethereum white paper. Indeed, DAOs are decentralized organizations, where the members implement their decisions using smart contracts. In the article, we introduce a simple framework for a DAO, and then we discuss some governance issues. In particular, we focus on how DAO members could be induced to dedicate sufficient time to voting sessions, for a proper functioning of the organization. Indeed, recent empirical research suggests how the members’ participation rate, to voting activities, has a meaningful positive correlation with to a DAO's performance. More specifically, we formalize the notion of attention time and propose a simple model for the so-called Holographic Consensus, a protocol introduced by the DAOStack platform to solve the issue of limited time dedicated to governance, discussing under what conditions DAO members may choose the so-called boosting voting procedure.
Kriptovalute su, kao neovisan sustav, postale važan element globalnog financijskog sustava. Sve se više koriste kao alat za obavljanje međunarodnih plaćanja i alat za ulaganje. Složeni sustav njihova djelovanja i nedostatak regulative uzrokuju da ulaganja u kriptovalute karakterizira puno veći rizik u usporedbi s drugom imovinom. Cilj ovog rada je usporediti tržište kriptovaluta s tržištima kapitala. Ulaganja u kriptovalute u prosjeku donose najviše dnevne stope prinosa, ali su s druge strane karakterizirane najvećim rizikom. Na takav rezultat mogli su znatno utjecati raširena postojanost ultraniskih kamatnih stopa i pad atraktivnosti dužničkih vrijednosnih papira. Najbolji rezultati postiže se za ulaganja u bitcoin i ethereum, koji imaju najveći udio u tržišnoj kapitalizaciji kriptovaluta
<strong>Abstract</strong>: As India commences its journey on a digital trajectory, the fintech ecosystem has been the crucial enabler for digital transformation. A step ahead, next-generation trading currencies based upon blockchain technology became one of the most talked about technology terms in the fintech sphere. Digital currencies and assets, which also use DLT underneath, are however subjected to stricter legal lenses. This particular apprehension of policymakers and the Central Bank indicates the essentiality of legal frameworks that safeguards users’ interest as well as the nation’s economic and security interest simultaneously and this paper provides a ground to analyze the same. This paper is one of the primary papers that studies the legal framework regarding blockchain and digital assets through a Systematic Literature Review Methodology with consideration of 43 kinds of literature providing an Indian legal perspective which could be a critical resource for existing legal frameworks and possible future alterations required thereof. <strong>Keywords: </strong>Legal Framework, Blockchain, Cryptocurrency, Decentralize, Ethereum, Smart Contract <strong>JEL Classification Number:</strong> K39, K40
Cyber attack detection is the process of detecting and responding to malicious or unauthorized activities in networks, computer systems, and digital environments. The objective is to identify these attacks early, safeguard sensitive data, and minimize the potential damage. An intrusion detection system (IDS) is a cybersecurity tool mainly designed to monitor system activities or network traffic to detect and respond to malicious or suspicious behaviors that may indicate a cyber attack. IDSs that use machine learning (ML) and deep learning (DL) have played a pivotal role in helping organizations identify and respond to security risks in a prompt manner. ML and DL techniques can analyze large amounts of information and detect patterns that may indicate the presence of malicious or cyber attack activities. Therefore, this study focuses on the design of blockchain-assisted hybrid metaheuristics with a machine learning-based cyber attack detection and classification (BHMML-CADC) algorithm. The BHMML-CADC method focuses on the accurate recognition and classification of cyber attacks. Moreover, the BHMML-CADC technique applies Ethereum BC for attack detection. In addition, a hybrid enhanced glowworm swarm optimization (HEGSO) system is utilized for feature selection (FS). Moreover, cyber attacks can be identified with the design of a quasi-recurrent neural network (QRNN) model. Finally, hunter–prey optimization (HPO) algorithm is used for the optimal selection of the QRNN parameters. The experimental outcomes of the BHMML-CADC system were validated on the benchmark BoT-IoT dataset. The wide-ranging simulation analysis illustrates the superior performance of the BHMML-CADC method over other algorithms, with a maximum accuracy of 99.74%.
Luis Miguel Jiménez Gómez, Erick Lambis-Alandete, Juan D. Velásquez-Henao
Debido al alto atractivo de las criptomonedas, los inversionistas y los investigadores han prestado mayor atención en la previsión de los precios de las criptomonedas. Con el desarrollo metodológico del Deep Learning, la previsión de las criptomonedas ha tenido mayor importancia en los últimos años. En este artículo, se evalúan cuatro modelos de Deep Learning: RNN, LSTM, GRU y CNN-LSTM con el objetivo de evaluar el desempeño en el pronóstico del precio de cierre diario de las dos criptomonedas más importantes: Bitcoin y Ethereum. Se utilizaron métricas de análisis de desempeño como MAE, RMSE, MSE y MAPE y como métrica de ajuste, el R2. Cada modelo de Deep Learning fue optimizado a partir de un conjunto de hiperparámetros y para diferentes ventanas de tiempo. Los resultados experimentales mostraron que el algoritmo RNN tuve un rendimiento superior en la predicción del precio de Bitcoin y el algoritmo LSTM en el precio de Ethereum. Incluso, ambos métodos presentaron mejor desempeño con dos modelos de la literatura evaluados. Finalmente, la confiabilidad del pronóstico de cada modelo se evaluó analizando la autocorrelación de los errores y se encontró que los dos modelos más eficientes tienen alto poder de generalización.
Pasar e-commerce Indonesia diperkirakan dapat menjadi kontributor pertumbuhan utama di Asia Pasifik. Berdasarkan analisis RedSeer, pasar e-commerce Indonesia diproyeksikan dapat meningkat menjadi US$137.5 miliar pada tahun 2025. Pada perkembangannya, e-commerce memerlukan suatu sistem rekomendasi. Sistem rekomendasi adalah aplikasi perangkat lunak yang menyediakan atau menyarankan item kepada pengguna. Sistem ini menggunakan teknik penyaringan untuk memberikan rekomendasi. Teknik berbasis konten diadopsi karena bersifat user independent. TF-IDF (Term Frequency Inverse Document Frequency) dan kesamaan kosinus digunakan untuk menentukan seberapa relevan atau miripnya produk dengan produk lainnya. Hasil dari penelitian ini sistem telah berhasil memberikan rekomendasi produk yang cukup relevan dengan nilai rata-rata precision 0.855 atau 85% dengan hasil rekomendasi yang ditampilkan hanya produk yang memiliki score kemiripan minimal 0.1 dan maksimal 1. Selain itu, e-commerce tidak lepas dari potensi ancaman dari peretas karena biasanya perusahaan e-commerce menyimpan datanya di lokasi terpusat (centralized). Namun masalah keamanan tersebut dapat dijawab melalui penggunaan teknologi blockchain.
Auto-completing code enables developers to speed up coding significantly. Recent advances in transformer-based large language model (LLM) technologies have been applied to code synthesis. However, studies show that many of such synthesized codes contain vulnerabilities. We propose a novel vulnerability-constrained decoding approach to reduce the amount of vulnerable code generated by such models. Using a small dataset of labeled vulnerable lines of code, we fine-tune an LLM to include vulnerability labels when generating code, acting as an embedded classifier. Then, during decoding, we deny the model to generate these labels to avoid generating vulnerable code. To evaluate the method, we chose to automatically complete Ethereum Blockchain smart contracts (SCs) as the case study due to the strict requirements of SC security. We first fine-tuned the 6-billion-parameter GPT-J model using 186,397 Ethereum SCs after removing the duplication from 2,217,692 SCs. The fine-tuning took more than one week using ten GPUs. The results showed that our fine-tuned model could synthesize SCs with an average BLEU (BiLingual Evaluation Understudy) score of 0.557. However, many codes in the auto-completed SCs were vulnerable. Using the code before the vulnerable line of 176 SCs containing different types of vulnerabilities to auto-complete the code, we found that more than 70% of the auto-completed codes were insecure. Thus, we further fine-tuned the model on other 941 vulnerable SCs containing the same types of vulnerabilities and applied vulnerability-constrained decoding. The fine-tuning took only one hour with four GPUs. We then auto-completed the 176 SCs again and found that our approach could identify 62% of the code to be generated as vulnerable and avoid generating 67% of them, indicating the approach could efficiently and effectively avoid vulnerabilities in the auto-completed code.
Matheus Acauã Dias, Marco Aurélio Amaral Henriques
Este trabalho tem como objetivo analisar e comparar o desempenho de dois mecanismos de consenso Proof-of-Stake para blockchains públicas: Casper (proposto pelo grupo da criptomoeda Ethereum) e Committeeless Proof-of-Stake (CPoS, proposto pelo grupo ReGrAS/Unicamp). O estudo se baseia em uma análise teórica e prática dos dois mecanismos, com o intuito de identificar vantagens e limitações de cada um, a fim de oferecer uma compreensão mais aprofundada sobre esses mecanismos identificando limitações e melhorias.
The use of smart contracts is transforming traditional industry and business practices. It enables the automatic enforcement of contractual terms without the need for a trusted third party. Smart contracts can automate a variety of transactions on Blockchain. Despite their numerous benefits, some challenges, such as security vulnerabilities, still need to be addressed before smart contracts can be widely adopted.This paper introduces two models of smart contracts – one simple and one more complex – using the interactive theorem prover Agda. This is a step towards converting the previous work of verifying Bitcoin smart contracts using weakest preconditions [1], [2] to Ethereum’s Solidity-style [3] smart contracts. Since Ethereum’s contracts are object-oriented, this model is substantially more complex than Bitcoin’s. We provide models supporting simple and complex executions, the calling of other contracts, and functions referring to addresses and messages. Furthermore, these models also support transferring money to other contracts and updating specific contracts, and the more complex model includes gas cost and pure functions.
Розвинені демократичні країни стрімко удосконалюють інфраструктуру систем електораль-ного волевиявлення. Технологія блокчейн швидко заполонила дефіцит інновацій в різноманітних сферах людської діяльності. У системи підтримки виборчого процесу також поступово впроваджуються концепції децентралізованого реєстру зберігання голосів та виключення із парадигми голосування третіх зацікавлених осіб. Від третіх осіб, які зазвичай є фальсифікаторами голосів, ніяк не можна було позбутись. Із появою блокчейнів така можливість стає реальністю. Об’єктом дослідження є процес електронного голосування. Предметом дослідження є системи електронного голосування на децентралізованих реєстрах типу блокчейн. Метою роботи є проведення оглядового дослідження існуючих систем електронного голосування на найбільш вживаних, поширених і надійних блокчейнах Bitcoin та Ethereum. Серед досліджених блокчейн-рішень електронного голосування, нажаль, жодне не впроваджено на загальнонаціональному рівні. У майбутніх дослідженнях планується пошук систем електронного голосування на новітніх блокчейнах, зокрема на блокчейні Near Protocol.
Malka N. Halgamuge, Geetha. K. Munasinghe, Moshe Zukerman
The Internet of Things (IoT) has emerged with Distributed Ledger Technology (DLT) to address existing scalability challenges and improve the trustworthiness of machine-to-machine communication. Among the numerous potential benefits of combining IoT and DLT, Blockchain, a subset of DLT, is a crucial enabler to accelerate secure IoT adoption. Appending a new block to a blockchain, especially in a blockchain-based IoT ecosystem, requires more delay than expected. This delay is one of several issues limiting the broader adoption of blockchain within the IoT domain. To assess this delay, we develop a new comprehensive model to estimate the time required to generate a new block in a blockchain-enabled IoT system. To this end, we develop sub-computation models and compare time consumption associated with the block generation process by conducting an extensive analysis of the following selected IoT layers: device layer, cluster head layer, fog/edge layer, and cloud layer. Our study identifies potential time-consuming steps in adding a new block to a network. Our results demonstrate that the type of blockchain framework and data encryption algorithms could affect the block generation time and that Avalanche, Conflux, Algorand, Polkadot Hyperledger Fabric outperforms Ethereum in terms of block generation time in IoT networks. On the other hand, the blockchain framework does not play a significant role in block generation time for smaller data packets. We also observed the benefit of using 256-bit ECC (elliptic curve cryptography) encryption and the fog layer in IoT networks to enhance the scalability of the block generation process. All in all, our results indicate that the total block generation time varies depending on the selected IoT framework, data encryption algorithm, blockchain type, and key functions of the layers. However, we found that time delays associated with queuing or block size are negligible relative to the other key components of block generation time.
Phan The Duy, Nghi Hoang Khoa, Nguyen Huu Quyen, Le Cong Trinh · 7 authors
This paper presents VulnSense framework, a comprehensive approach to efficiently detect vulnerabilities in Ethereum smart contracts using a multimodal learning approach on graph-based and natural language processing (NLP) models. Our proposed framework combines three types of features from smart contracts comprising source code, opcode sequences, and control flow graph (CFG) extracted from bytecode. We employ Bidirectional Encoder Representations from Transformers (BERT), Bidirectional Long Short-Term Memory (BiLSTM) and Graph Neural Network (GNN) models to extract and analyze these features. The final layer of our multimodal approach consists of a fully connected layer used to predict vulnerabilities in Ethereum smart contracts. Addressing limitations of existing vulnerability detection methods relying on single-feature or single-model deep learning techniques, our method surpasses accuracy and effectiveness constraints. We assess VulnSense using a collection of 1.769 smart contracts derived from the combination of three datasets: Curated, SolidiFI-Benchmark, and Smartbugs Wild. We then make a comparison with various unimodal and multimodal learning techniques contributed by GNN, BiLSTM and BERT architectures. The experimental outcomes demonstrate the superior performance of our proposed approach, achieving an average accuracy of 77.96\% across all three categories of vulnerable smart contracts.
Kriptovalute su inovativne digitalne valute koje koriste kriptografske tehnike za osiguranje transakcija. One su postale sveprisutne u suvremenom financijskom svijetu i imaju različite primjene, uključujući kupnju, slanje novca i investiranje. Za sigurno pohranjivanje i upravljanje kriptovalutama koriste se novčanici, koji mogu biti online, mobilni, hardverski ili papirnati. Ključna tehnologija koja omogućuje funkcioniranje kriptovaluta je blockchain. Ulančani blokovi predstavljaju distribuirani javni registar svih transakcija kriptovaluta, osiguravajući njihovu transparentnost i integritet. Bitcoin, kao prva i najpoznatija kriptovaluta, omogućuje brze, sigurne i jeftine transakcije diljem svijeta. Ethereum, s druge strane, ističe se po sposobnosti izvršavanja pametnih ugovora, što otvara vrata za razne primjene u decentraliziranim aplikacijama. Bitna karakteristika kriptovaluta je decentralizacija, što znači da nema centralnog tijela koje kontrolira kriptovalutu ili transakcije. To pruža korisnicima veću autonomiju, ali istovremeno donosi izazove u regulaciji i prihvaćanju od strane tradicionalnih financijskih institucija. Pametni ugovori omogućuju automatsko izvršavanje transakcija i dogovora, što otvara mogućnosti za automatizaciju poslovnih procesa i eliminaciju posrednika. U svijetu kriptovaluta postoje i neki izazovi i fenomeni koje treba uzeti u obzir. Kripto manipulacija je negativna pojava u kojoj se namjerno manipulira vrijednošću kriptovaluta radi financijske koristi. Meme coins su kriptovalute temeljene na zabavnom sadržaju, a play-to-earn koncept omogućuje korisnicima da zarade kriptovalute sudjelujući u igrama.
Quantum computers pose a significant threat to blockchain technology's security, which heavily relies on public-key cryptography and hash functions. The cryptographic algorithms used in blockchains, based on large odd prime numbers and discrete logarithms, can be easily compromised by quantum computing algorithms like Shor's algorithm and its future qubit variations. This survey paper comprehensively examines the impact of quantum computers on blockchain security and explores potential mitigation strategies. We begin by surveying the existing literature on blockchains and quantum computing, providing insights into the current state of research. We then present an overview of blockchain, highlighting its key components and functionalities. We delve into the preliminaries and key definitions of quantum computing, establishing a foundation for understanding the implications on blockchain security. The application of blockchains in cybersecurity is explored, considering their strengths and vulnerabilities in light of evolving quantum computing capabilities. The survey focuses on the quantum security of blockchain's fundamental building blocks, including digital signatures, hash functions, consensus algorithms, and smart contracts. We analyze the vulnerabilities introduced by quantum computers and discuss potential countermeasures and enhancements to ensure the integrity and confidentiality of blockchain systems. Furthermore, we investigate the quantum attack surface of blockchains, identifying potential avenues for exploiting quantum computing to strengthen existing attacks. We emphasize the need for developing quantum-resistant defenses and explore solutions for mitigating the threat of quantum computers to blockchains, including the adoption of quantum and post-quantum blockchain architectures. By examining vulnerabilities and discussing mitigation strategies, we aim to guide researchers, practitioners, and policymakers in developing robust and secure blockchain systems capable of withstanding advancements in quantum computing technology.
For more than a decade, as the number and value of cryptocurrencies exploded, more and more investors flocked to the cryptocurrency market with the expectation of positive returns. The price of cryptocurrencies, on the other hand, is extremely volatile. As a result, there is a great need to develop an accurate price prediction model to assist investors in making decisions and profit. This paper focuses on developing an LSTM-based prediction model for Bitcoin, Ethereum, EOS, and Solana cryptocurrency price prediction and calculating their RMSE and MAPE. Furthermore, four models are compared using this calculated MAPE. Based on the comparison results, the impact of cryptocurrency volatility, liquidity, and technology level on the accuracy of the LSTM prediction model is also examined. The paper concludes that the LSTM model can predict the price of Bitcoin more accurately because Bitcoin has the least volatility, the most liquidity and uses the oldest but most secure consensus mechanism.