Franklin Tchakounté, Koudanbe Amadou Calvin, Ado Adamou Abba Ari, David Jaurès Fotsa-Mbogne
One of the main concerns of cybersecurity is the detection of hoaxes across social media. Hoaxers propagate such messages to mislead users and to promote violence. Several approaches exist in literature to address this issue. They are mainly limited to detect hoax activities by characterizing the message nature and detecting provenance of messages. However, unless hoaxes are detected, they continue to propagate across social media nodes. This work aims at reducing the dissemination of hoaxes across group of users. Relying on social graph structure, this research develops a mechanism based on smart contract logics to prevent a group to consume a fake post. To achieve this objective, we used a smart contract to exploit a trust index computed based on message characteristics and group features such as graph density, group status, group degree, group acceptability. Based on the value of trust index, the message is forwarded or blocked. Experiments realized on groups of different characteristics revealed that the proposed smart contract is even able to reactively block a fake post of the same nature than the group type. Results indicate that the proportion of targeted groups could be reduced even if their interests match with the message subject. This research is an important step forward to anti-promote hoaxes with the novelty of exploiting smart contract approach to contain their propagation.
It is widely accepted that Ethereum mining is highly centralized. Nonetheless, centralization has been mostly characterized by exclusively looking at the influence that independent miners or mining pools can have over the network. Moreover, models of mining behavior assume that miners are either unrelated or only relate via mining pools under highly structured and transparent agreements. If these assumptions and the predictions they entail were to be completely accurate, there would not be any evidence of on-chain transactions between miners, other than the ones expected from mining pool payouts. By looking at on-chain transactions between miners in the Ethereum Network we find that aside from the payouts from mining pools to small miners, there are also transactions that define relationships between mining pools, independent miners and between independent miners and mining pools. Furthermore, by characterizing the topology of the network of miner transactions, we find the emergence of highly connected clusters that control significant amounts of hashing power and exhibit relationships in the opposite direction of what theoretical models predict. This more nuanced characterization of mining centralization can help identify network vulnerabilities and inform protocol redesigns.
Taehyun Kim, Hyomin Shin, Hyung Ju Hwang, Seungwon Jeong
Steemit is a blockchain-based social media platform, where authors can get author rewards in the form of cryptocurrencies called STEEM and SBD (Steem Blockchain Dollars) if their posts are upvoted. Interestingly, curators (or voters) can also get rewards by voting others' posts, which is called a curation reward. A reward is proportional to a curator's STEEM stakes. Throughout this process, Steemit hopes "good" content will be automatically discovered by users in a decentralized way, which is known as the Proof-of-Brain (PoB). However, there are many bot accounts programmed to post automatically and get rewards, which discourages real human users from creating good content. We call this type of bot a posting bot. While there are many papers that studied bots on traditional centralized social media platforms such as Facebook and Twitter, we are the first to study posting bots on a blockchain-based social media platform. Compared with the bot detection on the usual social media platforms, the features we created have an advantage that posting bots can be detected without limiting the number or length of posts. We can extract the features of posts by clustering distances between blog data or replies. These features are obtained from the Minimum Average Cluster from Clustering Distance between Frequent words and Articles (MAC-CDFA), which is not used in any of the previous social media research. Based on the enriched features, we enhanced the quality of classification tasks. Comparing the F1-scores, the features we created outperformed the features used for bot detection on Facebook and Twitter.
In Proof-of-Work Blockchain-based systems, the ledger is kept consistent through some participants solving cryptopuzzles, usually referred to as block mining. Conventional wisdom asserts that the mining protocol is incentive-compatible. However, whether some strategic mining behaviors occur in practice or not, has been the subject of extensive debate. In this paper, we target this question by detecting anomalies in the statistics of consecutive blocks among several popular cryptocurrency systems. Firstly, we measure the inequality of mining revenue distribution in each system. Secondly, we propose a statistical method to identify the selfish mining (SM) behavior, a mining attack strategy posited by Eyal and Sirer in 2014. Our method is based on abnormal (statistically significant) high probability of continuously mining blocks. Finally, we extend our method to detect the mining cartels, in which miners secretly get together and share information about newly mined blocks. Our analysis will contribute to the research of fairness in cryptocurrency mining by providing evidence that the aforementioned strategic mining behaviors do take place in practice.
Alexandra Mai, Katharina Pfeffer, Matthias Gusenbauer, Edgar Weippl · 5 authors
Frequent reports of monetary loss, fraud, and user-caused security incidents in the context of cryptocurrencies emphasize the need for human-centered research in this domain. We contribute the first qualitative user study (N=29) on user mental models of cryptocurrency systems and the associated threat landscape. Using Grounded Theory, we reveal misconceptions affecting users' security and privacy. Our results suggest that current cryptocurrency tools (e.g., wallets and exchanges) are not capable of counteracting threats caused by these misconceptions. Hence, users frequently fail to securely manage their private keys or assume to be anonymous when they are not. Based on our findings, we contribute actionable advice, grounded in the mental models of users, to improve the usability and secure usage of cryptocurrency systems.
Many popular blockchain platforms are supporting smart contracts for building decentralized applications. However, the vulnerabilities within smart contracts have led to serious financial loss to their end users. For the EOSIO blockchain platform, effective vulnerability detectors are still limited. Furthermore, existing vulnerability detection tools can only support one blockchain platform. In this work, we present WANA, a cross-platform smart contract vulnerability detection tool based on the symbolic execution of WebAssembly bytecode. Furthermore, WANA proposes a set of test oracles to detect the vulnerabilities in EOSIO and Ethereum smart contracts based on WebAssembly bytecode analysis. Our experimental analysis shows that WANA can effectively detect vulnerabilities in both EOSIO and Ethereum smart contracts with high efficiency.
As COVID-19 has been spreading across the world since early 2020, a growing number of malicious campaigns are capitalizing the topic of COVID-19. COVID-19 themed cryptocurrency scams are increasingly popular during the pandemic. However, these newly emerging scams are poorly understood by our community. In this paper, we present the first measurement study of COVID-19 themed cryptocurrency scams. We first create a comprehensive taxonomy of COVID-19 scams by manually analyzing the existing scams reported by users from online resources. Then, we propose a hybrid approach to perform the investigation by: 1) collecting reported scams in the wild; and 2) detecting undisclosed ones based on information collected from suspicious entities (e.g., domains, tweets, etc). We have collected 195 confirmed COVID-19 cryptocurrency scams in total, including 91 token scams, 19 giveaway scams, 9 blackmail scams, 14 crypto malware scams, 9 Ponzi scheme scams, and 53 donation scams. We then identified over 200 blockchain addresses associated with these scams, which lead to at least 330K US dollars in losses from 6,329 victims. For each type of scams, we further investigated the tricks and social engineering techniques they used. To facilitate future research, we have released all the well-labelled scams to the research community.
COVID-19 is a severe global epidemic in human history. Even though there are particular medications and vaccines to curb the epidemic, tracing and isolating the infection source is the best option to slow the virus spread and reduce infection and death rates. There are three disadvantages to the existing contact tracing system: 1. User data is stored in a centralized database that could be stolen and tampered with, 2. User’s confidential personal identity may be revealed to a third party or organization, 3. Existing contact tracing systems [1][2] only focus on information sharing from one dimension, such as location-based tracing, which significantly limits the effectiveness of such systems.We propose a global COVID-19 information sharing and risk notification system that utilizes the Blockchain, Smart Contract, and Bluetooth. To protect user privacy, we design a novel Blockchain-based platform that can share consistent and non-tampered contact tracing information from multiple dimensions, such as location-based for indirect contact and Bluetooth-based for direct contact. Hierarchical smart contract architecture is also designed to achieve global agreements from users about how to process and utilize user data, thereby enhancing the data usage transparency. Furthermore, we propose a mechanism to protect user identity privacy from multiple aspects. More importantly, our system can notify the users about the exposure risk via smart contracts. We implement a prototype system to conduct extensive measurements to demonstrate the feasibility and effectiveness of our system.
Ben Weintraub, Cristina Nita-Rotaru, Stefanie Roos
Payment channel networks (PCN) enable scalable blockchain transactions without fundamentally changing the underlying distributed ledger algorithm. However, routing a payment via multiple channels in a PCN requires locking collateral for potentially long periods of time. Adversaries can abuse this mechanism to conduct denial-of-service attacks. Previous work focused on source routing, which is unlikely to remain a viable routing approach as these networks grow. In this work, we examine the effectiveness of attacks in PCNs that use routing algorithms based on local knowledge, where compromised intermediate nodes can delay or drop transactions to create denial-of-service. We focus on SpeedyMurmurs as a representative of such protocols. We identify two attacker node selection strategies; one based on the position in the routing tree, and the other on between-ness centrality. Our simulation-driven study shows that while they are both effective, the centrality-based attack approaches near-optimal effectiveness. We also show that the attacks are ineffective in less centralized networks and discuss incentives for the participants in PCNs to create less centralized topologies through the payment channels they establish among themselves.
Ben Weintraub, Cristina Nita-Rotaru, Stefanie Roos
Payment channel networks (PCN) enable scalable blockchain transactions without fundamentally changing the underlying distributed ledger algorithm. However, routing a payment via multiple channels in a PCN requires locking collateral for potentially long periods of time. Adversaries can abuse this mechanism to conduct denial-of-service attacks. Previous work on denial-of-service attacks focused on source routing, which is unlikely to remain a viable routing approach as these networks grow.
In this work we examine the effectiveness of attacks in PCNs that use routing algorithms based on local knowledge, where compromised intermediate nodes delay or drop transactions to create denial-of-service. We focus on SpeedyMurmurs as a representative of such protocols. Our attack simulations show that SpeedyMurmurs is resilient to attacks by randomly selected intermediate nodes because it dynamically adjusts using local knowledge. We further consider attackers that control a significant fractions of paths and we show that this ability to route around problematic regions becomes insufficient for such attackers. We propose methods to incentivize payment channel networks with less central nodes and more diverse paths and show through simulation that these methods effectively mitigate the identified denial-of-service attacks.
Education Degree Fraud Detection and Student Certificate Verification using Blockchain - written by Jayesh G. Dongre , Sonali M. Tikam , Vasudha B. Gharat published on 2020/07/13 download full article with reference data and citations
A proof of work (PoW) blockchain protocol distributes rewards to its participants, called miners, according to their share of the total computational power. Sufficiently large miners can perform selfish mining - deviate from the protocol to gain more than their fair share. Such systems are thus secure if all miners are smaller than a threshold size so their best response is following the protocol. To find the threshold, one has to identify the optimal strategy for miners of different sizes, i.e., solve a Markov Decision Process (MDP). However, because of the PoW difficulty adjustment mechanism, the miners' utility is a non-linear ratio function. We therefore call this an Average Reward Ratio (ARR) MDP. Sapirshtein et al.\ were the first to solve ARR MDPs by solving a series of standard MDPs that converge to the ARR MDP solution. In this work, we present a novel technique for solving an ARR MDP by solving a single standard MDP. The crux of our approach is to augment the MDP such that it terminates randomly, within an expected number of rounds. We call this Probabilistic Termination Optimization (PTO), and the technique applies to any MDP whose utility is a ratio function. We bound the approximation error of PTO - it is inversely proportional to the expected number of rounds before termination, a parameter that we control. Empirically, PTO's complexity is an order of magnitude lower than the state of the art. PTO can be easily applied to different blockchains. We use it to tighten the bound on the threshold for selfish mining in Ethereum.
Abstract Directed Graph based models of a blockchain that capture accounts as nodes and transactions as edges, evolve over time. This temporal nature of a blockchain model enables us to understand the behavior (malicious or benign) of the accounts. Predictive classification of accounts as malicious or benign could help users of the permissionless blockchain platforms to operate in a secure manner. Motivated by this, we introduce temporal features such as burst and attractiveness on top of several already used graph properties such as the node degree and clustering coefficient. Using identified features, we train various Machine Learning (ML) models and identify the algorithm that performs the best in detecting malicious accounts. We then study the behavior of the accounts over different temporal granularities of the dataset before assigning them malicious tags. For the Ethereum blockchain, we identify that for the entire dataset—the ExtraTreesClassifier performs the best among supervised ML algorithms. On the other hand, using cosine similarity on top of the results provided by unsupervised ML algorithms such as K-Means on the entire dataset, we were able to detect 554 more suspicious accounts. Further, using behavior change analysis for accounts, we identify 814 unique suspicious accounts across different temporal granularities.
Ransomware is malicious software (malware) that blocks access to someone’s computer system or files on the system and subsequently demands a ransom to be paid for unlocking the computer or files. Ransomware is considered one of the main threats in cybercrime today. Cryptoware is a specific type of ransomware, which encrypts files on computer systems. The ransom is often demanded in bitcoins. Based on desk research, a series of interviews, and the investigation of several police files, this paper investigates the modi operandi in which cybercriminals use ransomware and cryptoware to make profits and how they launder these profits. Two models, based on the payment of the ransom via vouchers and via bitcoins respectively, are identified and described. These methods allow criminals to launder profits in relative anonymity and prevent the seizure of the illegally obtained money.
Bithin Alangot, Daniël Reijsbergen, Sarad Venugopalan, Paweł Szałachowski
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 an eclipse 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.
Bithin Alangot, Daniël Reijsbergen, Sarad Venugopalan, Paweł Szałachowski
Clients of permissionless blockchain systems, like Bitcoin, rely on an\nunderlying peer-to-peer network to send and receive transactions. It is\ncritical that a client is connected to at least one honest peer, as otherwise\nthe client can be convinced to accept a maliciously forked view of the\nblockchain. In such an eclipse attack, the client is unable to reliably\ndistinguish the canonical view of the blockchain from the view provided by the\nattacker. The consequences of this can be catastrophic if the client makes\nbusiness decisions based on a distorted view of the blockchain transactions. In\nthis paper, we investigate the design space and propose two approaches for\nBitcoin clients to detect whether an eclipse attack against them is ongoing.\nEach approach chooses a different trade-off between average attack detection\ntime and network load. The first scheme is based on the detection of suspicious\nblock timestamps. The second scheme allows blockchain clients to utilize their\nnatural connections to the Internet (i.e., standard web activity) to gossip\nabout their blockchain views with contacted servers and their other clients.\nOur proposals improve upon previously proposed eclipse attack countermeasures\nwithout introducing any dedicated infrastructure or changes to the Bitcoin\nprotocol and network, and we discuss an implementation. We demonstrate the\neffectiveness of the gossip-based schemes through rigorous analysis using\noriginal Internet traffic traces and real-world deployment. The results\nindicate that our protocol incurs a negligible overhead and detects eclipse\nattacks rapidly with high probability, and is well-suited for practical\ndeployment.\n
Zakwan Jaroucheh, Mohamad Alissa, William J. Buchanan, Xiaodong Liu
The growing trend of sharing news/contents, through social media platforms and the World Wide Web has been seen to impact our perception of the truth, altering our views about politics, economics, relationships, needs and wants. This is because of the growing spread of misinformation and disinformation intentionally or unintentionally by individuals and organizations. This trend has grave political, social, ethical, and privacy implications for society due to 1) the rapid developments in the field of Machine Learning (ML) and Deep Learning (DL) algorithms in creating realistic-looking yet fake digital content (such as text, images, and videos), 2) the ability to customize the content feeds and to create a polarized so-called "filter-bubbles" leveraging the availability of the big-data. Therefore, there is an ethical need to combat the flow of fake content. This paper attempts to resolves some of the aspects of this combat by presenting a high-level overview of TRUSTD, a blockchain and collective signature based ecosystem to help content creators in getting their content backed by the community, and to help users judge on the credibility and correctness of these contents.
In recent years, blockchain technology has created a new cryptocurrency world and has attracted a lot of attention. It also is rampant with various scams. For example, phishing scams have grabbed a lot of money and has become an important threat to users' financial security in the blockchain ecosystem. To help deal with this issue, this paper proposes a systematic approach to detect phishing accounts based on blockchain transactions and take Ethereum as an example to verify its effectiveness. Specifically, we propose a graph-based cascade feature extraction method based on transaction records and a lightGBM-based Dual-sampling Ensemble algorithm to build the identification model. Extensive experiments show that the proposed algorithm can effectively identify phishing scams.
The current pandemic situation has made us realize how fake news can have detrimental impacts on society. Although we can't ignore the benefits of social media, we surely can't overlook the misuse of these mediums to spread rumors and fake articles. These posts/ articles/ messages can be commonly called "Fake News" and they not only create misinformation but also create panic among the public. Blockchain is a disruptive technology that is being used to make innovative solutions in the sectors of food, fashion, supply chain, and banking. The decentralized and transparent nature of blockchain can not only be used in the above sectors but it can also be used to tackle the problem of fake news. Several solutions have been proposed to combat the problem using blockchain. This paper will propose one such solution to combat the fake news problem that can be used by all the news agencies. The system is called the "News Tracking and Tracing System " and is built using Ethereum.
Product serialization aims to allocate unique serial numbers to products in a supply chain. The security challenges to product serialization are: • Valid serial numbers can be stolen and used to label fake products. Thus uniqueness of a serial number should be verifiable at any stage of its lifecycle in a supply chain. • A planned change of custody of a product in distribution can be corrupted by a few intimidatory nodes. Compliance with the planned change of custody should be verifiable. • The manufacturer and the consumer should be able to verify that perishable food products with expired shelf life are discarded. In this paper, we use blockchains to develop a product serialization method that solves the above security issues in a multi-party perishable good supply chain. Blockchains can revolutionize security and transparency in supply chains by providing a secure data-sharing platform in a multi-party environment. Although blockchains can provide a secure data storage of change of custody events of products in a supply chain, a high volume of such events poses scalability problems for blockchains. In this paper, we solve the product serialization problem using blockchain offline channels. Our solution significantly reduces the number of transactions needed to be recorded in the blockchain. We propose a secure serialization protocol to verify the authenticity of serial numbers despite not frequently engaging with the blockchain.
Ransomware is malicious software (malware) that blocks access to someone’s computer system or files on the system and subsequently demands a ransom to be paid for unlocking the computer or files. Ransomware is considered one of the main threats in cybercrime today. Cryptoware is a specific type of ransomware, which encrypts files on computer systems. The ransom is often demanded in bitcoins. Based on desk research, a series of interviews, and the investigation of several police files, this paper investigates the modi operandi in which cybercriminals use ransomware and cryptoware to make profits and how they launder these profits. Two models, based on the payment of the ransom via vouchers and via bitcoins respectively, are identified and described. These methods allow criminals to launder profits in relative anonymity and prevent the seizure of the illegally obtained money.
Cryptocurrency has seen an explosive growth in recent years, thanks to the evolvement of blockchain technology and its economic ecosystem. Besides Bitcoin, thousands of cryptocurrencies have been distributed on blockchains, while hundreds of cryptocurrency exchanges are emerging to facilitate the trading of digital assets. At the same time, it also attracts the attentions of attackers. Fake deposit, as one of the most representative attacks (vulnerabilities) related to exchanges and tokens, has been frequently observed in the blockchain ecosystem, causing large financial losses. However, besides a few security reports, our community lacks of the understanding of this vulnerability, for example its scale and the impacts. In this paper, we take the first step to demystify the fake deposit vulnerability. Based on the essential patterns we have summarized, we implement DEPOSafe, an automated tool to detect and verify (exploit) the fake deposit vulnerability in ERC-20 smart contracts. DEPOSafe incorporates several key techniques including symbolic execution based static analysis and behavior modeling based dynamic verification. By applying DEPOSafe to 176,000 ERC-20 smart contracts, we have identified over 7,000 vulnerable contracts that may suffer from two types of attacks. Our findings demonstrate the urgency to identify and prevent the fake deposit vulnerability.
Çalışmanın amacı, günümüz dijital çağında geleneksel muhasebe süreçlerinden teknolojik süreçlere geçişte blockchain (blok zinciri) teknolojisini muhasebe ve mali kontroller açısından incelemektir. Bu bağlamda çalışma, blok zinciri uygulamalarına, yol haritasına, kurumsal uygulamalarda blok zinciri etkinliğine ve blok zinciri temelli muhasebe sistemine odaklanmaktadır. Blok zinciri temelli üç taraflı muhasebe sistemi ile birlikte, Kurumsal Kaynak Planlama (ERP) sistemlerinin blok zinciri ile entegrasyonu ve teknolojinin bir diğer yönü olan kod ile yazılmış, belirli bir blok zinciri katmanına gömülü akıllı sözleşmeler detaylı olarak ele alınmıştır. Blok zinciri temelli üç taraflı muhasebe sistemi; çift girişli sistemin üzerine kurulacak, içine gömülü üçüncü bir blok zinciri katmanı olacak hem kurum içinde hem de dış taraflarla yapılan işlemleri kaydedecek bir sistem önermektedir.
EOSIO has become one of the most popular blockchain platforms since its mainnet launch in June 2018. In contrast to the traditional PoW-based systems (e.g., Bitcoin and Ethereum), which are limited by low throughput, EOSIO is the first high throughput Delegated Proof of Stake system that has been widely adopted by many decentralized applications. Although EOSIO has millions of accounts and billions of transactions, little is known about its ecosystem, especially related to security and fraud. In this paper, we perform a large-scale measurement study of the EOSIO blockchain and its associated DApps. We gather a large-scale dataset of EOSIO and characterize activities including money transfers, account creation and contract invocation. Using our insights, we then develop techniques to automatically detect bots and fraudulent activity. We discover thousands of bot accounts (over 30% of the accounts in the platform) and a number of real-world attacks (301 attack accounts). By the time of our study, 80 attack accounts we identified have been confirmed by DApp teams, causing 828,824 EOS tokens losses (roughly \$2.6 million) in total.