AI as the first line of defense for cybersecurity, the goal was to keep the cyber-criminals at bay, but in case they managed to get in and infiltrate the network, we need to initiate the second line of defense – blockchain. New blockchain platforms are stepping up to address security concerns in the face of recent breaches. Transactions in the blockchain can be audited and traced. Since a blockchain system is protected with the help of ledgers and cryptographic keys, attacking and manipulating it becomes extremely difficult. Blockchain decentralizes the systems by distributing ledger data on several systems rather than storing them on one single network. The confidentiality of network members is high due to the public-key cryptography that authenticates users and encrypts their transactions. The distributed ledger ensures the protection of data against modification or destruction.
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.
Abstract For a long time, anomaly detection is such a well topic. Its use in the banking industry has aided in the detection of questionable hacking activity. In the network of bitcoin, since all nodes are unlabeled, there is no proof that any particular transaction is the result of illegal activity, this thesis seeks to identify transactions that are unusual or suspicious. Finding abnormalities in the bitcoin transaction network is the main objective. We discuss anomaly identification in this paper with particular reference to the Bitcoin transaction network(BTN). In this instance, anomalies behaviors is a proxy for apprehensive activity, thus our objective is to find anomalies in the dataset in terms of their percentage. To achieve this, we use the feature selection method which is sequential forward feature selection along with three ML techniques, k-means clustering, isolation forest, and support vector machine (SVM) and got the highest accuracy of 98.2% in SVM as compared to all other methods.
In this study, we propose a permissionless private blockchain framework and clarify that this framework is effective for blockchains self-managed and maintained by a single organization. The idea of our framework is to make a private blockchain permissionless, so that it can be verified by many unspecified users, thereby suppressing fraud by a single organization whose trust is important. Therefore, we argue that it makes sense for a single organization to use a permissionless private blockchain from a security perspective.
Mohammed Al Shamsi, Deborah Drummond Smith, Kimberly C. Gleason
Purpose The purpose of this paper is to describe how non-fungible tokens (NFTs) can be used in the commission of financial crime, including money laundering and crypto-fraud schemes, using the framework of the Space Transition Theory. Design/methodology/approach A literature review relating the Space Transition Theory to crime vulnerabilities related to NFTs is conducted and practical examples illustrating NFT schemes are provided. Findings The authors find that the Space Transition Theory explains the evolution of financial crimes into the NFT space. The transformation of the art industry from the physical to the virtual space through NFTs underlies the criminal activity surrounding them. NFTs enable crime because of the flexibility, dissociative anonymity, lack of deterrence and anonymity. Research limitations/implications Criminals can easily take advantage of the users’ limited knowledge of blockchain to defraud them of their money or tokens. These risks accentuate the need to adopt appropriate measures to augment the accountability of NFT transactions. Until such interventions are implemented, the NFT market remains a highly viable space for the perpetration of financial crimes. Practical implications The dynamic nature of the cyberspace and fast-past underlying technology provide a greater chance to escape than crimes committed in the physical space. The state of security on NFT platforms has elicited concerns from diverse quotas. NFTs pose significant money laundering risks because of the lack of appropriate regulatory mechanisms, generating a need for enhanced oversight and enforcement of sectors of the economy in physical space vulnerable to abuse in the NFT space, including entities such as art galleries, museums, sports teams and luxury brands. Social implications The Space Transition Theory is also supported in that norms and values regarding ethics and criminal actions in the physical space do not transfer to cyber space. Originality/value The novelty aspect of this research is in applying the Space Transition Theory to financial crime schemes based on NFTs.
A smart contract, as an important part of blockchain technology, has attracted considerable interest from both industry and academia. It provides the basis for the realization of a variety of practical blockchain applications and plays a crucial role in the blockchain ecosystem. While it also holds a large number of digital assets, the frequent occurrence of smart contract vulnerabilities have caused huge economic losses and destroyed the blockchain-based credit system. Currently, the security and reliability of smart contracts have become a new focus of research, and there are a number of smart contract vulnerability detection methods, such as traditional detection tools based on static or dynamic analysis. However, most of them often rely on expert rules, and therefore have poor scalability and high false negative and false positive rates. Recent deep learning methods alleviate this issue, but without considering the semantic information and context of source code. To this end, we propose a hybrid attention mechanism (HAM) model to detect security vulnerabilities in smart contracts. We extract code fragments from the source code, which focus on key points of vulnerability. We conduct extensive experiments on two public smart contract datasets (a total of 24,957 contracts). Empirical results show remarkable accuracy improvement over the state-of-the art methods on five kinds of vulnerabilities, where the detection accuracy could achieve 93.36%, 80.85%, 82.56%, 85.62%, and 82.19% for reentrancy, arithmetic vulnerability, unchecked return value, timestamp dependency, and tx.origin, respectively.
Md. Tauseef, Manjunath R Kounte, Abdul Haq Nalband, Mohammed Riyaz Ahmed
The emergence of the Internet of Things (IoT) has revolutionized the way we interact with the physical world. The rapid growth of IoT devices has led to a pressing need for robust security measures. Two promising approaches that can enhance IoT security are blockchain and artificial intelligence (AI). Blockchain can offer a decentralized and tamper-proof framework, ensuring the confidentiality and integrity of IoT data. AI can analyze large volumes of real-time data and detect anomalies in response to security threats in the IoT ecosystem. This paper explores the potential of these technologies and how they complement each other to provide a secured IoT system. Our main argument is that combining blockchain with AI can provide a robust solution for securing IoT networks and safeguarding the privacy of IoT users. This survey paper aims to provide a comprehensive understanding of the potential of these technologies for securing IoT networks and discuss the challenges and opportunities associated with their integration. It also provides a discussion on the current state of research on this topic and presents future research directions in this area.
Bitcoin is a pseudonymous, decentralised cryptocurrency that has become one of the most widely utilised digital assets to date. Because of its uncontrolled nature and Bitcoin users' inherent anonymity, it has seen a significant surge in its use for illegal operations. This necessitates the use of unique methods for categorising the addresses of Bitcoin users. This research classifies and predicts the portion of users' activities that are lawful and unlawful on the Bitcoin blockchain. The dataset contains almost 27 billion samples that are divided into nine user acts, five of which were unlawful. To predict cross-validation (CV) accuracy, ensemble learning algorithms are trained and tested. With cross-validation accuracy of 68.63% and 49.64%, respectively, gradient boosting emerged as the best ensemble learning algorithm for classification and prediction, while bagging emerged as the worst. To get the best classification and prediction, hyperparameter tuning is used to find the optimal parameters, which helped to enhance the cross-validation accuracy of the bagging algorithm to 67.70%, with moderate improvements in the rest of the learning algorithms.
Accelerated technological development has brought many novelties, among which is distributed ledger technology (DLT), often called blockchain (BC). BC is perceived as a peer-to-peer distributed immutable ledger that could revolutionize economies, societies and even our daily lives. All protocols for dealing with data and transactions are coded with an algorithm, so there is no need to trust the other contracting party or the intermediary. With the concept of decentralization and the absence of hierarchy, BC wants to avoid all traditional intermediaries and any regulation. The question arises, are BCtechnologies really decentralized and who controls them? What are the consequences if decision-making in BC is influenced by small groups of people or corporations? This article, in an attempt to answer these questions, explores technological scandals in which there have been significant deviations from the basic principles of BC (The DAO Hack, Parity's Smart Contract Bug on Ethereum and Facebook's Libra). Analysis of the above scandals suggests that decentralization is threatened and the current regulatory status of BC is substandard. It was shown that BC technology, due to its deterministic nature, cannot provide solutions for all life situations and that human judgment is irreplaceable.
This study aims to explore the principle of the interdependence and mutual influence of the Dark web and cryptocurrency. Specifically, it explores the terms "the Dark web" and "cryptocurrency", as well as distinguishes cryptocurrency from normal currency. Furthermore, it establishes a connection between the Dark web and cryptocurrency. In order to reach the aim of this study, multiple sources of literature had been analyzed, including books and online articles. The analysis of literature shows that there is a strong correlation between the existence of the Dark web and demand on cryptocurrency. Thus, the results of this paper conclude that the Dark web and cryptocurrency exist in symbiosis - cryptocurrency allows the Dark web to exist for its anonymity and impunity, while the Dark web guarantees demand on cryptocurrency.
This article discusses the growing concerns regarding the convergence of virtual currencies and mainstream finance, which is leading to an increase in illicit activities such as money laundering and terrorism financing. The challenges that law enforcement faces in addressing these crimes are exacerbated by limited technological expertise and a sense of impunity among perpetrators. The article highlights successful asset recovery cases involving crypto assets in the United States and the extension of anti-money laundering laws to virtual assets in the United Kingdom and India. While advanced jurisdictions are making progress in addressing these challenges, the article emphasizes the need for policy recommendations and best practices, particularly for jurisdictions in Africa, which is experiencing rapid growth in the crypto market. It also delves into potential avenues for collaboration between the European Union (EU) and India in addressing capacity deficiencies in developing or least developed countries. The cybersecurity practices and frameworks employed by both European and Indian entities may serve as instructive models for developing and least developed countries to combat terrorism financing with virtual assets.
Muhammad Saad, Maaz Bin Ahmad, Muhammad Asif, Muhammad Khalid Khan · 7 authors
Pandemics have always been a nightmare for humanity, especially in developing countries. Forced lockdowns are considered one of the effective ways to deal with spreading such pandemics. Still, developing countries cannot afford such solutions because these may severely damage the country’s economy. Therefore, this study presents the proactive technological mechanisms for business organizations to run their standard business processes during pandemic-like situations smoothly. The novelty of this study is to provide a state-of-the-art solution to prevent pandemics using industrial internet of things (IIoT) and blockchain-enabled technologies. Compared to existing studies, the immutable and tamper-proof contact tracing and quarantine management solution is proposed. The use of advanced technologies and information security is a critical area for practitioners in the internet of things (IoT) and corresponding solutions. Therefore, this study also emphasizes information security, end-to-end solution, and experimental results. Firstly, a wearable wristband is proposed, incorporating 4G-enabled ultra-wideband (UWB) technology for smart contact tracing mechanisms in industries to comply with standard operating procedures outlined by the world health organization (WHO). Secondly, distributed ledger technology (DLT) omits the centralized dependency for transmitting contact tracing data. Thirdly, a privacy-preserving tracing mechanism is discussed using a public/private key cryptography-based authentication mechanism. Lastly, based on geofencing techniques, blockchain-enabled machine-to-machine (M2M) technology is proposed for quarantine management. The step-by-step methodology and test results are proposed to ensure contact tracing and quarantine management. Unlike existing research studies, the security aspect is also considered in the realm of blockchain. The practical implementation of the proposed solution also obtains the results. The results indicate the successful implementation of blockchain-enabled contact tracing and isolation management using IoT and geo-fencing techniques, which could help battle pandemic situations. Researchers can also consider the 5G-enabled narrowband internet of things (NB-IoT) technologies to implement contact tracing solutions.
A secured platform is a critical component of digital governance, as it helps to ensure the privacy, security, and reliability of the electronic platforms and systems used to manage and deliver public services. Interoperability and data exchange are essential for digital governance, as they enable different government agencies and departments to share data, information, and resources seamlessly, regardless of the platforms and technologies they use. In this paper, we build a secure platform to enhance the trustworthiness of digital governance interoperability and data exchange using blockchain and deep learning-based frameworks. Initially, an optimal blockchain leveraging approach is designed using the bonobo optimization algorithm to authenticate data generated from smart city environments. Furthermore, we introduce the integration of a lightweight Feistel structure with optimal operations to enhance privacy preservation. This integration provides two levels of security and ensures interoperability and double-secured data exchange in digital governance systems. In addition, we utilize a deep reinforcement learning (DRL) model to detect and prevent intrusions such as fraud/corruption in the smart city data. This approach enhances transparency and accountability in accessing the data and shows its predominance over other cutting-edge techniques on two benchmark datasets, BoT-IoT and ToN-IoT. Furthermore, the effectiveness of the framework in real-time scenarios has been demonstrated through two case studies. Overall, our proposed framework provides a trustworthy platform for digital governance, interoperability, and data exchange, addressing the challenges of privacy, security, and reliability in managing and delivering public services.