Selfish Mining Research Articles

Asymmetric variable depth learning automaton and its application in defending against selfish mining attacks on bitcoin

Learning Automaton (LA) theory, a branch of reinforcement learning, initially began with the Fixed Structure Learning Automaton (FSLA) family and was later expanded to include the Variable Structure Learning Automaton (VSLA) family. Tuning the depth of FSLA in complex environments has long been a challenging task, significantly limiting the ability to effectively navigate the exploration-exploitation dilemma. No solution has been found for this open problem yet. This study addresses this issue by introducing a novel hybrid learning automaton model called Asymmetric Variable Depth Hybrid LA (AVDHLA). The AVDHLA model intelligently learns the depth of fixed structure LA in an autonomous manner by combining LKN,K from FSLA class and Variable Action Set LA (VASLA) from VSLA class. Computer simulations are conducted to validate the proposed model in diverse environments, including both stationary and non-stationary (Markovian switching and State-dependent) scenarios. Performance evaluation is based on predefined metrics, such as total number of rewards (TNR) and action switching (TNAS). Statistical tests indicate that across both stationary and non-stationary environments, AVDHLA consistently outperforms LKN,K in terms of TNR and TNAS across the majority of experiments. Moreover, the AVDHLA model is applied in two key applications. Firstly, it is used to defend against the selfish mining attack in Bitcoin and is compared with the well-known tie-breaking mechanism. Simulation results consistently demonstrate that our proposed method increases the threshold for successful selfish mining attacks from 25% to 40%. Secondly, the AVDHLA model has been applied to develop a novel learning automaton-based recommendation system. The results demonstrate the superiority of the proposed method in terms of the Click-Through Rate (CTR) and Precision compared to previous approaches.

Enhancing Electronic Agriculture Data Security with a Blockchain-Based Search Method and E-Signatures

The production of digital signatures with blockchain constitutes a prerequisite for the security of electronic agriculture applications (EAA), such as the Internet of Things (IoT). To prevent irresponsibility within the blockchain, attackers regularly attempt to manipulate or intercept data stored or sent via EAA-IoT. Additionally, cybersecurity has not received much attention recently because IoT applications are still relatively new. As a result, the protection of EAAs against security threats remains insufficient. Moreover, the security protocols used in contemporary research are still insufficient to thwart a wide range of threats. For these security issues, first, this study proposes a security system to combine consortium blockchain blocks with Edwards25519 (Ed25519) signatures to stop block data tampering in the IoT. Second, the proposed study leverages an artificial bee colonizer (ABC) approach to preserve the unpredictable nature of Ed25519 signatures while identifying the optimal solution and optimizing various complex challenges. Advanced deep learning (ADL) technology is used as a model to track and evaluate objects in the optimizer system. We tested our system in terms of security measures and performance overhead. Tests conducted on the proposed system have shown that it can prevent the most destructive applications, such as obfuscation, selfish mining, block blocking, block ignoring, blind blocking, and heuristic attacks, and that our system fends off these attacks through the use of the test of the Scyther tool. Additionally, the system measures performance parameters, including a scalability of 99.56%, an entropy of 60.99 Mbps, and a network throughput rate of 200,000.0 m/s, which reflects the acceptability of the proposed system over existing security systems.

AHEAD: A Novel Technique Combining Anti-Adversarial Hierarchical Ensemble Learning with Multi-Layer Multi-Anomaly Detection for Blockchain Systems

Blockchain technology has impacted various sectors and is transforming them through its decentralized, immutable, transparent, smart contracts (automatically executing digital agreements) and traceable attributes. Due to the adoption of blockchain technology in versatile applications, millions of transactions take place globally. These transactions are no exception to adversarial attacks which include data tampering, double spending, data corruption, Sybil attacks, eclipse attacks, DDoS attacks, P2P network partitioning, delay attacks, selfish mining, bribery, fake transactions, fake wallets or phishing, false advertising, malicious smart contracts, and initial coin offering scams. These adversarial attacks result in operational, financial, and reputational losses. Although numerous studies have proposed different blockchain anomaly detection mechanisms, challenges persist. These include detecting anomalies in just a single layer instead of multiple layers, targeting a single anomaly instead of multiple, not encountering adversarial machine learning attacks (for example, poisoning, evasion, and model extraction attacks), and inadequate handling of complex transactional data. The proposed AHEAD model solves the above problems by providing the following: (i) data aggregation transformation to detect transactional and user anomalies at the data and network layers of the blockchain, respectively, (ii) a Three-Layer Hierarchical Ensemble Learning Model (HELM) incorporating stratified random sampling to add resilience against adversarial attacks, and (iii) an advanced preprocessing technique with hybrid feature selection to handle complex transactional data. The performance analysis of the proposed AHEAD model shows that it achieves higher anti-adversarial resistance and detects multiple anomalies at the data and network layers. A comparison of the proposed AHEAD model with other state-of-the-art models shows that it achieves 98.85% accuracy against anomaly detection on data and network layers targeting transaction and user anomalies, along with 95.97% accuracy against adversarial machine learning attacks, which surpassed other models.

BA-flag: a self-prevention mechanism of selfish mining attacks in blockchain technology

BA-flag: a self-prevention mechanism of selfish mining attacks in blockchain technology

Efficient Algorithm for Proportional Lumpability and Its Application to Selfish Mining in Public Blockchains

This paper explores the concept of proportional lumpability as an extension of the original definition of lumpability, addressing the challenges posed by the state space explosion problem in computing performance indices for large stochastic models. Lumpability traditionally relies on state aggregation techniques and is applicable to Markov chains demonstrating structural regularity. Proportional lumpability extends this idea, proposing that the transition rates of a Markov chain can be modified by certain factors, resulting in a lumpable new Markov chain. This concept facilitates the derivation of precise performance indices for the original process. This paper establishes the well-defined nature of the problem of computing the coarsest proportional lumpability that refines a given initial partition, ensuring a unique solution exists. Additionally, a polynomial time algorithm is introduced to solve this problem, offering valuable insights into both the concept of proportional lumpability and the broader realm of partition refinement techniques. The effectiveness of proportional lumpability is demonstrated through a case study that consists of designing a model to investigate selfish mining behaviors on public blockchains. This research contributes to a better understanding of efficient approaches for handling large stochastic models and highlights the practical applicability of proportional lumpability in deriving exact performance indices.

Selfish mining attack in blockchain: a systematic literature review

Selfish mining attack in blockchain: a systematic literature review

Statistical detection of selfish mining in proof-of-work blockchain systems

The core of many cryptocurrencies is the decentralised validation network operating on proof-of-work technology. In these systems, validation is done by so-called miners who can digitally sign blocks once they solve a computationally-hard problem. Conventional wisdom generally considers this protocol as secure and stable as miners are incentivised to follow the behaviour of the majority. However, whether some strategic mining behaviours occur in practice is still a major concern. In this paper we target this question by focusing on a security threat: a selfish mining attack in which malicious miners deviate from protocol by not immediately revealing their newly mined blocks. We propose a statistical test to analyse each miner’s behaviour in five popular cryptocurrencies: Bitcoin, Litecoin, Monacoin, Ethereum and Bitcoin Cash. Our method is based on the realisation that selfish mining behaviour will cause identifiable anomalies in the statistics of miner’s successive blocks discovery. Secondly, we apply heuristics-based address clustering to improve the detectability of this kind of behaviour. We find a marked presence of abnormal miners in Monacoin and Bitcoin Cash, and, to a lesser extent, in Ethereum. Finally, we extend our method to detect coordinated selfish mining attacks, finding mining cartels in Monacoin where miners might secretly share information about newly mined blocks in advance. Our analysis contributes to the research on security in cryptocurrency systems by providing the first empirical evidence that the aforementioned strategic mining behaviours do take place in practice.

BitTrace

Bitcoin is a digital currency system built on the foundation of fairness. However, some malicious miners, driven by their own interests, employ unfair tactics such as selfish mining to compete, which disregard the legitimate miners' investments in computational power and energy consumption. In order to assess the efficiency of the Bitcoin system in real-time and promptly detect malicious miners in the network, this paper proposes a data collection framework called BitTrace, which addresses the issues of low efficiency, lack of timeliness, and data loss in traditional data collection frameworks. BitTrace enables real-time collection and analysis of the blockchain formation process, storing it as structured data. Furthermore, the paper discusses factors that influence the efficiency of data collection and proposes a topological control scheme based on the DPC algorithm to enhance the integrity and efficiency of data collection. Researchers can explore various research areas and applications, such as selfish mining detection and legitimate mining strategy research.

A Denial-of-Service Attack Based on Selfish Mining and Sybil Attack in Blockchain Systems

A Denial-of-Service Attack Based on Selfish Mining and Sybil Attack in Blockchain Systems

Hedera: A Permissionless and Scalable Hybrid Blockchain Consensus Algorithm in Multiaccess Edge Computing for IoT

Abstract-Multi-access edge computing network (MEC), as one of the key infrastructures of IoT, provides cloud computing capabilities at the edge of radio access network (RAN) by integrating telecommunication and IT services. Integrating blockchain into the MEC network can provide users with secure, private, and traceable edge computing services at the near end, thereby improving IoT security, privacy, and automated use of resources. Due to some characteristics of the MEC network, there are still some challenges to integrate blockchain and edge computing into one system, especially the consensus algorithm of blockchain. The resources of edge computing nodes are limited, and the scale of the network is constantly expanding. Therefore, the blockchain consensus algorithm for the MEC network should occupy as little computing resources as possible, be green, and be permissionless. This paper proposes a permissionless and scalable consensus algorithm "Hedera" for multi-access edge computing network, which has the advantages of permissionless, security, decentralization, scalability, and greenness. The Hedera consensus algorithm is a hybrid blockchain consensus algorithm that combines the permissionless proof-of-capacity algorithm and the permissioned asynchronous Byzantine algorithm. This paper tests the fairness, throughput, scalability, latency, and resource consumption of the algorithm by developing and deploying a prototype system. The experimental results show that the Hedera algorithm proposed in this paper is fair, the throughput reaches 13986.3TPS, and the resource consumption is much lower than the PoW consensus. By analyzing its security and liveness, it can resist Sybil attack, Nothing-at-stake attack, selfish mining attack, message hijacking attack, and has good liveness.

FORTIS: Selfish Mining Mitigation by (FOR)geable (TI)me(S)tamps

The selfish mining (SM) attack of Eyal and Sirer allows a rational mining pool with a hash power (α) much less than 50% of the whole Bitcoin network to steal from the fair shares of honest miners. This attack has been studied extensively in various settings in order for its optimization and mitigation. In this context, Heilman proposes a defense “Freshness Preferred”, based on timestamps, which are issued routinely by a timestamp authority. In contrast, we consider the case where timestamps are generated by no authority; instead every miner includes the current time into a block freely. However, due to two attacks that we discover, this turns out to be a non-trivial task. These attacks are Oracle mining , which works by cleverly setting the timestamp to future, and Bold mining , which works by generating an alternative chain starting from a previous block. Unfortunately, these attacks are hard to analyze and optimize, and to our knowledge, the available tools fail to help us for this task. To ease this, we come up with generalized formulas for revenue and profitability of SM attacks. Our analyses show that the use of timestamps could be promising for selfish mining mitigation. Nevertheless, Freshness Preferred in its current form is quite vulnerable, as any rational miner with α > 0 can directly benefit from our attacks. To cope with this problem, we propose a novel SM mitigation algorithm Fortis without an authority, which protects the honest miners’ shares against any attacker with α < 27.0 against all the known SM-type attacks. By building upon the blockchain simulator BlockSim, we simulate our Oracle and Bold mining attacks against Freshness Preferred and Fortis . Simulation results also demonstrate the effectiveness of these attacks against the former and their ineffectiveness against the latter.

Analysis of hybrid attack and defense based on block withholding strategy

As the most influential digital cryptocurrency in the world, Bitcoin is widely recognized for its Proof of Work (PoW) consensus mechanism. However, the blockchain system based on PoW has also encountered many attacks and threats. Most of the current research focuses on analyzing the impact of a single attack on revenue while ignoring the effect of hybrid attacks on the blockchain system and attack revenue. We aim to study the impact of hybrid attacks and defensive measures. In this paper, we extend the block withholding attack model based on the existing selfish mining model and add bribery attacks to the attack model. We compare the benefits under different computing capabilities and theoretically analyze the defense model to prevent attacks. Finally, we compare the advantages of attackers under the attack model and the defense model. Our experiments show that different attack combinations can have negative effects, and the defense model with hidden target values can solve block withholding attacks well.

Crystal: Enhancing Blockchain Mining Transparency With Quorum Certificate

Researchers have discovered a series of theoretical attacks against Bitcoin's Nakamoto consensus; the most damaging ones are selfish mining, double-spending, and consistency delay attacks. These attacks have one common cause: block withholding. This paper proposes Crystal, which leverages quorum certificates to resist block withholding misbehavior. Crystal continuously elects committees from miners and requires each block to have a quorum certificate, i.e., a set of signatures issued by members of its committee. Consequently, an attacker has to publish its blocks to obtain quorum certificates, rendering block withholding impossible. To build Crystal, we design a novel two-round committee election in a Sybil-resistant, unpredictable and non-interactive way, and a reward mechanism to incentivize miners to follow the protocol. Our analysis and evaluations show that Crystal can significantly mitigate selfish mining and double-spending attacks. For example, in Bitcoin, an attacker with 30% of the total computation power will succeed in double-spending attacks with a probability of 15.6% to break the 6-confirmation rule; however, in Crystal, the success probability for the same attacker falls to 0.62%. We provide formal end-to-end safety proofs for Crystal, ensuring no unknown attacks will be introduced. To the best of our knowledge, Crystal is the first protocol that prevents selfish mining and double-spending attacks while providing safety proof.

Multi-stage Proof-of-Works: Properties and vulnerabilities

Since its appearance in 2008, Bitcoin has attracted considerable attention. So far, it has been the most successful cryptocurrency, with the highest market capitalization. Nevertheless, due to the method it uses to append new transactions and blocks to the blockchain, based on a Proof-of-Work, Bitcoin suffers from poor scalability, which strongly limits the number of transactions per second and, hence, its adoption as a global payment layer for everyday uses. In this paper we analyze some recent proposals to address this issue. In particular, we focus our attention on permissionless blockchain protocols, whose distributed consensus algorithm lies on a Proof-of-Work composed of k>1 sequential hash-puzzles, instead of a single one. Such protocols are referred to as multi-stage Proof-of-Works. We consider a simplified scenario, commonly used in the blockchain literature, in which the number of miners, their hashing powers, and the difficulty values of the hash-puzzles are constant over time. Our contribution is threefold. Firstly, we derive a closed-form expression for the mining probability of a miner, that is, the probability that the miner completes the Proof-of-Work of the next block to be added to the blockchain, before any other miner does. Secondly, we show that in multi-stage Proof-of-Works the mining probability might not be strictly related to the miner hashing power. This feature could be exploited by a smart miner, and could open up potential fairness and decentralization issues in mining. Finally, we focus on a more restricted scenario and present two attacks, which can be applied successfully against multi-stage Proof-of-Works: a Selfish Mining attack and a Selfish Stage-Withholding attack. We show that both are effective, and we point out that Selfish Stage-Withholding can be seen as a complementary strategy to Selfish Mining, which in some cases increases the selfish miner profitability in the Selfish Mining attack.

Q‐Learning model for selfish miners with optional stopping theorem for honest miners

AbstractBitcoin, the most popular cryptocurrency used in the blockchain, has miners join mining pools and get rewarded for the proportion of hash rate they have contributed to the mining pool. This work proposes the prediction of the relativegain of the miners by machine learning and deep learning models, the miners' selection of higher relativegain by the Q‐learning model, and an optional stopping theorem for honest miners in the presence of selfish mining attacks. Relativegain is the ratio of the number of blocks mined by selfish miners in the main canonical chain to the blocks of other miners. A Q‐learning agent with ‐greedy value iteration, which seeks to increase the relativegain for the selfish miners, that takes into account all the other quintessential parameters, including the hash rate of miners, time warp, the height of the blockchain, the number of times the blockchain was reorganized, and the adjustment of the timestamp of the block, is implemented. Next, the ruin of the honest miners and the optional stopping theorem are analyzed so that the honest miners can quit the mining process before their complete ruin. We obtain a low mean square error of 0.0032 and a mean absolute error of 0.0464 in our deep learning model. Our Q‐learning model exhibits a linearly increasing curve, which denotes the increase in the relativegain caused by the selection of the action of performing the reorganization attack.

Rethinking selfish mining under pooled mining

Bitcoin’s core security requires honest participants to control at least 51% of the total hash power. However, it has been shown that several techniques can exploit the fair mining in the Bitcoin network. This study focuses on selfish mining, which is based on the idea, ”keeping blocks secret.” Herein, we analyze selfish mining regarding competition between mining pools. We emphasize that mining-related information is shared between mining pools and participants. Based on shared information about selfish mining, we have developed an effective and practical counter strategy.

Security Attacks on E-Voting System Using Blockchain

Electronic voting has become popular in democratic countries, and thus the cyber security of this system is demanded. In this paper, some attacks were made on a proposed electronic election model based on blockchain technology, where the impact of each attack (Sybil, DDoS, Eclipse, Selfish mining, 51% attack) was calculated, and the time in which it achieved 51% of the attack was calculated. In this study, we investigate of Blockchain technology’s attack surface, focusing on general blockchains. The following factors show how these attacks have an impact on the proposed model: 1) The cryptographic architecture of the Blockchain. 2) The distributed architecture of systems using Blockchain. 3) The Blockchain application context. For each of these factors, we identify several attacks, including selfish mining, 51% attack, sybil attacks, eclipse attacks, distributed denial-of-service (DDos) attacks, consensus delay (due to selfish behavior or distributed denial-of-service attacks), blockchain forks, orphan blocks, block swallowing, wallet theft, smart contract attacks, and privacy attacks.

Security Attacks on E-Voting System Using Blockchain

Electronic voting has become popular in democratic countries, and thus the cyber security of this system is demanded. In this paper, some attacks were made on a proposed electronic election model based on blockchain technology, where the impact of each attack (Sybil, DDoS, Eclipse, Selfish mining, 51% attack) was calculated, and the time in which it achieved 51% of the attack was calculated. In this study, we investigate of Blockchain technology’s attack surface, focusing on general blockchains. The following factors show how these attacks have an impact on the proposed model: 1) The cryptographic architecture of the Blockchain. 2) The distributed architecture of systems using Blockchain. 3) The Blockchain application context. For each of these factors, we identify several attacks, including selfish mining, 51% attack, sybil attacks, eclipse attacks, distributed denial-of-service (DDos) attacks, consensus delay (due to selfish behavior or distributed denial-of-service attacks), blockchain forks, orphan blocks, block swallowing, wallet theft, smart contract attacks, and privacy attacks.

A General Quantitative Analysis Framework for Attacks in Blockchain

Decentralized cryptocurrency systems have become primary targets for attackers due to substantial profit gain and economic rewards. A number of attack models have been proposed during last few years. However, the evaluation and comparison of those attack models remain problematic due to the lack of systematic framework to analyze them. In this work, we propose a general quantitative analysis framework for attack models in the network and consensus layer of blockchain. We identify the problem statement and evolution process. And we show how to apply our general framework in previous attacks such as selfish mining and bribery attack. We also explained that the framework is suitable for other attacks in blockchain. For further exploration, we simulate the success rate and benefits of different attacks through experiments. We provide several defensive strategies, and study how these strategies against previous attack models.

Tree Representation, Growth Rate of Blockchain and Reward Allocation in Ethereum With Multiple Mining Pools

It is interesting but difficult and challenging to study Ethereum with multiple mining pools. One of the main difficulties comes from not only how to represent such a general tree with multiple block branches (or sub-chains) related to the multiple mining pools, but also how to analyze a multi-dimensional stochastic system due to the mining competition among the multiple mining pools. In this paper, we first set up a mathematical representation for the tree with multiple block branches. Then we provide a block classification of Ethereum: Regular blocks (in the main chain), orphan blocks, uncle blocks, stale blocks, and nephew blocks, and give some key ratios and probabilities of generating the different types of blocks by applying the law of large numbers. Based on this, we further discuss the growth rate of blockchain and the reward allocation among the multiple mining pools through applying the renewal reward theorem. Finally, we use some simulation experiments to verify our theoretical results, and show that the approximate computation approaches developed, such as the key ratios and probabilities, the long-term growth rate of blockchain, and the long-term reward allocation (rate) among the multiple mining pools, can have a faster convergence. Therefore, we provide a powerful tool for observing and understanding the influence of the selfish mining attacks on the performance of Ethereum with multiple mining pools. We believe that the methodology and results developed in this paper will shed light on the study of Ethereum with multiple mining pools, such that a series of promising research can be inspired potentially.