Christos Karapapas, Iakovos Pittaras, George C. Polyzos, Constantinos Patsakis
The InterPlanetary File System~(IPFS) offers a decentralized approach to file storage and sharing, promising resilience and efficiency while also realizing the Web3 paradigm. Simultaneously, the offered anonymity raises significant questions about potential misuse. In this study, we explore methods that malicious actors can exploit IPFS to upload and disseminate harmful content while remaining anonymous. We evaluate the role of pinning services and public gateways, identifying their capabilities and limitations in maintaining content availability. Using scripts, we systematically test the behavior of these services by uploading malicious files. Our analysis reveals that pinning services and public gateways lack mechanisms to assess or restrict the propagation of malicious content.
The proliferation of Internet of Things (IoT) devices has facilitated the exchange of information among individuals and devices. This development has introduced several challenges, including increased vulnerability to potential cyberattacks and digital forensics. IoT forensic investigations need to be managed in a forensically sound manner using a standard framework. However, adopting traditional digital forensics tools introduces various challenges, such as identifying all IoT devices and users at the crime scene. Therefore, collecting evidence from these devices is a major problem. This paper proposes a permissioned blockchain integration solution for IoT forensics (PBCIS-IoTF) that aims to observe data transactions within the blockchain. The PBCIS-IoTF framework designs and tests Hyperledger blockchains simulated with a Raspberry Pi device and chaincode to address the challenges of IoT forensics. This blockchain is deployed using multiple nodes within the network to avoid a single point of failure. The authenticity and integrity of the acquired evidence are analysed by comparing the SHA-256 hash metadata in the blockchain of all peers within the network. We further integrate webpage access with the blockchain to capture the forensics data from the userโs IoT devices. This allows law enforcement and a court of law to access forensic evidence directly and ensures its authenticity and integrity. PBCIS-IoTF shows high authenticity and integrity across all peers within the network.
Cryptocurrency has emerged as a lucrative yet volatile landscape for cybercriminal activity, presenting novel challenges for law enforcement and policymakers alike. This review seeks to explore the diverse array of cybercrimes occurring within the cryptocurrency domain, examining their types, motives, techniques, and the regulatory responses shaping this complex ecosystem. Utilizing a scoping literature search methodology, this study analyzes 228 pertinent sources drawn from a pool of over 4,000 reviewed publications. The findings elucidate the intricate interplay between cryptocurrencies and illicit activities, revealing the multifaceted nature of cybercrimes within this realm. From the exploitation of the dark web for illicit transactions to the pervasive threat of crypto ransomware targeting entities globally, the review underscores the diverse methods and motivations driving such nefarious endeavors. By shedding light on the evolving tactics employed by cybercriminals and exploring future directions for technological and regulatory measures adopted by governments, this paper offers valuable insights to navigate this dynamic landscape effectively.
WebAssembly (WASM) has emerged as a crucial technology in smart contract development for several blockchain platforms. Unfortunately, since their introduction, WASM smart contracts have been subject to several security incidents caused by contract vulnerabilities, resulting in substantial economic losses. However, existing tools for detecting WASM contract vulnerabilities have accuracy limitations, one of the main reasons being the coarse-grained emulation of the on-chain data APIs. In this article, we introduce WACANA, an analyzer for WASM contracts that accurately detects vulnerabilities through fine-grained emulation of on-chain data APIs. WACANA precisely simulates both the structure of on-chain data tables and their corresponding API functions, and integrates concrete and symbolic execution within a coverage-guided loop to balance accuracy and efficiency. Evaluations on a vulnerability dataset of 2,012 contracts show WACANA outperforming state-of-the-art tools in accuracy. Further validation on 5,602 real-world contracts confirms WACANAโs practical effectiveness.
Shabnam Fazliani, Mohammad Mowlavi Sorond, Arsalan Masoudifard
The advent of smart contracts has enabled the rapid rise of Decentralized Finance (DeFi) on the Ethereum blockchain, offering substantial rewards in financial innovation and inclusivity. This growth, however, is accompanied by significant security risks such as illicit accounts engaged in fraud. Effective detection is further limited by the scarcity of labeled data and the evolving tactics of malicious accounts. To address these challenges with a robust solution for safeguarding the DeFi ecosystem, we propose $\textbf{SLEID}$, a $\textbf{S}$elf-$\textbf{L}$earning $\textbf{E}$nsemble-based $\textbf{I}$llicit account $\textbf{D}$etection framework. SLEID uses an Isolation Forest model for initial outlier detection and a self-training mechanism to iteratively generate pseudo-labels for unlabeled accounts, enhancing detection accuracy. Experiments on 6,903,860 Ethereum transactions with extensive DeFi interaction coverage demonstrate that SLEID significantly outperforms supervised and semi-supervised baselines with $\textbf{+2.56}$ percentage-point precision, comparable recall, and $\textbf{+0.90}$ percentage-point F1 -- particularly for the minority illicit class -- alongside $\textbf{+3.74}$ percentage-points higher accuracy and improvements in PR-AUC, while substantially reducing reliance on labeled data.
The blockchain technology is fast becoming a crucible of e governance. It is pitched to safeguard privacy and is hard to tamper with as it works using the distributed ledger technology. The digital evidence is secured and strengthened respectively. The regulators are dealing with complex and immense volume of data. To deal with this challenge, one of the possible solutions is cloud computing. The use of blockchain in digital forensics has initiated nuanced understanding of evidence management. There are multiple storage and classification models related to blockchain along with algorithms that are helpful in data handling. This article is an analysis of the evolving models that use blockchain and algorithms. The purpose of this article is to analyse and suggest sustainable and pragmatic solutions for regulators. The algorithmic approaches discussed in this article are efficient in terms of time, cost, access and energy. However, to further the objectives of National Strategy on Blockchain, enhanced efforts are needed to secure digital evidence management.
There is a huge demand to ensure the compliance of smart contracts listed on blockchain platforms to safety and economic standards described in natural languages. Today, manual efforts in the form of auditing are commonly used to achieve this goal. ML-based automated techniques have the promise to alleviate human efforts and the resulting monetary costs. However, unlike other domains where ML techniques have had huge successes, no systematic ML techniques have been proposed or applied to smart contract auditing. We present SC-Bench, the first dataset for automated smart-contract auditing research. SC-Bench consists of 5,377 real-world smart contracts running on Ethereum, a widely used blockchain platform, and 15,975 violations of standards on Ehereum called ERCs. Out of these violations, 139 are real violations programmers made. The remaining are errors systematically injected by us to reflect the violations of different ERC rules. We evaluate SC-Bench using GPT-4 by prompting it with both the contracts and ERC rules. In addition, we manually identify each violated rule and the corresponding code site (i.e., oracle) and prompt GPT-4 with the information asking for a True-or-False question. Our results show that without the oracle, GPT-4 can only detect 0.9% violations, and with the oracle, it detects 22.9% violations. These results show the potential room for improvement in ML-based techniques for smart-contract auditing.
As digital crime continues to rise, the preservation of digital evidence has become a critical phase in digital forensic investigations. This phase focuses on securing and maintaining the integrity of evidence for legal proceedings. Existing solutions for evidence preservation, such as centralized storage systems and cloud frameworks, present challenges related to security and collaboration. In this paper, we propose a novel framework that addresses these challenges in the preservation phase of forensics. Our framework employs a combination of advanced technologies, including the following: (1) Segmenting evidence into smaller components for improved security and manageability, (2) Utilizing steganography for covert evidence preservation, and (3) Implementing blockchain to ensure the integrity and immutability of evidence. Additionally, we incorporate Long Short-Term Memory (LSTM) networks to enhance steganography in the evidence preservation process. This approach aims to provide a secure, scalable, and reliable solution for preserving digital evidence, contributing to the effectiveness of digital forensic investigations. An experiment using linguistic steganography showed that the LSTM autoencoder effectively generates coherent text from bit streams, with low perplexity and high accuracy. Our solution outperforms existing methods across multiple datasets, providing a secure and scalable approach for digital evidence preservation.
Open access
Advanced Steganography and Watermarking Techniques
Daniele Friolo, Geoffrey Goodell, D. R. Toliver, Hazem Danny Nakib
This article builds upon the protocol for digital transfers described by Goodell, Toliver, and Nakib, which combines privacy by design for consumers with strong compliance enforcement for recipients of payments and self-validating assets that carry their own verifiable provenance information. We extend the protocol to allow for the verification that reissued assets were created in accordance with rules prohibiting the creation of new assets by anyone but the issuer, without exposing information about the circumstances in which the assets were created that could be used to identify the payer. The modified protocol combines an audit log with zero-knowledge proofs, so that a consumer spending an asset can demonstrate that there exists a valid entry on the audit log that is associated with the asset, without specifying which entry it is. This property is important as a means to allow money to be reissued within the system without the involvement of system operators within the zone of control of the original issuer. Additionally, we identify a key property of privacy-respecting electronic payments, wherein the payer is not required to retain secrets arising from one transaction until the following transaction, and argue that this property is essential to framing security requirements for storage of digital assets and the risk of blackmail or coercion as a way to exfiltrate information about payment history. We claim that the design of our protocol strongly protects the anonymity of payers with respect to their payment transactions, while preventing the creation of assets by any party other than the original issuer without destroying assets of equal value.
Asset securitization is an important financial derivative involving complicated asset transfer operations. Therefore, digitizing traditional asset securitization contracts will improve efficiency and facilitate reliability verification. Furthermore, accurate and verifiable requirement description is essential for collaborative development between financial professionals and software engineers. A domain specific language for writing asset securitization contract has been proposed. This solves the problem of difficulty for financial professionals to directly write smart contract by simplifying writing rules. However, due to existing design of the language focused on some simple scenarios, it is insufficient and informal to describe various detailed scenarios. What is more, there are still many reliability issues, such as verifying the correctness of the logical properties of the contract and ensuring the consistency between the contract text and the contract code, within the language in the generation and execution of smart contracts. To overcome the challenges stated above, we extend, simplify and innovate the syntax subset of the domain specific language and name it AS-SC (Asset Securitization โ Smart Contract), which can be used by financial professionals to accurately describe requirements. Besides, because formal methods are math-based techniques that describe system properties and can generate programs in a more formal and reliable manner, we propose a semantic consistent code conversion method, named AS2EB, for converting from AS-SC to Event-B, a common and useful formal language. AS2EB method can be used by software engineers to verify requirements. The combination of AS-SC and AS2EB ensures consistency and reliability of the requirements, and reduces the cost of repeated communications and later testing. Taking the credit asset securitization contract as case study, the feasibility and rationality of AS-SC and AS2EB are validated. In addition, by carrying out experiments on three randomly selected real cases in different classic scenarios, we show high-efficiency and reliability of AS2EB method.
Tokens have proliferated across blockchains in terms of number, market capitalisation and utility. Some tokens are tokenised versions of existing tokens -- known variously as wrapped tokens, fractional tokens, or shares. The repeated application of this process creates matryoshkian tokens of arbitrary depth. We perform an empirical analysis of token composition on the Ethereum blockchain. We introduce a graph that represents the tokenisation of tokens by other tokens, and we show that the graph contains non-trivial topological structure. We relate properties of the graph, e.g., connected components and cyclic structure, to the tokenisation process. For example, we identify the longest directed path and its corresponding sequence of tokens, and we visualise the connected components relating to a stablecoin and an NFT protocol. Our goal is to explore and visualise what has been wrought with tokens, rather than add yet another brick to the edifice.
The Internet of Things (IoT) has been deployed in a vast range of applications with exponential increases in data size and complexity. Existing forensic techniques are not effective for the accuracy and detection rate of security issues in IoT forensics. Cyber forensic comprises huge volume constraints that are processing huge volumes of data in the Information and Communication Technology (ICT) comprised of IoT devices and platforms. Trust blockchain is effective technology those are utilized to assess the tamper-proof records in all transaction in the IoT environment. With the implementation of trust blockchain the record and transaction are processed with a distributed ledger that is managed by the network nodes. The challenge associated with the trust blockchain in IoT forensics is cost and security. To achieve significant cost-effectiveness organizations, need to evaluate the risks and benefits associated with IoT forensics in the trust blockchain technology. In this paper, developed a Block Chain Enabled Cyber-Physical system with distributed storage. The developed Blockchain model is termed as Integrated Hadoop Blockchain Forensic Machin Learning (IHBF-ML). The IHBF-ML model uses the Hadoop Distributed File System (HDFS) with cyberspace to improve security. Within the IHBF-ML model, IoT data communication is established with the smart contract. The smart contract-based blockchain process uses the Machine Learning model integrated with Cat Boost classification model for anomaly detection. Cost in IoT forensic is minimized with the parallel processing of the data through MapReduce Framework for the traffic translation, extraction, and analysis of the dynamic feature traffic from the IoT environment. The experimental analysis stated that constructed IHBF-ML model reduces the cost by ~25% than the other conventional blockchain Ethereum and EOS.
The domain of Digital Forensics for the Industrial Internet of Things (IIoT) and the proposed use of a Distributed Digital Ledger (DDL), has for the most part been theoretical in nature within the current literature. The work in this paper explores the practical feasibility of using DDL technology for Digital Forensics in the IIOT context. We detail a new methodology for testing the performance of writing to and reading from a DDL in an IIOT environment, and present findings on the overhead associated with storing and retrieving IIoT transactions in a DDL. We conclude that while it is possible to build and use a DDL for storing IIoT transactions, there are limitations to the number of sensors that can be supported by a single implementation and the time it takes to retrieve transactions may be too high to be practical for Digital Forensics.
S Mohammed Ishaq, Arpitha K M, Himanshu Choubey, Mohammed Azeemulla ยท 5 authors
In an increasingly computerized universe, the secure administration & trading of crypto files and documents has become a critical concern. In this estimate seeks to label this issue beside creating decentralized implementation (App) that uses distributed larger technology and deep neural network models to enable secure and efficient digital asset management, with an emphasis on NFTs. The App's features include secure wallet network, NFT picture production, stamp out, a sale out, and account handling. The App's backend is built on the Goerli development network with reliability intelligent contracts, while IPFS and ReactJS/Ethers are utilized for scattered storage and client development, individually. Furthermore, the Open AI Api is used to create unique NFT picture depends on customer input. This design showcases the actual application of distributed larger technology and deep neural network models for creating Apps for assured and scattered crypto asset management. Universal, the project adds to the continuing study on blockchain-based solutions for secure digital asset management, while also emphasizing the power of distributed larger technology and deep neural network models to change the way we handle and trade crypto assets.
The openness and transparency of Ethereum transaction data make it easy to be exploited by any entities, executing malicious attacks. The sandwich attack manipulates the Automated Market Maker (AMM) mechanism, profiting from manipulating the market price through front or after-running transactions. To identify and prevent sandwich attacks, we propose a cascade classification framework GasTrace. GasTrace analyzes various transaction features to detect malicious accounts, notably through the analysis and modeling of Gas features. In the initial classification, we utilize the Support Vector Machine (SVM) with the Radial Basis Function (RBF) kernel to generate the predicted probabilities of accounts, further constructing a detailed transaction network. Subsequently, the behavior features are captured by the Graph Attention Network (GAT) technique in the second classification. Through cascade classification, GasTrace can analyze and classify the sandwich attacks. Our experimental results demonstrate that GasTrace achieves a remarkable detection and generation capability, performing an accuracy of 96.73% and an F1 score of 95.71% for identifying sandwich attack accounts.
Mrs. D. Thamizhisai, S. Bharathi, U. Bhuvaneshwaran, S. Mervin Immanuvel ยท 5 authors
The incorporation of blockchain technology into forensic investigations represents a significant advancement, tackling critical challenges within the legal and criminal justice systems. Central to this integration are smart contracts, which automate and secure essential aspects of investigations. These self-executing agreements operate based on predefined rules, ensuring integrity and transparency in tasks such as evidence tracking, chain of custody management, and access control. A key advantage lies in the substantial enhancement of data security. Blockchain's cryptographic principles and decentralized structure make it highly resistant to unauthorized access and tampering, crucial in maintaining evidence integrity. Additionally, blockchain's immutability ensures the reliability of information; once recorded, data becomes virtually unalterable, providing an indisputable ledger of events. In summary, this innovative integration streamlines operations, reduces errors and disputes, and strengthens the trustworthiness of forensic investigations by offering an unforgeable and transparent chain of custody and evidence history within the legal and criminal justice framework.
Yepeng Ding, Arthur Gervais, Roger Wattenhofer, Hiroyuki Satล
Decentralized finance (DeFi) is revolutionizing the traditional centralized finance paradigm with its attractive features such as high availability, transparency, and tamper-proofing. However, attacks targeting DeFi services have severely damaged the DeFi market, as evidenced by our investigation of 80 real-world DeFi incidents from 2017 to 2022. Existing methods, based on symbolic execution, model checking, semantic analysis, and fuzzing, fall short in identifying the most DeFi vulnerability types. To address the deficiency, we propose Context-Sensitive Concolic Verification (CSCV), a method of automating the DeFi vulnerability finding based on user-defined properties formulated in temporal logic. CSCV builds and optimizes contexts to guide verification processes that dynamically construct context-carrying transition systems in tandem with concolic executions. Furthermore, we demonstrate the effectiveness of CSCV through experiments on real-world DeFi services and qualitative comparison. The experiment results show that our CSCV prototype successfully detects 76.25% of the vulnerabilities from the investigated incidents with an average time of 253.06 seconds.
The blockchain ecosystem, particularly with the rise of Web3 and Non-Fungible Tokens (NFTs), has experienced a significant increase in users and applications. However, this expansion is challenged by the need to connect early adopters with a wider user base. A notable difficulty in this process is the complex interfaces of blockchain wallets, which can be daunting for those familiar with traditional payment methods. To address this issue, the category of "embedded wallets" has emerged as a promising solution. These wallets are seamlessly integrated into the front-end of decentralized applications (Dapps), simplifying the onboarding process for users and making access more widely available. However, our insights indicate that this simplification introduces a trade-off between ease of use and security. Embedded wallets lack transparency and auditability, leading to obscured transactions by the front end and a pronounced risk of fraud and phishing attacks. This paper proposes a new protocol to enhance the security of embedded wallets. Our VELLET protocol introduces a wallet verifier that can match the audit trail of embedded wallets on smart contracts, incorporating a process to verify authenticity and integrity. In the implementation architecture of the VELLET protocol, we suggest using the Text Record feature of the Ethereum Name Service (ENS), known as a decentralized domain name service, to serve as a repository for managing the audit trails of smart contracts. This approach has been demonstrated to reduce the necessity for new smart contract development and operational costs, proving cost-effective through a proof-of-concept. This protocol is a vital step in reducing security risks associated with embedded wallets, ensuring their convenience does not undermine user security and trust.
Archana B, Adithya Baragi S, K. N. Anusha, Jeevan Basri B S ยท 5 authors
Evidence management is crucial in the field of forensic science. Evidence obtained from a crime scene is important in solving the case and delivering justice to the victim involved. Hence, protecting the integrity of the evidence throughout the process is of prime importance. Chain of Custody (CoC) is the process which maintains the integrity of the evidence using Blockchain Technology. Inability to maintain the chain of custody will make the evidence inadmissible in court, eventually leading to the case dismissal. Digitalization of forensic evidence management system is a need of time as it is an environment friendly model. Blockchain are digitally distributed ledgers of transactions signed cryptographically in chronological order that are sorted into blocks and is completely open to anyone in the blockchain network. Present study aims to create a framework and further propose an algorithm to implement blockchain technology to digitalize forensic evidence management system and maintain Chain of Custody
Abstract: As a consequence of mass unemployment being the byproduct of COVID-19, people around the world discovered investment in cryptocurrency as a means to tackle their declining financial condition. Subsequently, the prominence of Ethereum as a platform for crypto transactions also gave rise to fraudulent transactions. The need to detect these frauds exists even today. This study proposes a token-based approach to detect fraud in Ethereum transactions incorporating the ERC20 standard, by employing machine learning techniques. After cleaning and preprocessing of the dataset, the transaction data was fed to Random Forest (RF), AdaBoost, Extra Trees (ET), Gradient Boosting (GB) and Extreme Gradient Boosting (XGB) classifiers in search of the most suitable model for fraud detection. Meticulous evaluation revealed that RF, ET and XGB classifiers yielded the highest accuracy of 95%. The proposed token-based approach hence presents a novel and efficient solution for fraud detection, with room for improvement and scalability.
P. Maragathavalli, Aravindhar RS, R Keerthana, M. Harini ยท 5 authors
Cybercrime gives challenges to law enforcement agencies to secure digital evidence and maintain its integrity. Blockchain known for its decentralized and immutable nature, provides a secure ledger to record digital evidence transactions restricting unauthorized access. This project proposes a framework for digital forensic evidence management, contributing to the enhancement of security and reliability in digital forensic practices through the utilization of Ethereum Blockchain technology and Advanced Encryption Standard (AES) encryption. Through a systematic review, various studies, methodologies, and implementations employing Blockchain to safeguard digital evidence are explored. Blockchain, known for its decentralized and immutable nature, provides a secure ledger to record digital evidence transactions, restricting unauthorized access. Advanced Encryption Standard (AES) algorithm ensures that the data stored on the blockchain remains tamper-resistant and secure. In the blockchain ecosystem, Proof of Stake (POS) plays a critical role by facilitating transaction validation and block creation. It distinguishes itself by selecting validators based on the amount of cryptocurrency they 'stake' or pledge as collateral, offering an energy-efficient and environmentally sustainable alternative to the traditional method.
In the domain of computer forensics, ensuring the integrity of operations like preservation, acquisition, analysis, and documentation is critical. Discrepancies in these processes can compromise evidence and lead to potential miscarriages of justice. To address this, we developed a generic methodology integrating each forensic transaction into an immutable blockchain entry, establishing transparency and authenticity from data preservation to final reporting. Our framework was designed to manage a wide range of forensic applications across different domains, including technology-focused areas such as the Internet of Things (IoT) and cloud computing, as well as sector-specific fields like healthcare. Centralizing our approach are smart contracts that seamlessly connect forensic applications to the blockchain via specialized APIs. Every action within the forensic process triggers a verifiable transaction on the blockchain, enabling a comprehensive and tamper-proof case presentation in court. Performance evaluations confirmed that our system operates with minimal overhead, ensuring that the integration bolsters the judicial process without hindering forensic investigations.
Cryptocurrency tracker is an online platform that provides a userfriendly experience. Users get a simple and userfriendly experien ce through the user interface. Users can sign into their account with Gmail or a mobile number for easy access to their account. U sers can track prices of different cryptocurrencies and view currency charts. Using this user interface, users can find prices and ot her relevant information about cryptocurrencies. The app helps users to create watchlists and we can track prices. We can set alerts for cryptocurrency prices. We can customize notifications and help understand new cryptocurrency trends. Users can easily find various cryptocurrencies and track future crypt currency trends. It helps users invest in new popular cryptocurrencies that will be more useful to them in the future. Overall, the Cryptocurrency Tracker web app is a valuable tool for anyone looking to invest, trade, or just keep an eye on the cryptocurrency market. It provides realtime data and insights that can help users make informed investment decisions and stay abrea st of the latest industry trends and developments.
Cryptocurrencies are crucial in modern commerce and finance, whether at the national, corporate, or individual level. They serve as fundamental currencies for buying and selling, enabling various business transactions. However, the rise of cybercrime has brought about concerns regarding their operations, potential breaches in encrypted currencies, and the security systems managing them. The frequency of attack tactics and the motivation of attackers seeking financial gain are well-known. Many cryptocurrencies lack the necessary algorithms, techniques, and knowledge to effectively detect and mitigate malware, making them vulnerable targets for hackers. In this study, machine learning techniques are employed to detect malicious code in digital currencies. Additionally, a comparison of these techniques is conducted to determine the most suitable algorithm and technology, Furthermore, this study highlights the importance of effective malware detection in securing cryptocurrencies. Three datasets of different sizes were used, each yielding distinct results based on dataset size. The AdaBoost model demonstrated superior performance when applied to the short dataset, while the decision tree model performed best with the medium-sized dataset. Conversely, the Naive Bayes model consistently produced the worst results, while the large-size KNN model achieved the highest performance.