The increasing proliferation of user data is moving the world from the era of 'big data' to a new era of shared data, and some are considering data as a factor of production that is paving the way for a new business economy. In this paper, we propose a solution that uses blockchain technology as a platform for online neighborhood watch using a form of data cooperative among individuals or organizations in the sharing of data through a peer-to-peer mechanism. We prove the concept by implementing a distributed phishing data sharing system that will maintain a community ledger of reported phishing activities with a consensus-based approval of the phishing transaction and a novel reputation scoring system thereby adding reliability to the system and effectively tackling the phishing problem. The data cooperative provides a way for timely multi-party sharing of phishing data among anti-phishing organizations and users of the internet eliminating the current approach of each organization maintaining its database. Our results show that blockchain is effective in complementing the existing methods of phishing detection and serves as a platform for sharing phishing data with respect to scalability, cost, and memory consumption. Also, our results further show that transaction times on the Ropsten test net follow a Gamma distribution. Our approach can be extrapolated to other data sharing systems like medical data, spam calls, discussion forums, etc.
Thousands of new cryptocurrencies have been introduced in recent years. Most are introduced with a so-called "whitepaper" containing a mix of technical documentation, legal boilerplate and marketing material. Notably, many proposed currencies reuse text from previous established cryptocurrencies. We analyze the whitepapers from 1 260 actively traded cryptocurrencies and 2 039 ICOs. We develop two measures of similarity. Moderately similar papers reuse text in a portion of the paper, often the legal disclaimers. By contrast, some highly similar whitepapers appear to copy most of the text. 4% of coin and 19% of ICO whitepapers are highly similar to those of traded coins. The fraction rises to 64% for coins and 67% for ICOs when we consider moderate text reuse.
Because Bitcoin P2P networking is permissionless by the application requirement, it is vulnerable against networking threats based on identity/credential manipulations such as Sybil and spoofing attacks. The current Bitcoin implementation keeps track of its peer's networking misbehaviors through ban score. In this paper, we investigate the security problems of the ban-score mechanism and discover that the ban score is not only ineffective against the Bitcoin Message-based DoS attacks but also vulnerable to a Defamation attack. In the Defamation attack, the network adversary can exploit the ban-score mechanism to defame innocent peers.
N. Suganthi, Gokul Sridharan, E Shravanth, K. Veena
Abstract: Democratic rule is based on safe and open electoral systems that can preserve their authenticity. The conventional voting processes are hampered significantly by electoral fraud and security breaches as well as centralized management and inefficiencies in auditability and accessibility. The issues of electoral systems reduce public trust along with lowering the credibility of election results. This paper introduces a new three-tier blockchain-based e-voting system designed to enhance voter privacy alongside system scalability and end-to-end verifiability to restore trust in electoral processes. Voter verification at Layer 1 (Identity Verification) combines Decentralized Identity (DID) with Zero-Knowledge Proofs (ZKP) and multimodal biometric techniques involving fingerprint scanning, facial recognition technology, and voice analysis. The system enables only the participation of valid voters while also protecting their private data and fulfilling different user needs. The system's Layer 2 (Vote Casting & Secure Storage) employs a hybrid consensus algorithm combining Byzantine Fault Tolerance (BFT) and Delegated Proof-of-Stake (dPoS) to store votes securely while reducing energy consumption. The system leverages Triple-Blind Signatures to provide complete voter anonymity by decoupling voter identities from their votes as well as any accompanying metadata. Lattice-based post-quantum cryptography is employed to encrypt votes which are distributed across sharded blockchain subnets for enhanced performance without sacrificing fault tolerance. The system accumulates votes via Merkle roots and verifies them via zk-SNARKs in Layer 3 (Result Processing & Transparency) that allows public observation without compromising voter privacy. A Live Audit Dashboard provides voters with the capability to check their vote in real time which facilitates transparent and accountable voting processes. The suggested system attains a secure and open electronic voting process using sophisticated cryptographic protocols in a decentralized setup compliant with international requirements while enabling digital democratic participation.
Blockchain technology's (BT) Ethereum Smart Contracts allows programmable transactions that involve the transfer of monetary assets among peers on a BT network independent of a central authorizing agency. Ethereum Smart Contracts are programs that are deployed as decentralized applications, having the building blocks of the blockchain consensus protocol. This technology enables consumers to make agreements in a transparent and conflict-free environment. However, the security vulnerabilities within these smart contracts are a potential threat to the applications and their consumers and have shown in the past to cause huge financial losses. In this paper, we propose a framework that combines static and dynamic analysis to detect Denial of Service (DoS) vulnerability due to an unexpected revert in Ethereum Smart Contracts. Our framework, SmartScan, statically scans smart contracts under test (SCUTs) to identify patterns that are potentially vulnerable in these SCUTs and then uses dynamic analysis to precisely confirm their exploitability of the DoS-Unexpected Revert vulnerability, thus achieving increased performance and more precise results. We evaluated SmartScan on a set of 500 smart contracts collected from the Etherscan. Our approach shows an improvement in precision and recall when compared to available state of the art techniques.
Nikolay Ivanov, Jianzhi Lou, Ting Chen, Jin Li · 5 authors
Ethereum holds multiple billions of U.S. dollars in the form of Ether cryptocurrency and ERC-20 tokens, with millions of deployed smart contracts algorithmically operating these funds. Unsurprisingly, the security of Ethereum smart contracts has been under rigorous scrutiny. In recent years, numerous defense tools have been developed to detect different types of smart contract code vulnerabilities. When opportunities for exploiting code vulnerabilities diminish, the attackers start resorting to social engineering attacks, which aim to influence humans -- often the weakest link in the system. The only known class of social engineering attacks in Ethereum are honeypots, which plant hidden traps for attackers attempting to exploit existing vulnerabilities, thereby targeting only a small population of potential victims. In this work, we explore the possibility and existence of new social engineering attacks beyond smart contract honeypots. We present two novel classes of Ethereum social engineering attacks - Address Manipulation and Homograph - and develop six zero-day social engineering attacks. To show how the attacks can be used in popular programming patterns, we conduct a case study of five popular smart contracts with combined market capitalization exceeding $29 billion, and integrate our attack patterns in their source codes without altering their existing functionality. Moreover, we show that these attacks remain dormant during the test phase but activate their malicious logic only at the final production deployment. We further analyze 85,656 open-source smart contracts, and discover that 1,027 of them can be used for the proposed social engineering attacks. We conduct a professional opinion survey with experts from seven smart contract auditing firms, corroborating that the exposed social engineering attacks bring a major threat to the smart contract systems.
Modern day businesses are largely dependent on digital technologies. People prefer viewing the reviews before making any decisions. It applies to all consumables like buying Electronic items, Clothing, Travel, Guest-House, Restaurant, Rental, Housing, Automobile, Cosmetics, Jewellery, Movies, etc. Online services like Mantra, Yelp, Amazon, Facebook, Google My Business, Trip Advisor offer great services to the customer. However, drawbacks of these systems are fake reviews, negative reviews and sometimes even tampering of the reviews given by the customers, which has a huge impact on the business leading to huge financial losses. Sometimes a competitor in the business might also influence the ratings being provided. The centralized storage of these reviews also leads to problems like tampering or manipulation of the data being stored. In this paper we propose an application in the restaurant industry that solves all these drawbacks by making use of the Ethereum blockchain. The food reviews given by the customers are stored as smart contracts in the blockchain, which can't be altered, thus guaranteeing the authenticity of the reviews. Validity of the reviews is ensured because it is difficult for the restaurants to delete or create new accounts to wipe away the bad reviews given. Blockchain is immutable so we ensure that the reviews are genuine and the system is trustable.
With the prosperous development of blockchain technologies in the past few years, some cybercrimes have emerged in the blockchain ecosystem, such as the phishing scams on Ethereum. To alleviate these security problems, a few anomaly detection frameworks were proposed. Specifically, previous studies usually model the transfer relationship between accounts in the blockchain ecosystem as a transaction network, where nodes represent accounts and edges represent the corresponding transaction records. Inspired by the adversarial attacks on graph data, we believe the robustness of existing detection frameworks still needs to be further verified even though they have achieved good performance. In this paper, a phishing detection framework based on feature learning and a phishing hidden framework based on inserting transaction records are proposed, respectively. Experimental results show the effectiveness of our phishing detection framework and the superiority of the phishing hidden strategies, which indicate that existing phishing detection frameworks are lack of robustness and still need further improvement against malicious attacks.
Bandwidth depletion Denial-of-Service (DoS) attack can impact the propagation of a mined block in the Bitcoin blockchain network. On Bitcoin Proof-of-Work (PoW) consensus several machines try to resolve an expensive cryptographic puzzle faster than anyone else and succeed to mine a valid block. Despite a DoS attack impedes one machine to propagate its mined block allowing it to become valid for most peers, there will be several other peers to resolve the puzzle in time, hence the blockchain will continue to grow. However, from the perspective of the owner of the attacked machine, this can be critical because it will not receive a mining reward. This chapter covers such an attack in the Lisk blockchain that utilizes the Delegated Proof of Stake (DPoS) consensus mechanism. A mitigation strategy was created based on two tools that I have created allowing a delegate account to be configured in more than one node, allowing to forge a block even when one of its nodes is under DoS attack. Also, the transaction flood DoS attack is explored, and a mitigation strategy was created for a specific sidechain in the Lisk ecosystem. The mitigation strategy identifies spam transactions and rejects them to be included on the Lisk nodes transaction pool, hence they will not be propagated into the blockchain. Towards the end, I evaluated scenarios and mitigation strategies created for each attack demonstrating solutions for several scenarios.
Xi He, Ketai He, Shenwen Lin, Jinglin Yang · 5 authors
Abstract Single heuristic method and incomplete heuristic conditions were difficult to cluster a large number of addresses comprehensively and accurately. Therefore, this paper analysed the associations between Bitcoin transactions and addresses and used six heuristic conditions to cluster addresses and entities. We proposed an improved change address detection algorithm and compared it with the original change address algorithm to prove the effectiveness of the improved algorithm. By adding conditional constraints, the identified change address was more accurate, and the convergence speed of the algorithm was accelerated. Our work presented the pseudo‐anonymity mechanism of the Bitcoin system, which could be used by the law enforcement agencies to track and crack down illegal transactions.
Blockchain like Bitcoin and Ethereum suffer from scalability issues. Sharding is one of the most promising and leading solutions to scale blockchain. The basic idea behind sharding is to divide the blockchain network into multiple committees, where each processing a separate set of transactions, rather than the entire network processes all transactions. In this paper, we propose a probabilistic approach to analyze the security of sharding-based blockchain protocols. Based on this approach, we investigate the threat of Sybil attacks in these protocols. The key contribution of our paper is a tractable probabilistic approach to accurately compute the failure probability that at least one committee fails and ultimately compute the probability of a successful attack. To show the effectiveness of our approach, we conduct a numerical and comparative analysis of the proposed approach with existing approaches.
Blockchain like Bitcoin and Ethereum suffer from scalability issues. Sharding\nis one of the most promising and leading solutions to scale blockchain. The\nbasic idea behind sharding is to divide the blockchain network into multiple\ncommittees, where each processing a separate set of transactions, rather than\nthe entire network processes all transactions. In this paper, we propose a\nprobabilistic approach to analyze the security of sharding-based blockchain\nprotocols. Based on this approach, we investigate the threat of Sybil attacks\nin these protocols. The key contribution of our paper is a tractable\nprobabilistic approach to accurately compute the failure probability that at\nleast one committee fails and ultimately compute the probability of a\nsuccessful attack. To show the effectiveness of our approach, we conduct a\nnumerical and comparative analysis of the proposed approach with existing\napproaches.\n
Lampis Alevizos, Vinh‐Thong Ta, Mahmoud Hashem Eiza
Abstract With the purpose of defending against lateral movement in today's borderless networks, zero trust architecture (ZTA) adoption is gaining momentum. With a full‐scale ZTA implementation, it is unlikely that adversaries will be able to spread through the network starting from a compromised endpoint. However, the already authenticated and authorized session of a compromised endpoint can be leveraged to carry out limited, though malicious, activities ultimately rendering the endpoints the Achilles heel of ZTA. To effectively detect such attacks, distributed collaborative intrusion detection systems with an attack scenario‐based approach have been developed. Nonetheless, advanced persistent threats have demonstrated their ability to bypass this approach with a high success ratio. As a result, adversaries can pass undetected or potentially alter the detection logging mechanisms to achieve a stealthy presence. Recently, blockchain technology has demonstrated solid use cases in the cyber security domain. In this paper, motivated by the convergence of ZTA and blockchain‐based intrusion detection and prevention, we examine how ZTA can be augmented onto endpoints. Namely, we perform a state‐of‐the‐art review of ZTA models, real‐world architectures with a focus on endpoints, and blockchain‐based intrusion detection systems. We discuss the potential of blockchain's immutability fortifying the detection process and identify open challenges as well as potential solutions and future directions.
Rahul Pitale, Kapil Tajane, S. S. Khandagale, Vidhya Gadewar · 6 authors
The increase in fake goods is severely affecting the industrial sector and consumers. According to the survey, incidences involving fake products have increased in recent years, which have had a negative impact on sales, profits, and brand recognition. Far from reducing the ability of a company to make money, this counterfeiting also affects the consumer's ability to trust their goods in open market. Without proper tracking and security measures, consumers gradually stop trusting brands to keep their consumers safe from theft. This is happening because the manufacturing and distribution processes are hidden to consumers and this information is easily manipulated or falsified by others. So, users must have a method to determine if a product is genuine or not. This study proposes a blockchain based anti-counterfeiting system for product traceability throughout the supply chain. By using public or permissionless blockchain all the information throughout the supply chain will be recorded in the blockchain network in the form of blocks that are immutable, transparent, secure, tamper-proof, and trusted. This proposed method uses QR code for making the system further secure.
Clients of permissionless blockchain systems, like Bitcoin, rely on an underlying peer-to-peer network to send and receive transactions. It is critical that a client is connected to at least one honest peer, as otherwise the client can be convinced to accept a maliciously forked view of the blockchain. In such aneclipse attack, the client is unable to reliably distinguish the canonical view of the blockchain from the view provided by the attacker. The consequences of this can be catastrophic if the client makes business decisions based on a distorted view of the blockchain transactions. In this paper, we investigate the design space and propose two approaches for Bitcoin clients to detect whether an eclipse attack against them is ongoing. Each approach chooses a different trade-off between average attack detection time and network load. The first scheme is based on the detection of suspicious block timestamps. The second scheme allows blockchain clients to utilize their natural connections to the Internet (i.e., standard Web activity) to gossip about their blockchain views with contacted servers and their other clients. Our proposals improve upon previously proposed eclipse attack countermeasures without introducing any dedicated infrastructure or changes to the Bitcoin protocol and network, and we discuss an implementation. We demonstrate the effectiveness of the gossip-based schemes through rigorous analysis using original Internet traffic traces and real-world deployment. The results indicate that our protocol incurs a negligible overhead and detects eclipse attacks rapidly with high probability, and is well-suited for practical deployment.
Having a transparent and convenient insurance system is desired by users, but the insurance industry currently plays differently. For example, when users ask for a fast and simple self-service, the industry may need a long and cumbersome process, and sometimes users are not sure whether they have a fair deal according to their contract. With the aim of enhancing the current bicycle insurance system in Denmark, we investigate the local market and develop BlockCycle, which is a blockchain-based bicycle insurance system. The basic idea is that insurance companies transform their insurance products into a smart contract, including the criteria of applying the product and the eligibility of compensating into program logic. When a smart contract is deployed, the immutability can ensure the deal integrity and protect both users and insurance companies from the threat of unauthorized manipulation. In addition, blockchain can provide a secure and simple way to decentralize data and services, which enables high availability and reliability. Our experimental results indicate the viability of our system.
Bin Wang, Han Liu, Chao Liu, Zhiqiang Yang · 7 authors
Decentralized finance, i.e., DeFi, has become the most popular type of application on many public blockchains (e.g., Ethereum) in recent years. Compared to the traditional finance, DeFi allows customers to flexibly participate in diverse blockchain financial services (e.g., lending, borrowing, collateralizing, exchanging etc.) via smart contracts at a relatively low cost of trust. However, the open nature of DeFi inevitably introduces a large attack surface, which is a severe threat to the security of participants funds. In this paper, we proposed BLOCKEYE, a real-time attack detection system for DeFi projects on the Ethereum blockchain. Key capabilities provided by BLOCKEYE are twofold: (1) Potentially vulnerable DeFi projects are identified based on an automatic security analysis process, which performs symbolic reasoning on the data flow of important service states, e.g., asset price, and checks whether they can be externally manipulated. (2) Then, a transaction monitor is installed offchain for a vulnerable DeFi project. Transactions sent not only to that project but other associated projects as well are collected for further security analysis. A potential attack is flagged if a violation is detected on a critical invariant configured in BLOCKEYE, e.g., Benefit is achieved within a very short time and way much bigger than the cost. We applied BLOCKEYE in several popular DeFi projects and managed to discover potential security attacks that are unreported before. A video of BLOCKEYE is available at https://youtu.be/7DjsWBLdlQU.