In recent times, there has been a swift advancement in the field of cryptocurrency. The advent of cryptocurrency has provided us with convenience and prosperity, but has also given rise to certain illicit and unlawful activities. Unlike classical currency, cryptocurrency conceals the activities of criminals and exposes their behavioral patterns, allowing us to determine whether present cryptocurrency transactions are legitimate by analyzing their behavioral patterns. There are two issues to consider when determining whether cryptocurrency transactions are legitimate. One is that most cryptocurrency transactions comply with laws and regulations, but only a small portion of them are used for illegal activities, which is related to the sample imbalance problem. The other issue concerns the excessive volume of data, and there are some unknown illegal transactions, so the data set contains an abundance of unlabeled data. As a result, it is critical to accurately distinguish between which transactions among the plethora of cryptocurrency transactions are legitimate and which are illegal. This presents quite a difficult challenge. Consequently, this paper combines mutual information and self-supervised learning to create a self-supervised model on the basis of mutual information that is used to improve the massive amount of untagged data that exist in the data set. Simultaneously, by merging the conventional cross-entropy loss function with mutual information, a novel loss function is created. It is employed to address the issue of sample imbalance in data sets. The F1-Score results obtained from our experimentation demonstrate that the novel loss function in the GCN method improves the performance of cryptocurrency illegal behavior detection by four points compared with the traditional loss function of cross-entropy; use of the self-supervised network that relies on mutual information improves the performance by three points compared with the original GCN method; using both together improves the performance by six points.
Umair B. Chaudhry, Aysha Kattakath Mulangat Hydros
Abstract Cyber security in the banking sector is of high importance nowadays. The rate of cyberattacks is spiking every year, and the implementation of strong cybersecurity models is required to ensure the confidentiality and integrity of data. Since protecting a bank requires a wide range of security practices, this paper focuses on protecting the bank resources from malicious actors and securing the transactions using a blockchain consensus mechanism that uses a zero‐trust security approach among the participants in the transaction. In addition to the framework, an algorithm for blockchain‐based online transactions was designed to make use of practical implementation in the future. The ideas formulated during the research and literature review were integrated to design the framework and the algorithm. The proposed framework ensures that the security of the banking sector can be enhanced by adopting the zero‐trust concept and blockchain technology. The consensus algorithms used for the transaction make it immutable and decentralized. Zero‐trust principles adopted in the model ensure the confidentiality and integrity of the banking system.
Efficient and convenient vulnerability detection for smart contracts is a key issue in the field of smart contracts. The earlier vulnerability detection for smart contracts mainly relies on static symbol analysis, which has high accuracy but low efficiency and is prone to path explosion. In this paper, the authors propose a static method for vulnerability detection based on deep learning. It first disassembles Ethereum smart contracts into opcode sequences and then converts the vulnerability detection problem into a natural language text classification problem. The word vector method is employed to map each opcode to a uniform vector space, and the opcode sequence matrix is trained by the TextCNN method to detect vulnerabilities. Furthermore, a code obfuscation method is given to enhance and balance the dataset, while three different opcode sequence generation methods are proposed to construct features. The experimental results verify that the average prediction accuracy of each smart contract exceeds 96%, and the average detection time is less than 0.1 s.
Casimer DeCusatis, Brian Gormanly, John Iacino, Reed Percelay · 6 authors
Blockchain, smart contracts, and related concepts have emerged in recent years as a promising technology for cryptocurrency, NFTs, and other areas. However, there are still many security issues that must be addressed as these technologies evolve. This paper reviews some of the leading social engineering attacks on smart contracts, as well as several vulnerabilities which result from insecure code development. A smart contract test bed is constructed using Solidity and a Metamask wallet to evaluate vulnerabilities such as insecure arithmetic, denial of service, and re-entrancy attacks. Cross-chain vulnerabilities and potential vulnerabilities resulting from layer 2 side-chain processing were also investigated. Mitigation best practices are proposed based on the experimental results.
Lakshmi P. Krishnan, Iman Vakilinia, Sandeep Reddivari, Sanjay Ahuja
With the emergence of cryptocurrencies and Blockchain technology, the financial sector is turning its gaze toward this latest wave. The use of cryptocurrencies is becoming very common for multiple services. Food chains, network service providers, tech companies, grocery stores, and so many other services accept cryptocurrency as a mode of payment and give several incentives for people who pay using them. Despite this tremendous success, cryptocurrencies have opened the door to fraudulent activities such as Ponzi schemes, HYIPs (high-yield investment programs), money laundering, and much more, which has led to the loss of several millions of dollars. Over the decade, solutions using several machine learning algorithms have been proposed to detect these felonious activities. The objective of this paper is to survey these models, the datasets used, and the underlying technology. This study will identify highly efficient models, evaluate their performances, and compile the extracted features, which can serve as a benchmark for future research. Fraudulent activities and their characteristics have been exposed in this survey. We have identified the gaps in the existing models and propose improvement ideas that can detect scams early.
Kai Wang, Maike Tong, Changhao Wu, Jun Pang · 7 authors
The development of clustering heuristics has demonstrated that Bitcoin is not completely anonymous. Currently, existing clustering heuristics only consider confirmed transactions recorded in the Bitcoin blockchain. However, unconfirmed transactions in the mempool have yet to be utilized to improve the performance of the clustering heuristics. In this paper, we bridge this gap by combining unconfirmed and confirmed transactions for clustering Bitcoin addresses effectively. First, we present a data collection system for capturing unconfirmed transactions. Two case studies are performed to show the presence of user behaviors in unconfirmed transactions not present in confirmed transactions. Next, we apply the state-of-the-art clustering heuristics to unconfirmed transactions, and the clustering results can reduce the number of entities after applying, for example, the co-spend heuristics in confirmed transactions by 2.3%. Finally, we propose three novel clustering heuristics to capture specific behavior patterns in unconfirmed transactions, which further reduce the number of entities after the application of the co-spend heuristics by 9.8%. Our results demonstrate the utility of unconfirmed transactions in address clustering and further shed light on the limitations of anonymity in cryptocurrencies. To the best of our knowledge, this paper is the first to apply the unconfirmed transactions in Bitcoin to cluster addresses.
Abstract The South African Local Government (SALG) uses the tendering system to procure goods and services. Some of these tendering projects are aimed at promoting socio-economic and industrial policies. Hence, the tendering system used by SALG should be fair, transparent, competitive, cost-effective, equitable, and free from corruption. However, the mismanagement of the tendering system might lead to interruption of operations, late service delivery, rising costs, and most importantly, fraud and corruption. The use of paperwork to share project information might lead to the mismanagement of the tendering project because it might contribute towards illicit altering of project information during the process. The purpose of this study is to develop a Blockchain prototype that might be used to securely share project information with all the parties interested in the tendering project. It is recommended that the adoption of the proposed solution will enable various organisations to have access to real-time data, allowing them to have access to the entire project history regardless of their geographical location. Access to real-time data would promote real-time auditing and digital forensic investigations because both auditors and investigators will have access to credible digital evidence or project information of their interest in real-time.
As computer technology develops, the popularity of cryptocurrencies and their use will grow, and the newer people enter the industry. It changes the business model between organized businesses out of the need for another trusted party. Blockchain smart contracts can automatically enforce agreed contract between two unknowns. Briefly introduce Ethereum, a cryptocurrency, and focus on the security of its smart contracts in internet transactions. Ethereum was the first platform to support high-level programming languages to implement smart contracts, and the second largest blockchain platform, providing a runtime environment for essentially all Decentralized Finance applications. Bitcoin also supports the development and execution of smart contracts, but it is affected by the nature of the programming language used, and it hardly supports transactions except for verifying signatures. Because smart contracts can support a variety of large transactions, some security vulnerabilities can be extremely costly. In an extensive search and survey, the issue of smart contracts for the Ethereum blockchain was valued. The article will discuss some of the existing or former contract vulnerabilities and their solutions. It concludes with a discussion of the future direction of the smart contract space and provides some suggestions for those researching the field.
Bank record security management is vital to the financial security of every nation. It aims to safeguard residents' as well as government income, determines eligibility for social assistance, and has a substantial influence on a country's foreign relations, economy and reputation. Numerous efforts are now ongoing throughout the globe to increase and improve security in the financial industry, especially in developing nations where demand is highest. It is common knowledge that the qualities of blockchain technology promote efficiency, transparency, and confidence. Concerns over data security, the danger of cybercrime, and the efficacy of bank system security are growing, especially in nations like Zambia that are still developing. Even after years later, it seems the banks and Ministry of Finance are unable to verify and assure the country's money is 100% secure, even inside the banks, according to information acquired and based on several interviews about bank security, the existence of a firewall is where all faith lies. Even after registration, there is no guarantee that client information is entirely secure and inaccessible to unauthorized third parties. This raises concerns and dangers about deceit being possible. In Zambia's bank record security management, there have been instances of illegal data changes and more recently, a cybercrime-related data breach. Throughout the years, government and public monies have been misappropriated without any documents to indicate what transpired or what happened to the revenue, and who knows what else. If the institution's information system is made safer and more dependable, process verification problems may be resolved. This research has been engaged with the aim of developing model design that employs blockchain technology principles to enhance Zambia's Record Security Management in banks. The later section of this research has an inclusion of a summary of the various literature about blockchain technology and Bank Record Security Management that is relevant. It describes the standard registry, storage, and operations, as well as a general description of blockchain, including its characteristics and uses. It demonstrates that using blockchain technology might boost efficiency by decreasing human labour and paper processing, and by giving clear provenance that verifies the origin of products. It may be used in industries such as health, education, agriculture, real estate, Bank record Security Management, etc. It also proves that smart contracts have a vast future potential, and that Ethereum is one of the most suitable platforms for constructing distributed applications using smart contracts. To fulfil the objective of the research, it was important to investigate Zambia's Record Security Management procedures in order to uncover continuing problems related with unauthorized alterations and other reported concerns. The Bank Blockchain Adoption Framework guided the examination of process mapping, stages and data. The results allow us to break Bank RSM into three phases: registration, verification and lastly, storage. Using a flowchart process diagram, network architecture, use cases, and technological development diagrams, a full design solution is shown. This project in design science offers a blockchain-based application architecture that will enhance the existing Bank Record Security Management System. It is proposed that the blockchain network be used to share datasets between banks, other financial organizations, and other informatics under the Ministry of Home Affairs Security. On the other hand, a centralized server will be an operating node in the blockchain network that is where records will be stored digitally. The research built a novel registration method on the Ethereum blockchain on Binance Smart Chain using the React App client, which in this case is the NodeJS server's web interface. The architecture was used to assess a smart contract's verification implementation. The NodeJS Server uses the web3 library plugin to interface with the Ethereum client network. Bank Record Security Management based on blockchain technology may enhance the security and integrity of banks and nations. This study's contribution may be utilized for additional research in each of the aforementioned application areas to uncover potential benefits and drawbacks.
Pablo Santamaría, Llanos Tobarra, Rafael Vargas, Antonio Robles-Gómez
The digital revolution is renewing many aspects of our lives, which is also a challenge in judicial processes, such as the Chain-of-Custody (CoC) process of any electronic evidence. A CoC management system must be designed to guarantee them to maintain its integrity in court. This issue is essential for digital evidence’s admissibility and probative value. This work has built and validated a real prototype to manage the CoC process of any digital evidence. Our technological solution follows a process model that separates the evidence registry and any evidence itself for scalability purposes. It includes the development of an open-source smart contract under Quorum, a version of Ethereum oriented to private business environments. The significant findings of our analysis have been: (1) Blockchain networks can become a solution, where integrity, privacy and traceability must be guaranteed between untrustworthy parties; and (2) the necessity of promoting the standardization of CoC smart contracts with a secure, simple process logic. Consequently, these contracts should be deployed in consortium environments, where reliable, independent third parties validate the transactions without having to know their content.
Wash trading is considered a highly inopportune and illegal behavior in regulated markets. Instead, it is practiced in unregulated markets, such as cryptocurrency or NFT (Non-Fungible Tokens) markets. Regarding the latter, in the past many researchers have been interested in this phenomenon from an “ex-ante” perspective, aiming to identify and classify wash trading activities before or at the exact time they happen. In this paper, we want to investigate the phenomenon of wash trading in the NFT market from a completely different perspective, namely “ex-post”. Our ultimate goal is to analyze wash trading activities in the past to understand whether the game is worth the candle, i.e., whether these illicit activities actually lead to a significant profit for their perpetrators. To the best of our knowledge, this is the first paper in the literature that attempts to answer this question in a “structured” way. The efforts to answer this question have enabled us to make some additional contributions to the literature in this research area. They are: (i) a framework to support future “ex-post” analyses of the NFT wash trading phenomenon; (ii) a new dataset on wash trading transactions involving NFTs that can support further future investigations of this phenomenon; (iii) a set of insights of the NFT wash trading phenomenon extracted at the end of an experimental campaign.
The banking sector is identified as the main means for laundering illicit money, these banks generally have access to both banking mechanism and legal authority to make decisions. Money launderers and those financing terrorism are conveniently accessing financial institutions and its mechanism. These institutions provide all financial funds transfers both domestic and international range. Anti-money laundering (AML) laws and other data protection laws that keep getting stricter have forced many financial institutions to put in place long, expensive processes to stay in compliance. To bridge the gap, emerging technology can help in mitigating money laundering and other financial crimes. Blockchain is considered one of the world's best-known examples of Distributed Ledger Technology. In the financial sector, this type of technology has been hailed as the key to future success. Emerging technology can be used in many ways in Financial Services. It can change many processes, payments between peer-to-peer, trade agreements and tracking of supply chains. Emerging technology can be used in many ways in financial services and can change many processes, peer-to-peer payments, trade agreements, and the tracking of supply chains. These use cases depend on the participants or users being identified and verified. "Know Your Customer" is the term for this (KYC). Before making a transaction, one of the most basic ways to build trust between the people involved is to check out the user. This current paper focuses on issues of compliance and Anti Money Laundering policies in the banking sector by using new emerging technologies such as blockchain; further, the paper focuses keenly on issues relating to the manipulation of KYC and the financial burden on banks while also addressing AML policies, Finally, the paper provides ideas and suggestions regarding the rise of emerging technologies such as blockchain while addressing the problems of ML. This also includes the capability of blockchain technology to bring banking systems with recalibrated mode of compliances polices.
Gaurav Dhiman, Marcello Carvalho dos Reis, Paulo C. S. Barbosa, Victor Hugo C. de Albuquerque · 5 authors
Abstract Retraction: [Gaurav Dhiman, Marcello Carvalho dos Reis, Paulo C. S. Barbosa, Victor Hugo C. de Albuquerque, Sandeep Kautish, Blockchain‐based covert software information transmission for bitcoin, IET Software 2023 ( https://doi.org/10.1049/sfw2.12120 )]. The above article from IET Software , published online on 8 February 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.
Financial enterprises generate profits based on economic development. More importantly, a healthy market is difficult to achieve due to their susceptibility to the parasitic credit card fraud transactions that accompany economic growth, unless an effective anti-counterfeiting technology is developed to alleviate the issue. To solve the problem, we propose a gradient-boosting decision tree based anti-fraud protection with blockchain Technology, referred to as GBDT-APBT, which treats anti-fraud transaction model as the accumulation of the classfiers' weakness and builds up a classifiers' to judge whether the transaction is fraudulent. Each user's private data is trained offline at the local blockchain node, then the trained model is directly uploaded to the cloud, and the final consensus model is obtained by voting. Due to incorporating blockchain technology, GBDT-APBT demonstrates decentralisation, openness, autonomy, anonymity, and immutability, showing its ability to satisfying the demand for an effective and beneficial anti-counterfeiting system, with high performance and effectiveness in detecting fraud information. Experiments show that compared with other methods, GBDT-APBT offers a promising approach to the security of credit card transactions with reference to the detection accuracy.
Purpose- The aim of this study is to reveal the publications that shed light on the dark side of the cryptocurrency markets with a systematic approach. Methodology- For this purpose, 369 publications in the Scopus database between 2014-2022 were determined as samples. In the publications provided by the database, the keywords "cryptocurrency" and "fraud", "scam", "phishing", "ponzi", "crime" were scanned over the publication title, abstract and keywords, and an integrated bibliometric analysis was employed. The R program was used in the analysis, and the "Biblioshiny" application in the RStudio program was employed to visualize the findings. Findings- The analysis reveals that the number of publications, the number of citations and the interest in the field have increased especially in recent years. The rate of increase in the number of publications in the field and the fact that most of these publications are at the stage of notification have shown that the field is an important developing field. The most intense interest in the field has been shown from universities in China. On the other hand, it was seen that the most interest in the field was from computer sciences and the interest of journals in the field of finance remained weak. It has been determined that the topics that may attract attention in the future are digital forensics, digital assets, fraudulent cryptocurrencies, corruption prevention, mining and cyber attacks. Conclusion- The study reveals the evolution of the dark side of cryptocurrency markets in academic research. The findings provide researchers interested in the field with the opportunity to explore themes and issues that may be on the agenda in the future. Keywords: Cryptocurrency, fraud, ponzi, crime, bibliometric analysis JEL Codes: G11, G19
As smart contracts process digital assets, their security is essential for blockchain applications. Many approaches have been proposed to detect smart contract vulnerabilities. Studies show that few of the reported vulnerabilities are exploited and hypothesize that many of the reported vulnerabilities are false positives. However, no follow-up study is performed to confirm the hypothesis and understand why the reported vulnerabilities are not exploited. In this study, we first collect 136,969 unique real-world smart contracts and analyze them using four vulnerability detectors, namely Oyente, SmartCheck, Slither, and SolDetector. Then, we apply Strauss’ grounded theory approach to manually analyze the source code of the smart contracts reported as vulnerable to recognizing false positives and understand the reasons for false results. In addition, we analyze the transaction logs of the smart contracts reported as vulnerable to identifying and understanding their exploitations. Our results show that 75.37% of the 4,364 smart contracts reported as vulnerable are false positives, and eleven reasons are causing the false positives. After analyzing the 4,106,134 transaction logs of the contracts reported as vulnerable, we find that vulnerabilities of only 67 (0.015%) of the contracts have been exploited in history. We also identify six reasons for demotivating and preventing the attackers from exploiting the vulnerabilities. Our results reveal that state-of-the-art smart contract vulnerability detectors primarily treat the smart contracts as yet another application developed using Object Oriented (OO) languages when analyzing and reporting the smart contract vulnerabilities. Without considering the specific design principles of the Solidity programming language and the characteristics of smart contracts’ application scenarios and execution environments, many of the reported vulnerabilities are not exploitable or not cost-effective to be exploited by adversaries.
Hanna Kim, Jian Cui, Eugene Jang, Chanhee Lee · 7 authors
As Non-Fungible Tokens (NFTs) continue to grow in popularity, NFT users have become targets of phishing attacks by cybercriminals, called \textit{NFT drainers}. Over the last year, \$100 million worth of NFTs were stolen by drainers, and their presence remains a serious threat to the NFT trading space. However, no work has yet comprehensively investigated the behaviors of drainers in the NFT ecosystem. In this paper, we present the first study on the trading behavior of NFT drainers and introduce the first dedicated NFT drainer detection system. We collect 127M NFT transaction data from the Ethereum blockchain and 1,135 drainer accounts from five sources for the year 2022. We find that drainers exhibit significantly different transactional and social contexts from those of regular users. With these insights, we design \textit{DRAINCLoG}, an automatic drainer detection system utilizing Graph Neural Networks. This system effectively captures the multifaceted web of interactions within the NFT space through two distinct graphs: the NFT-User graph for transaction contexts and the User graph for social contexts. Evaluations using real-world NFT transaction data underscore the robustness and precision of our model. Additionally, we analyze the security of \textit{DRAINCLoG} under a wide variety of evasion attacks.
Minh Hoang Nguyen, Phuong Duy Huynh, Son Hoang Dau, Xiaodong Li
The rapid development of blockchain and cryptocurrency in the past decade has created a huge demand for digital trading platforms. Popular decentralised exchanges (DEXs) such as Uniswap and PancakeSwap were created to address this market gap, facilitating cryptocurrency exchange without intermediaries and hence eliminating security and privacy issues associated with traditional centralised platforms. This, however, due to lack of regulation, results in the emergence of a host of damaging investment fraudulent schemes, including Ponzi, honey pot, pump-and-dump, and rug-pull.In this study, we aim to investigate the problem of detecting rug-pull on Uniswap using supervised learning. We aggregate a list of 23 features and propose the use of a hybrid feature selection technique to find the most relevant features for rug-pull. The classifier, using this refined set of features, outperforms the classifier in the previous studies and achieves an f1-score of 99%, a precision of 97% on non-malicious tokens, and a recall of 99% on malicious tokens. Additionally, we show that the XGBoost classifier, built using these proposed features, can distinguish scam tokens and newly listed tokens, which are often harder to differentiate as they have similar characteristics, and also propose a validation method.
Sayak Saha Roy, Dipanjan Das, Priyanka Bose, Christopher Kruegel · 6 authors
The rapid growth in popularity and hype surrounding digital assets such as art, video, and music in the form of non-fungible tokens (NFTs) has made them a lucrative investment opportunity, with NFT-based sales surpassing $25B in 2021 alone. However, the volatility and general lack of technical understanding of the NFT ecosystem have led to the spread of various scams. The success of an NFT heavily depends on its online virality. As a result, creators use dedicated promotion services to drive engagement to their projects on social media websites, such as Twitter. However, these services are also utilized by scammers to promote fraudulent projects that attempt to steal users' cryptocurrency assets, thus posing a major threat to the ecosystem of NFT sales. In this paper, we conduct a longitudinal study of 439 promotion services (accounts) on Twitter that have collectively promoted 823 unique NFT projects through giveaway competitions over a period of two months. Our findings reveal that more than 36% of these projects were fraudulent, comprising of phishing, rug pull, and pre-mint scams. We also found that a majority of accounts engaging with these promotions (including those for fraudulent NFT projects) are bots that artificially inflate the popularity of the fraudulent NFT collections by increasing their likes, followers, and retweet counts. This manipulation results in significant engagement from real users, who then invest in these scams. We also identify several shortcomings in existing anti-scam measures, such as blocklists, browser protection tools, and domain hosting services, in detecting NFT-based scams. We utilize our findings to develop and open-source a machine learning classifier tool that was able to proactively detect 382 new fraudulent NFT projects on Twitter.
Ransomware attacks are one of the most dangerous related crimes in the coin market. To increase the challenge of fighting the attack, early detection of ransomware seems necessary. In this article, we propose a high-performance Bitcoin transaction predictive system that investigates Bitcoin payment transactions to learn data patterns that can recognize and classify ransomware payments for heterogeneous bitcoin networks into malicious or benign transactions. The proposed approach makes use of three supervised machine learning methods to learn the distinctive patterns in Bitcoin payment transactions, namely, logistic regression (LR), random forest (RF), and Extreme Gradient Boosting (XGBoost). We evaluate these ML-based predictive models on the BitcoinHeist ransomware dataset in terms of classification accuracy and other evaluation measures such as confusion matrix, recall, and F1-score. It turned out that the experimental results recorded by the XGBoost model achieved an accuracy of 99.08%. As a result, the resulting model accuracy is higher than many recent state-of-the-art models developed to detect ransomware payments in Bitcoin transactions.
With the increase of the adoption of blockchain technology in providing decentralized solutions to various problems, smart contracts have become more popular to the point that billions of US Dollars are currently exchanged every day through such technology. Meanwhile, various vulnerabilities in smart contracts have been exploited by attackers to steal cryptocurrencies worth millions of dollars. The automatic detection of smart contract vulnerabilities therefore is an essential research problem. Existing solutions to this problem particularly rely on human experts to define features or different rules to detect vulnerabilities. However, this often causes many vulnerabilities to be ignored, and they are inefficient in detecting new vulnerabilities. In this study, to overcome such challenges, we propose a framework to automatically detect vulnerabilities in smart contracts on the blockchain. More specifically, first, we utilize novel feature vector generation techniques from bytecode of smart contract as source code is rarely publicly available. These feature vectors are then analyzed using our innovative metric learning-based Deep Neural Networks (DNNs) to produce detection results. The framework’s predictions are further refined through a voting mechanism to achieve consensus. We conduct comprehensive experiments on large-scale benchmarks, and the quantitative results demonstrate the effectiveness and efficiency of our approach.
Mobile applications have rapidly grown over the past few decades to offer futuristic applications, such as autonomous vehicles, smart farming, and smart city. Such applications require ubiquitous, real-time, and secure communications to deliver services quickly. Toward this aim, sixth-generation (6G) wireless technology offers superior performance with high reliability, enhanced transmission rate, and low latency. However, managing the resources of the aforementioned applications is highly complex in the precarious network. An adversary can perform various network-related attacks (i.e., data injection or modification) to jeopardize the regular operation of the smart applications. Therefore, incorporating blockchain technology in the smart application can be a prominent solution to tackle security, reliability, and data-sharing privacy concerns. Motivated by the same, we presented a case study on public safety applications that utilizes the essential characteristics of artificial intelligence (AI), blockchain, and a 6G network to handle data integrity attacks on the crime data. The case study is assessed using various performance parameters by considering blockchain scalability, packet drop ratio, and training accuracy. Lastly, we explored different research challenges of adopting blockchain in the 6G wireless network.
Internet of Things (IoT) enables communication among objects to collect information and make decisions to improve the quality of life. There are several unresolved security and privacy concerns in IoT due to multiple resource constrained devices, which lead to various cyber attacks. The conventional access control techniques depend on a central authority that further poses privacy and scalability issues in IoT. Various problems with access control in IoT can be resolved to prevent various cyber attacks using the decentralization and immutability properties of the blockchain. This study explored the current research trends in blockchain-enabled secure access control mechanisms and also identifies their applicability in creating reliable access control solutions for IoT. The basic properties of blockchain, such as decentralization, auditability, transparency, and immutability, act as the propulsion that provides integrity and security, disregarding the participation of an external entity. Initially, the application of blockchain was created only for cryptocurrencies but with the introduction of Ethereum, which allows the writiting and execution of smart contracts, applications other than cryptocurrencies are also being created. As various research articles have been written on the usage of different types of blockchains for creating secure access control solutions for IoT, this study intends to find and examine such primary researches as well as come up with a systematic review of various findings. This study perceives the most frequently utilized blockchain for creating blockchain-based access control solutions to prevent various cyber attacks and also discusses the improvement in access control mechanisms using blockchain along with smart contracts in IoT. The present study also discusses the obstacles in building decentralized access control solutions for IoT systems as well as future research areas. For new researchers, this article is a nice place to start and a strong reference point.
The Internet of Things (IoT) is a key enabler technology that recently received significant attention from the scientific community across the globe. It helps transform everyone’s life by connecting physical and virtual devices with each other to offer staggering benefits, such as automation and control, higher productivity, real-time information access, and improved efficiency. However, IoT devices and their accumulated data are susceptible to various security threats and vulnerabilities, such as data integrity, denial-of-service, interception, and information disclosure attacks. In recent years, the IoT with blockchain technology has seen rapid growth, where smart contracts play an essential role in validating IoT data. However, these smart contracts can be vulnerable and degrade the performance of IoT applications. Hence, besides offering indispensable features to ease human lives, there is also a need to confront IoT environment security attacks, especially data integrity attacks. Toward this aim, this paper proposed an artificial intelligence-based system model with a dual objective. It first detects the malicious user trying to compromise the IoT environment using a binary classification problem. Further, blockchain technology is utilized to offer tamper-proof storage to store non-malicious IoT data. However, a malicious user can exploit the blockchain-based smart contract to deteriorate the performance IoT environment. For that, this paper utilizes deep learning algorithms to classify malicious and non-malicious smart contracts. The proposed system model offers an end-to-end security pipeline through which the IoT data are disseminated to the recipient. Lastly, the proposed system model is evaluated by considering different assessment measures that comprise the training accuracy, training loss, classification measures (precision, recall, and F1 score), and receiver operating characteristic (ROC) curve.