Zloupotrebe i napadi na blokčejn sisteme

The design of the difficulty adjustment algorithm (DAA) of the Bitcoin system is vulnerable as it dismisses miners' strategic responses to policy changes. We develop an economic model of the Proof-of-Work based blockchain system. Our model allows miners to pause operation when the expected reward is below the shutdown point. Hence, the supply of aggregate hash power can be elastic in the cryptocurrency price and the difficulty target of the mining puzzle. We prove that, when the hash supply is elastic, the Bitcoin DAA fails to adjust the block arrival rate to the targeted level. In contrast, the DAA of another blockchain system, Bitcoin Cash, is shown to be stable even when the cryptocurrency price is volatile and the supply of hash power is highly elastic. We also provide empirical evidence and simulation results supporting the model's prediction. Our results indicate that the current Bitcoin system might collapse if a sharp price reduction lowers the reward for mining denominated in fiat money. While this crisis can be prevented through the upgrading of DAA, we also discuss that a large fraction of miners may disagree with upgrading the DAA because they can obtain a larger expected profit from an unstable DAA.

Introduction: Embezzlement of customer funds is a form of criminal act of corruption that harms many parties, including customers who lose their money, financial institutions whose reputation is damaged, and the public who have lost confidence in the financial system. Effective prevention and law enforcement efforts are needed to eradicate this practice.Purposes of the Research: This study aims to describe and analyze the phenomenon of embezzlement of customer funds as a form of corruption in Indonesia. Embezzlement of customer funds is a crime that harms society and affects trust in financial institutions.Methods of the Research: The research method used is literature study which involves analysis of literature, reports, court cases, and laws related to corruption and finance in Indonesia. The data collected is analyzed qualitatively to provide an in-depth understanding of embezzlement of customer funds.Results of the Research: The research results show that embezzlement of customer funds occurs through various fraudulent schemes carried out by parties who have access to customer funds, such as bank employees, investment managers, or stockbrokers. These actions often involve the use of illegally obtained customer information or abuse of authority. Several factors influence the embezzlement of customer funds in Indonesia, including weak internal and external control systems, low integrity of individuals involved in the financial industry, and lack of adequate policies and regulations. Therefore, improvements are needed in financial governance, increased supervision, employee training, and strict law enforcement to prevent and take action against perpetrators of embezzlement of customer funds. The conclusion of this study is that embezzlement of customer funds is a criminal act of corruption that needs serious attention from the government, financial institutions and the public. With the right steps, such as strengthening regulations and increasing awareness of the risk of embezzlement of funds, it is expected to reduce incidents of embezzlement of customer funds and maintain public confidence in the financial system in Indonesia

A survey on blockchain systems: Attacks, defenses, and privacy preservation

A Lucas Critique to the Difficulty Adjustment Algorithm of the Bitcoin System

Cryptocurrency is a digital asset designed by cryptography, such as Secure Hash Algorithm 2 (SHA-2) and Message Digest 5 (MD5). Cryptocurrency uses Blockchain technology to ensure security, transparency, ease of locating, and unchangeability. This makes cryptocurrency very popular in many sectors, especially in the financial industry. Although, the uncertainty and the dynamic change of cryptocurrency price make the risk for investment in this digital asset high. This is the reason why studies about cryptocurrency price prediction became popular globally. This study intended to predict cryptocurrency prices using hybrid GRU LSTM than setting up the epoch to get the most accurate prediction model. The researcher would make a web-based application that can be used by the public, especially those involved in cryptocurrency investment. The result was a web-based application that could predict the price of cryptocurrency for the next few days, which had been validated using data from the previous 7 days, 14 days, 30 days, 60 days, and 90 days.

Purpose: Previous studies of FinTech and InsureTech focused on how the introduction of new technologies and systems would affect the existing financial industry. Nevertheless, there were no studies approached from a comprehensive perspective on the relationship with InsurTech, a concept derived from FinTech, and its impact on the financial and insurance industries in the future. This study aims to contribute to related industries and academic fields by predicting the impact, differences, and future prospects of the concept and introduction of FinTech and insurtech on the financial industry.
 Research design, data, and methodology: Discuss the concept and status of FinTech and insurance technology through qualitative methods such as existing prior research and academic thesis books, and understand what influence it has on the current financial industry, insurance industry, and consumers.
 Results: Through the impact of each area of FinTech and InsureTech on the financial business area of this study, FinTech and InsureTech were similar concepts but differences could be identified. FinTech and InsureTech are still positively affecting the financial industry and consumers, but they are expected to become innovators that can lead the financial industry in the future.
 Implications: This study explained the concept of FinTech and InsureTech, and examined the impact of newly introduced high-tech technologies on the financial industry. Through this, it was possible to see the positive impact of FinTech and insurtech on the business sector and the general public. This author hopes that FinTech and InsureTech will be the driving force for new creation through improvement and innovation in each field.

The purpose of this research is to identify how effective the determinants of the price changes in cryptocurrencies are and if they are predictable. The study addresses several independent variables that are in our consideration which may impact the prices the most. To obtain the results, panel data has been used to run fixed effects models. Then I treated them as time series data to run dynamic, distributed lags, and first-differencing regression models. Important political shocks and instabilities have been analyzed and interpreted in this paper. In the light of our findings we were able to comment on the complex relation between cryptocurrency prices and socio-political situations throughout the time range. The results address that cryptocurrency price changes are not predictable. It is hard to say what does affect the most prices. Internet search trends seem to have an impact but at the end it has been found that the correlation is not strong. From an economist's viewpoint, investing in cryptocurrencies without analyzing price changes and news might be disastrous and we can call it basically gambling. Cryptocurrencies shouldn't be seen as a gambling medium and should be taken more seriously like an investment medium. In some specific occasions investing in cryptocurrencies may lead lucrative income.

The growing interest in blockchain and cryptocurrencies is driving the research and use of attacks that can target different parts of the system. For example, some attacks target the network or the system itself through sharding, DDOS attacks, or wormhole attacks. Some other attacks, such as the 51% attack, aim to acquire a majority share of the production capacity in the system. With these powers, an attacker or group of attackers can rewrite the blockchain at will, perform double-spend attacks, or censor any entity they choose. Other attack types combine multiple attack vectors into one, building a competitive chain using unknown participants that were previously isolated from the network via an obfuscation attack. Other attack methods also exist through exploitation of smart contracts/decentralized applications bugs. Most decentralized applications are susceptible to favouritism attacks, where an attacker unfairly exploits information related to events that have not yet been recorded on the blockchain. Provided list of attack vectors, despite its inexhaustibility, is aimed to emphasize the fact that blockchain systems have vulnerabilities. The presence of the latter indicates the need to promote detailed information about blockchain security mechanisms through their simulation. This article is designed to explore the possibilities of modeling the most common types of attacks on the blockchain by using an organization-oriented modeling method, as well as to show in practice the method of modeling regulatory mechanisms through the reproduction of special meta-agents – regulatory agents – responsible for the coordinated operation of the entire system, and in particular contributing to resistance attacks on the network by checking the proposed transactions for compliance with the rules laid down in them, which results in endorsement or rejection of the former. Further research may involve modeling the motivation of system agents in the event of rejection of transactions by regulatory agents.

Purpose : This study examined the relationship between social media engagement and cryptocurrency prices, considering information asymmetry and lack of historic data in the cryptocurrency market.Approach : The study analyzed the correlation between social media engagement and prices of major cryptocurrencies using a graphical approach. Data on daily social media reach, mentions, engagements, and cryptocurrency price changes were collected for three sets of 16 days each.Findings : The study showed diverse relationships between social media engagement and cryptocurrency prices. Bitcoin exhibited a strong positive correlation, Altcoins showed a moderately positive correlation, and Stable Coins demonstrated a weak negative correlation. No significant correlation was observed for utility tokens and security tokens.Practical Implications : The study suggested that entrepreneurs should focus on creating engaging content to boost cryptocurrency prices instead of relying solely on social media engagement. Retail investors should exercise caution when using social media for investment decisions, as it may not accurately reflect a cryptocurrency’s true value or potential.Originality/Value : This study contributed to the understanding of the complex relationship between social media engagement and cryptocurrency prices. The study highlighted the need for thorough research and consideration of multiple factors when making investment decisions in the cryptocurrency market.Conclusion and Implications : The study’s implications extend to entrepreneurs and retail investors in the cryptocurrency market. Entrepreneurs should consider social media engagement as one factor among many to increase cryptocurrency prices and be aware of its limitations. Retail investors should conduct thorough research and consider multiple factors, as relying solely on social media engagement may not accurately assess a cryptocurrency’s potential.

In the blockchain system, mining pools are popular for miners to work collectively and obtain more revenue. Nowadays, there are consensus attacks that threaten the efficiency and security of mining pools. As a new type of consensus attack, the Fork After Withholding (FAW) attack can cause huge economic losses to mining pools. Currently, there are a few evaluation tools for FAW attacks, but it is still difficult to evaluate the FAW attack protection capability of target mining pools. To address the above problem, this paper proposes a novel evaluation framework for FAW attack protection of the target mining pools in blockchain systems. In this framework, we establish the revenue model for mining pools, including honest consensus revenue, block withholding revenue, successful fork revenue, and consensus cost. We also establish the revenue functions of target mining pools and other mining pools, respectively. In particular, we propose an efficient computing power allocation optimization algorithm (CPAOA) for FAW attacks against multiple target mining pools. We propose a model-solving algorithm based on improved Aquila optimization by improving the selection mechanism in different optimization stages, which can increase the convergence speed of the model solution and help find the optimal solution in computing power allocation. Furthermore, to greatly reduce the possibility of falling into local optimal solutions, we propose a solution update mechanism that combines the idea of scout bees in an artificial bee colony optimization algorithm and the constraint of allocating computing power. The experimental results show that the framework can effectively evaluate the revenue of various mining pools. CPAOA can quickly and accurately allocate the computing power of FAW attacks according to the computing power of the target mining pool. Thus, the proposed evaluation framework can effectively help evaluate the FAW attack protection capability of multiple target mining pools and ensure the security of the blockchain system.

Blockchain technology, the backbone of digital cryptocurrencies, has rapidly ascended as a pivotal tool in modern commerce due to its decentralized, immutable nature. It offers fresh, innovative avenues for overcoming trust issues inherent in traditional trading systems. Yet, the unique traits that make blockchain advantageous also render it vulnerable. Cybercriminals are ceaselessly innovating, devising new tactics to exploit these vulnerabilities and resulting in a surge of security incidents that have led to substantial economic losses. The increasing frequency and sophistication of these attacks jeopardize the integrity and stability of blockchain networks. This paper offers a comprehensive study of blockchain system architecture, the principles underlying various attack methods, and viable defense strategies, all organized within a hierarchical framework. Initially, the paper categorizes blockchain attacks according to the hierarchy of blockchain systems, providing a detailed exploration of the characteristics and principles behind these attacks at each level. Next, the paper summarizes existing countermeasures and proposes effective new strategies for bolstering blockchain security. The paper concludes with a recap of its key findings and outlines the landscape for future research in blockchain security.

Blockchain is a disruptive technology that enables disparate users to share their information in blocks trustworthily without a centralized entity. One fundamental problem is how to stable the block interval. To address this problem, our method is: 1. predict the computing power (i.e., hashrate) of a blockchain system by the cryptocurrency price; 2. stable the interval according to the predicted power. This paper focuses on the prediction of the global computing power. In our prediction, we adopt a LSTM-based regression algorithm to handle the hysteresis of computing power changes in response to the price changes. Taking the Bitcoin system as an example, we run extensive experiments that verify that our prediction algorithm is very accurate.

The adoption of emerging technologies in the finance industry, such as blockchain, promises to provide new perspectives on data security and business processes. This advanced innovation paves the way for unprecedented financial and organizational optimization, transforming not only processes and structures, but also fundamental paradigms of trust and knowledge. The financial industry faces numerous challenges in combating fraud and embezzlement, due to the complexity and scale of its operations. Therefore, the infrastructure provided by the blockchain system helps reduce costs related to intermediaries, enhances security, and improves trust between stakeholders, thanks to the principle of decentralization and the immutability of its ledger. Furthermore, this technological revolution offers financial actors the ability to create new services and solutions in order to meet the needs of several business activities, especially those with complex supply chains. A perfect use of it, will certainly develop the standards of this industry and stimulate innovative economic growth. Although the blockchain offers substantial opportunities for the financial sector, its potential can only be fully achieved with adequate regulatory support, as a way to steer the system toward a more digital and inclusive economy. After a presentation of blockchain technology and its mode of operation, we will be focusing on its potential in finance and how it might provide creative accounting and financial management solutions.

With the development of blockchain technology, the increasing safety accidents result in huge economic losses in blockchain systems. Delegated Proof of Stake (DPoS) selects the witness nodes to produce blocks by voting, leading to the quick confirmation of transactions. As one of the widely used consensus mechanisms in public blockchain, DPoS is still threatened by attacks. In this paper, an analysis method for collusion attacks of DPoS consensus mechanism is proposed. Meanwhile, we analyze the behavioral motivations of malicious nodes and detect the attacks that exist in the voting process of DPoS. First, the coalitional game is the basic form of cooperative game, which can be used to analyze the structure, strategy and benefits of cooperative game. We build a coalitional game model to analyze motivations of DPoS nodes that launched collusion attacks. And then we use the Shapley-Shubik power index and Banzhaf power index in weighted voting games of DPoS, which calculated different values that DPoS suffered attacks during the voting phase. Experimental results show that collusion attacks in DPoS can be effectively detected by this method. In addition, the analysis results can further contribute to the security of the DPoS blockchain system.KeywordsBlockchainConsensus mechanismDPoSCollusion attackCoalitional gameWeighted voting game

With the widespread deployment of Internet of Things (IoT) devices, hackers can use IoT devices to launch large-scale distributed denial of service (DDoS) attacks, which bring great harm to the Internet. However, how to defend against these attacks remains to be an open challenge. In this paper, we propose a novel prevention method for IoT DDoS attacks based on blockchain and obfuscation of IP addresses. Our observation is that IoT devices are usually resource-constrained and cannot support complicated cryptographic algorithms such as RSA. Based on the observation, we employ a novel authentication then communication mechanism for IoT DDoS attack prevention. In this mechanism, the attack targets' IP addresses are encrypted by a random security parameter. Clients need to be authenticated to obtain the random security parameter and decrypt the IP addresses. In particular, we propose to authenticate clients with public-key cryptography and a blockchain system. The complex authentication and IP address decryption operations disable IoT devices and thus block IoT DDoS attacks. The effectiveness of the proposed method is analyzed and validated by theoretical analysis and simulation experiments.