Abstract In shaping the Internet of Money, the application of blockchain and distributed ledger technologies (DLTs) to the financial sector triggered regulatory concerns. Notably, while the user anonymity enabled in this field may safeguard privacy and data protection, the lack of identifiability hinders accountability and challenges the fight against money laundering and the financing of terrorism and proliferation (AML/CFT). As law enforcement agencies and the private sector apply forensics to track crypto transfers across ecosystems that are socio-technical in nature, this paper focuses on the growing relevance of these techniques in a domain where their deployment impacts the traits and evolution of the sphere. In particular, this work offers contextualized insights into the application of methods of machine learning and transaction graph analysis. Namely, it analyzes a real-world dataset of Bitcoin transactions represented as a directed graph network through various techniques. The modeling of blockchain transactions as a complex network suggests that the use of graph-based data analysis methods can help classify transactions and identify illicit ones. Indeed, this work shows that the neural network types known as Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) are a promising AML/CFT solution. Notably, in this scenario GCN outperform other classic approaches and GAT are applied for the first time to detect anomalies in Bitcoin. Ultimately, the paper upholds the value of public–private synergies to devise forensic strategies conscious of the spirit of explainability and data openness.
<p>Cybersecurity is an inherent characteristic that should be addressed before the large deployment of smart city applications. Recently, Blockchain appears as a promising technology to provide several cybersecurity aspects of smart city applications. This paper provides a comprehensive review of the existing blockchain-based solutions for the cybersecurity of the main smart city applications, namely smart healthcare, smart transportation, smart agriculture, supply chain management, smart grid, and smart homes. We describe the existing solutions and we discuss their merits and limits. Moreover, we define the security requirements of each smart city application and we give a mapping of the studied solutions to these defined requirements. Additionally, future directions are given. We believe that the present survey is a good starting point for every researcher in the fields of cybersecurity, blockchain, and smart cities.</p>
A service can be an intangible commodity in which no physical goods are transferred from the seller to the buyer. However, traditional trading platforms have many limitations in trading services due to dishonest buyers and brokers. In this paper, we propose a service trading ecosystem based on blockchain, named STEB, which combines blockchain, smart contract, encryption, and digital authentication techniques for service trading. In addition, a dual-chain architecture, which contains two types of blockchains, namely TraChain and SerChain, and a hierarchical encryption scheme of the data on the chain, are proposed to ensure the integrity of transaction data and fine-grained privacy protection of users. Furthermore, we describe a new set of smart contracts to ensure safe transactions for the entire service trading. Security analysis and simulation results confirm that the proposed STEB can achieve more efficient contract execution and enhance service transaction privacy.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Due to its significant global impact, both domestic and international efforts are underway to cure the infection and stop the COVID-19 virus from spreading further. In resource-limited environments, overwhelmed healthcare institutions and surveillance systems are struggling to cope with this epidemic, necessitating a specific strategic response. In this study, we looked into the COVID-19 situation and to establish trust, accountability, and transparency, we employed blockchain's immutable and tamper-proof properties. We offered a smart contract (SC)-based solution (Block-HPCT) that has been successfully tested to preserve a digital health passport (DHP) for vaccine recipients; also, for contact tracing (CT) we employed proof of location concept, which aids in a swift and credible response directly from the appropriate healthcare authorities. To connect on-chain and off-chain data, trusted and registered oracles were integrated and to provide a double layer of security along with symmetric key encryption; both Interplanetary File System (IPFS) and Hyperledger Fabric were merged as storage center. We also provided a full description of the suggested solution's system design, implementation, experiment results, and evaluation (privacy and cost analysis). As per the findings, the suggested approach performed satisfactorily across all significant assessment criteria, implying that it can lead the way for practical implementations and also can be used for similar types of situations where contact tracing of infectious can be crucial.
Gibran Gómez, Pedro Moreno-Sánchez, Juan Antonio Caballero-Hernández
Cybercriminals often leverage Bitcoin for their illicit activities. In this work, we propose back-and-forth exploration, a novel automated Bitcoin transaction tracing technique to identify cybercrime financial relationships. Given seed addresses belonging to a cybercrime campaign, it outputs a transaction graph, and identifies paths corresponding to relationships between the campaign under study and external services and other cybercrime campaigns. Back-and-forth exploration provides two key contributions. First, it explores both forward and backwards, instead of only forward as done by prior work, enabling the discovery of relationships that cannot be found by only exploring forward (e.g., deposits from clients of a mixer). Second, it prevents graph explosion by combining a tagging database with a machine learning classifier for identifying addresses belonging to exchanges. We evaluate back-and-forth exploration on 30 malware families. We build oracles for 4 families using Bitcoin for C&C and use them to demonstrate that back-and-forth exploration identifies 13 C&C signaling addresses missed by prior work, 8 of which are fundamentally missed by forward-only explorations. Our approach uncovers a wealth of services used by the malware including 44 exchanges, 11 gambling sites, 5 payment service providers, 4 underground markets, 4 mining pools, and 2 mixers. In 4 families, the relations include new attribution points missed by forward-only explorations. It also identifies relationships between the malware families and other cybercrime campaigns, highlighting how some malware operators participate in a variety of cybercriminal activities.
Saroj Gopali, Zulfiqar Ali Khan, Bipin Chhetri, Bimal Karki · 5 authors
Various decentralized applications have deployed millions of smart contracts (SCs) on the Blockchain networks. SCs enable programmable transactions involving the transfer of monetary assets between peers on a Blockchain network without any need to a central authority. However, similar to any software program, SCs may contain security issues. Software se-curity engineers and researchers have already uncovered several Ethereum BlockChain and SC vulnerabilities. Still, researchers continuously discover many more security flaws in deployed SCs. Indeed, the popularity of SCs attracts adversaries to launch new attack vectors. Thus, efficient vulnerability detection is necessary. This paper lists broad known vulnerabilities in SCs and classifies them based on the multi-class categories such as Suicidal, Prodigal, Greedy, and Normal SCs. The paper adopts artificial recurrent neural network architecture such as Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) used in deep learning to identify and then classify vulnerable Scs.
The financial property of Ethereum makes smart contract attacks frequently bring about tremendous economic loss. Method for effective detection of vulnerabilities in contracts imperative. Existing efforts for contract security analysis heavily rely on rigid rules defined by experts, which are labor-intensive and non-scalable. There is still a lack of effort that considers combining expert-defined security patterns with deep learning. This paper proposes EtherGIS, a vulnerability detection framework that utilizes graph neural networks (GNN) and expert knowledge to extract the graph feature from smart contract control flow graphs (CFG). To gain multi-dimensional contract information and reinforce the attention of vulnerability-related graph features, sensitive EVM instruction corpora are constructed by analyzing EVM underlying logic and diverse vulnerability triggering mechanisms. The characteristic of nodes and edges in a CFG is initially confirmed according to the corpora, generating the corresponding attribute graph. GNN is adopted to aggregate the whole graph's attribute and structure information, bridging the semantic gap between low-level graph features and high-level contract features. The feature representation of the graph is finally input into the graph classification model for vulnerability detection. Furthermore, automated machine learning (AutoML) is adopted to automate the entire deep learning process. Data for this research was collected from Ethereum to build up a dataset of six vulnerabilities for evaluation. Experimental results demonstrate that EtherGIS can productively detect vulnerabilities in Ethereum smart contracts in terms of accuracy, precision, recall, and F1-score. All aspects outperform the existing work.
Mohammad Monirujjaman Khan, Nesat Tasneem RoJa, Faris A. Almalki, Maha Aljohani
The days of storing data manually are behind us. We are opting for the online form of data storage and transfer. The new era of data digitization comes with its own perks and detriments. Cybersecurity is still a crucial concern today. As more data transfer occurs through an online medium, the risks of a breach and cyberattacks are inevitable. The whole foundation of e-commerce is based on the online transfer of goods and transactions without the need to travel. Transferring transactional data and transactions in e-commerce are prone to cyber threats. Our research’s major objective is to develop a system that protects against such mishaps, especially during the transfer of transactional data, and also implement an automated system that ensures these transactions occur without any errors. To implement this, we are taking advantage of new emerging technologies called blockchain and smart contract. Blockchain allows a decentralized, immutable digital ledger to safely store and transfer data across the network. Blockchain technology is used in e-commerce to transfer transactions in a safe, secure, and faster way. Blockchain enables a peer-to-peer transaction system and data encryption that enables the safe transfer of transactional data. Blockchain is used to transfer transactional data. A smart contract is a special program that enables, verifies, and enforces the terms of a contract digitally. It provides transactional security as the contact is in place. The blockchain, coupled with smart contracts, will revolutionize the future of e-commerce. We have combined blockchain technology to ensure data security and user privacy with smart contracts to ensure that the protocol for the transaction is maintained. The results are presented by building and implementing the proposed system that provides the solution for transactional data privacy.
Eugene B. Chang, Paul J. Darcy, Kim‐Kwang Raymond Choo, Nhien‐An Le‐Khac
Cryptocurrency has been (ab)used to purchase illicit goods and services such as drugs, weapons and child pornography (also referred to as child sexual abuse materials), and thus mobile devices (where cryptocurrency wallet applications are installed) are a potential source of evidence in a criminal investigation. Not surprisingly, there has been increased focus on the security of cryptocurrency wallets, although forensic extraction and attribution of forensic artefacts from such wallets is understudied. In this paper, we examine Bitcoin and Dogecoin. The latter is increasingly popular partly due to endorsements from celebrities and being positioned as an introductory path to cryptocurrency for newcomers. Specifically, we demonstrate how one can acquire forensic artefacts from Android Bitcoin and Dogecoin cryptocurrency wallets, such as wallet IDs, transaction IDs, timestamp information, email addresses, cookies, and OAuth tokens.
This paper investigates the cryptocurrency giveaway scam with the YouTube live stream carried out on 5/15/2022 and 5/16/2022. In this scam scheme, the scammer plays a recorded video of a famous person in a YouTube live stream annotated with a cryptocurrency giveaway announcement. In the annotated announcement, the victims are directed to the scammer's webpage. The scammer's webpage is designed intelligently to deceive victims such that they believe the legitimacy of the giveaway. The scammer claims that whatever donation the victim sends to a cryptocurrency wallet address, the giveaway scheme will double the donated amount and immediately send it back to the victim. By analyzing the scammers' wallet addresses, it can be seen that scammers could steal a significant amount of money in a short time. After analyzing the attackers' techniques, tactics, and procedures, this paper discusses the countermeasures that can be applied to mitigate such a fraudulent activity in the future.
The distributed and decentralized property of Blockchain technologies is very much suitable for those applications where integrity, confidentiality and proper tracking of operations is at highest priority. In this chapter, a Blockchain-based secure evidence management system for tracking police complaints and forensic reports is proposed. The proposed system provides security and privacy preservation of important documents used as evidence in the police system. The proposed scheme provides a Ethereum-based DApp to track a police complaint starting from FIR to when the complaint gets resolved, and also to provide security to forensic reports as security, confidentiality and integrity are at the highest priority. The proposed application supports forensic investigation with authenticity, immutability, traceability, resilience and distributed trust between evidential entitles. Details of tracking police complaints, further investigation, forensic report preservation and analysis will be recorded in chains of block. The proposed system is validated through various experimental results for security, privacy and sensitivity of important documents used as evidence by a police department.
May 20, 2022·2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA)
Smart contract is the key core part of the blockchain’s operation, which has the feature that it cannot be changed after deploying, resulting in the fact that the vulnerabilities associated with smart contracts cannot be fixed. Currently, deep learning has the best performance for detecting smart contract vulnerabilities. However, the common deep learning method treats the smart contract source code as a text sequence, ignoring the running logic and data flow of the program, and can not extract the constructed features. In this paper, a modular vulnerability detection model Link-DC is proposed. the model takes the contract graph constructed by smart contracts and the pattern features defined by expert knowledge as the original data input to capture richer feature information through stitching. Then, the deep and cross network is adopted to construct low-dimensional and sparse features into high-dimensional nonlinear features, thus improving the performance and efficiency of detection. Extensive experiments are conducted on smart contracts with vulnerabilities on the Ether and VNTChain platforms. The experiment results show that the high detection accuracy performance of the Link-DC model is obtained, where the accuracy of detection of re-entry vulnerability, timestamp-dependent vulnerability, and infinite loop vulnerability reaches 94.37%, 92.11%, and 85.29%, respectively.
The current practice of informal cyber threat intelligence (CTI) sharing among organizations is characterized with the use of emails and social media exchanges among individuals. This model is highly subjective and dependent on a specific individual’s social networks. Financial institutions have CTI at their disposal that could protect each other from computer hacks and fraud. The effective sharing of this intelligence among financial institutions could reduce the high income leakages that is brought about by cyber-attacks. The challenge is how to share this intelligence confidentially and anonymously since the financial institutions are competitors, have a huge reputation to protect and thrive on business secrecy. This research proposes a new way of sharing cyber threat intelligence by using Ethereum smart contract blockchain technology. This will be accomplished by hashing the device identity and replacing the hashed device identity by an on-chain verifiable random function to protect the identity of the participating nodes or the financial institutions in the blockchain network when passing information.
Security and privacy are two conditions that are closely linked to current trends in cryptocurrency, and this study offers a similar comprehensive review. Cryptocurrency adds security to transactions and controls the formation of additional currency units. Great growth for cryptocurrency market testing leads to the misuse of failures to benefit enemies. In this study, the review was designed to focus on safety and security standards of cryptocurrencies especially in Bitcoin. This study explains cryptocurrency agreements, their benefits, and communications within the framework
In recent years, a new form of cyber-extortion called ransomware is baffling the world with the speculation of connection to terrorist activities. Since cybercriminals usually demand their ransom using Bitcoin, Bitcoin has also become a part of this speculation. Unfortunately, however, all of the stories are based on anecdotal evidence. Therefore, the current study explores the dynamic properties of ransomware attacks, Bitcoin prices, and terrorist activities by connecting two opportunity-based theory frameworks: The Routine Activity Theory (RAT) and Cyber-Routine Activity Theory (Cyber-RAT). The findings of the study indicated unidirectional ties between the prevalence of ransomware and Bitcoin as well as ties between the prevalence of ransomware and terrorist activities. Several policies and prevention strategies from the overall findings of interrelated relations among three events are also presented.
In the context of the rapid development of blockchain technology, smart contracts have also been widely used in the Internet of Things, finance, healthcare, and other fields. There has been an explosion in the number of smart contracts, and at the same time, the security of smart contracts has received widespread attention because of the financial losses caused by smart contract vulnerabilities. Existing analysis tools can detect many smart contract security vulnerabilities, but because they rely too heavily on hard rules defined by experts when detecting smart contract vulnerabilities, the time to perform the detection increases significantly as the complexity of the smart contract increases. In the present study, we propose a novel hybrid deep learning model named CBGRU that strategically combines different word embedding (Word2Vec, FastText) with different deep learning methods (LSTM, GRU, BiLSTM, CNN, BiGRU). The model extracts features through different deep learning models and combine these features for smart contract vulnerability detection. On the currently publicly available dataset SmartBugs Dataset-Wild, we demonstrate that the CBGRU hybrid model has great smart contract vulnerability detection performance through a series of experiments. By comparing the performance of the proposed model with that of past studies, the CBGRU model has better smart contract vulnerability detection performance.
The complexity of blockchain technologies is reflected in the various ways in which social actors operate with crypto transactions, and this is seen in the wide range of discourses and practices adopted on the crypto market. Using social constructivism, this study highlights the fact that social practices in the crypto area respect certain thematic ideologies, through which individual discourses reflect different pragmatic expectations in relation to virtually encrypted transactions. Drawing on inductive thematic analysis based on 38 semi-structured interviews with Romanian investors, four major themes emerge: (1) unbreakable power of code encompasses rather libertarian discourses on potential state policies, (2) key role of peers explains the investments in the crypto market as a form of community membership, (3) assuming plenty of risks highlights the discourses focused on substantial and unpredictable gains, and (4) preferring safety above all encompasses discourses on financial stability through rather conservative behaviors.
To address the challenge of representing ransomware-cryptocurrency payments, this article outlines a novel approach to the extraction and sharing of threat intelligence data from the Bitcoin blockchain. This work results in the creation of two new cyber-observable objects, x-cryptocurrency-address, and x-cryptocurrency-transaction.
S. K. UmaMaheswaran, Dipesh Uike, K. K. Ramachandran, A Tharangini · 6 authors
The universal blockchain consensus mechanism was then examined, consists of a review of its features, highlighting benefits and drawbacks. Following that, we offered a taxonomic taxonomy as well as a detailed evaluation of previous approaches and approaches that employ machine learning (ML) approaches to address common security issues and abnormal behaviours in Bitcoin systems and cryptocurrencies. Furthermore, researchers discussed several unanswered research questions and potential research areas, as well as some closing thoughts. This paper evaluates the present degree of awareness on the many types of cryptocurrency crime that persist today or may occur in the future, as well as full explanations of the scams found. It is unknown how useful cryptocurrencies will be in the long term as terrorist techniques and cryptocurrencies evolve. Unfortunately, some recent advancements in cryptocurrency will make it easier for the most skilled terrorist groups to utilise them to attack Developed democracies, and cryptocurrencies will be incredibly beneficial for people who currently participate in overseas fundraising and illegal operations. Machine learning mechanism is developed for detecting raising threats in cryptocurrency. The suggested approach comprises of a detection technique that is integrated into many Bitcoin entities. Prior to creating the initial analysers, the suggested method employs a capture mechanism to extract an antigen symbol from the activity. To collect data, survey or primary quantitative data is conducted.
In India, as per records, it has been observed that with each succeeding year, criminal activities are surging. Crime is an act executed against the law of constitution and is a menace to our society. Thus, crime and criminals need to be monitored conveniently. Unlike USA or other countries, in India, criminal records are not public, which means neither private organizations nor common people can access criminal records for employee verification. A centralized system, which gives access to criminal data, would be a feasible solution to this problem. This study aims to develop a Flutter-based blockchain-enabled permissionless system that would help in the remote access of criminal records. The system would be developed using Ethereum, which offers vivid features such as truffle for enabling Ethereum virtual environment, Ganache for personal testing of smart contracts, InterPlanetary file system for encrypted storage of data and easy tracking of files, and MetaMask, which acts as a digital wallet.
Bitcoin is a decentralized peer-to-peer (P2P) cryptocurrency system with an innovative payment network. For blockchain applications, ECDSA is formed with public and private keys, especially in Bitcoin, which uses an elliptic curve in a cryptography standard known as Secp256k1 to ensure funds are spent by legitimate owners. Despite the fact that ECDSA is a key component of Bitcoin transactions, today's criminals employ pseudonymous addresses, which make it impossible to track unlawful activity because they don't maintain real-world identities. Bitcoin Fog, Helix Mix, and other mixing services are supposed to provide transaction privacy. However, these services are commonly utilized for concealment, it is more difficult to track criminals. First, using time-frequency analysis, features can be recovered at the network, account, or transaction level to create attributed temporal heterogeneous network Motifs and characterize many forms of address patterns. To increase the performance of evaluation metrics, we introduced a decision-tree machine learning approach for the categorization of bitcoin mixing services from unlabeled addresses in this work. Experiments with bitcoin Kaggle datasets are frequently used to determine the success of our categorization approach.
Blockchain Technology Applications and Security
Cybercrime and Law Enforcement Studies
Advanced Steganography and Watermarking Techniques
Since the Industrial era, women are playing a significant role in the workforce to move the world forward. Their increasing contribution in various fields has earned a fortune for the global economy. Despite that, women constantly face more obstacles than men in the workplace. When half of the population are mistreated because of gender inequality, the economy of any nation is supposed to collapse. One of the biggest barriers for women in their careers is workplace harassment. Workplace harassment may include physical, verbal or nonverbal harassment that not only have an adverse effect on a woman's career, mental health and physical health but also organizational reputation. A common way to make a complaint in most organizations is to fill up a complaint form, email or go directly to the competent authority and complain. But victims often hesitate to complain because their identity might get revealed or their documentary evidence might be tampered. As a result, most of the harassers get through very easily. To resolve this problem, this paper presents a blockchain-based anonymous, transparent and secure platform where women can easily complain against their harassers. To keep the platform secure and reliable, a two-level hierarchical model is introduced, where level-1 is the Human Resources (HR) and level-2 is the Higher Authority. In level-1, victims can anonymously complain to HR and in Level-2, victims can complain with their identity revealed to higher authority. This way, the proposed platform ensures women of a healthy work environment and provides all necessary support to stand up against injustice in the workplace.