Smart contracts are susceptible to various vulnerabilities that can lead to significant financial losses. The usage of tools for vulnerabilities is reducing the threats but presents some limitations related to the approach used by the tool itself. This paper presents a novel approach to smart contract vulnerability detection utilizing Large Language Models (LLMs), as a tool to detect all the vulnerabilities at once. Our proposed tool leverages the advanced natural language processing capabilities of LLMs to analyze smart contract code and identify potential security flaws. By training the LLM on a diverse dataset of known smart contract vulnerabilities and secure coding practices, we enhance its ability to recognize subtle and complex vulnerabilities that traditional static analysis tools might miss. The evaluation of our tool demonstrates its effectiveness in detecting a wide range of vulnerabilities with satisfaction and accuracy, providing developers with a robust mechanism to improve the security of their smart contracts before deployment. This approach signifies a significant advancement in the application of artificial intelligence for blockchain security, highlighting the potential of LLMs to enhance the reliability and safety of decentralized applications.
S. Harihara Gopalan, A. Manikandan, N. P. Dharani, G. Sujatha
Abstract The proposed Blockchain-Based Mitigation of Deauthentication Attacks (BBMDA) Framework aims to enhance the security and trustworthiness of IoT environments by leveraging blockchain technology, the Elliptic Curve Digital Signature Algorithm (ECDSA) for secure authentication, and Multi-Task Transformer (MTT) for efficient traffic classification. This paper presents a novel approach to mitigate de-authentication attacks in IoT ecosystems. The research methodology involves developing and implementing the BBMDA framework, followed by a comprehensive evaluation and comparison with existing techniques. Key findings indicate that the BBMDA framework outperforms traditional methods such as Support Vector Machine (SVM), k-nearest Neighbors (KNN), and Convolutional Neural Network (CNN) in terms of accuracy, false positive rate, false negative rate, precision, recall, and F1-score. These results underscore the effectiveness and efficiency of the proposed framework in enhancing IoT security.
Izdehar M. Aldyaflah, Wenbing Zhao, Shunkun Yang, Xiong Luo
Stemming vulnerabilities out of a smart contract prior to its deployment is essential to ensure the security of decentralized applications. As such, numerous tools and machine-learning-based methods have been proposed to help detect vulnerabilities in smart contracts. Furthermore, various ways of encoding the smart contracts for analysis have also been proposed. However, the impact of these input methods has not been systematically studied, which is the primary goal of this paper. In this preliminary study, we experimented with four common types of input, including Word2Vec, FastText, Bag-of-Words (BoW), and Term Frequency–Inverse Document Frequency (TF-IDF). To focus on the comparison of these input types, we used the same deep-learning model, i.e., convolutional neural networks, in all experiments. Using a public dataset, we compared the vulnerability detection performance of the four input types both in the binary classification scenarios and the multiclass classification scenario. Our findings show that TF-IDF is the best overall input type among the four. TF-IDF has excellent detection performance in all scenarios: (1) it has the best F1 score and accuracy in binary classifications for all vulnerability types except for the delegate vulnerability where TF-IDF comes in a close second, and (2) it comes in a very close second behind BoW (within 0.8%) in the multiclass classification.
Smart contracts have emerged as a promising and innovative technology to enhance cyber-security incident response. With the rise of cyber-security incidents, traditional incident response systems often face challenges in terms of efficiency, transparency and reliability. Smart contracts based on blockchain technology provide a way using circuits and an automated approach to streamline incident response activities. This paper examines the potential of smart contracts in cyber- security incident response, discussing their benefits and challenges. Highlight key features of smart contracts that enable safe and effective incident response, such as automated incident reporting, evidence protection, integrated response actions and, in this case, potential limitations and security considerations of smart contracts were addressing. Overall, smart contracts have the potential to transform cyber-security incident response by providing a secure and transparent framework for industry collaboration and automation
[Purpose] This article addresses the lack of legislation in India to govern digital currencies, as well as the legality of Bitcoin in comparison to Russia's ban on other digital assets for payment regulation, highlighting the necessity for effective legislation once all around the world. It further delves into potential misuse of private cryptocurrencies in a variety of ways while placing upon the need for both countries to form a new digital currency regulatory authority. [Methodology/Approach/Design] This paper probes into the existing legal regulations for Cryptocurrency in India, Russia and other countries by using primary and secondary data throughout the paper. The primary data have been taken from legitimate government sources such as Russia's federal law and other bills and laws enacted in India, such as the "Banning of Cryptocurrency and Regulation of Official Digital Currency Bill, 2019" and "The Cryptocurrency and Regulation of Official Digital Currency Bill, 2021." Various judgments like the case of Internet and Mobile Association of India v. Reserve Bank of India and Dwaipayan Bhowmick v. Union Of India and Ors. have been taken out from Manupatra which is an online database for legal research. Government reports and notifications from the Indian, Russian and US government have also been examined. The secondary data include numerous news articles from Times of India, The Mirror, India Times, The Moscow Times, Telegraph, The UK News and other new sites. Lastly, articles by various researchers like Bohme et al, Dyhrberg and Kim have also been thoroughly analysed. [Findings] It was concluded that legalising Cryptocurrency through codified laws, appropriate approval for digital currencies through regulatory authorities, establishing clear definition of ‘cryptocurrency’, uniform taxation for all types of Cryptocurrencies, updation of penal laws and setting imprisonment for cryptocurrency regulation violation seem to be some effective solutions to reignite the Indian and Russian Economies. [Practical Implications] The practical implication lies in the fact that the use of cryptocurrencies is increasing on a daily basis, but neither the national government nor the world organizations has made any steps to control the market for virtual currencies.
Criminals, using crypto wallets referred to as Deceptive Creator Wallets (DCWs), have orchestrated fraudulent activities by luring victims to transfer funds to fraud smart contracts. Since it is almost impossible to reverse the transactions or pinpoint the true identity of the criminals, the industry has turned to flagging such contracts as user warnings. However, current mitigation efforts focus on individual contracts, overlooking the DCWs behind the scenes. Consequently, our research found that this oversight allows fraud to thrive. To address this, we developed CoCo, an automated forensic analysis pipeline that processes a single fraud contract and generates evidence that the legal authorities need to mitigate the fraud. Applying CoCo to 157 confirmed fraud contracts, our research uncovered 1,283,198 associated contracts linked to 91 DCWs, responsible for 2,638,752 ETH ($2,089,504,682) in illicit profits. More alarmingly, CoCo traces the fraudulent activities back to September 2017. In response, we are closely collaborating with Etherscan and the FBI to combat the fraud identified in our study.
In the current era of rapid technological advancement, while numerous problems have been addressed, the issue of data security has emerged as a significant challenge. This concern is prevalent across various sectors, including finance, healthcare, and daily activities, where protecting sensitive data is paramount. Against this backdrop, blockchain technology, characterized by its decentralized nature, presents a compelling solution to these security difficulties. This paper aims to shed light on the efficacy of blockchain as a robust security tool, emphasizing its potential to safeguard data in an increasingly vulnerable digital landscape. In this paper, we explore the strengths of blockchain security and the prospective advancements that promise to fortify data protection shortly.
This conceptual paper delves into the intriguing intersection between Ponzi schemes and cryptocurrency, shedding light on the unique dynamics, challenges, and implications arising from their convergence. While Ponzi schemes have long been recognized as fraudulent investment schemes, the emergence of cryptocurrency has introduced novel avenues for perpetrating such schemes. This paper examines the underlying mechanisms that facilitate the fusion of Ponzi schemes and cryptocurrency. It explores the emergence, characteristics, impact, and regulatory challenges. This paper contributes to a deeper understanding of the complex dynamics at play and provides insights into safeguarding investors and promoting the integrity of cryptocurrency markets.
Kithmini Godewatte Arachchige, Philip Branch, Jason But
The Internet of Things (IoT) and blockchain are emerging technologies that have attracted attention in many industries, including healthcare, automotive, and supply chain. IoT networks and devices are typically low-powered and susceptible to cyber intrusions. However, blockchains hold considerable potential for securing low-power IoT networks. Blockchain networks provide security features such as encryption, decentralisation, time stamps, and ledger functions. The integration of blockchain and IoT technologies may address many of the security concerns. However, integrating blockchain with IoT raises several issues, including the security vulnerabilities and anomalies of blockchain-based IoT networks. In this paper, we report on our experiments using our blockchain test bed to demonstrate that blockchains on IoT platforms are vulnerable to DDoS attacks, which can also potentially lead to device hardware failures. We show that a number of anomalies are visible during either a DDoS attack or IoT device failure. In particular, the temperature of IoT hardware devices can exceed 90 °C during a DDoS attack, which could lead to hardware failure and potential fire hazards. We also found that the Block Transaction Rate (BTR) and network block loss percentage can increase due to corrupted hardware, with the BTR dropping to nearly zero blocks/sec and a block loss percentage of over 50 percent for all evaluated blockchains, and as high as 81.3 percent in one case. Our experiments demonstrate that anomalous temperature, latency, bandwidth, BTR, and network block loss percentage can potentially be used to identify DDoS attacks.
Sabuj Saha, Ahmed Rizvan Hasan, Alvi Mahmud, Nujhat Ahmed · 6 authors
The use of cryptocurrency for financial crimes has increased in recent years because of its decentralized and anonymous nature. This study extracted scholarly articles from the Scopus database and adopted bibliographic and content analysis to review financial fraud research in cryptocurrency. In addition, this study discussed the top ten cryptocurrency scams, potential reasons for falling into those traps, and associated theories to explore scammers’ behavior and outlined comprehensive future research guidelines for a safer financial world. Since 2018, the publication trend of revealing cryptocurrency frauds has gained momentum, and research on this topic has increased significantly in the last two years. The USA is the most significant contributor to cryptocurrency scam research. We found that both developed and developing countries are fairly concerned about combatting crypto fraudsters even though there are no regulated guidelines across the countries. The research potential has shifted from malware, bitcoin, and blockchain to fintech-based crimes such as money laundering, pump-and-dump schemes, and phishing. We observed that ICO fraud, money laundering, Ponzi schemes, phishing, darknet market transactions, ransomware, and pumps and dumps are some of the predominant crimes in crypto and that investor overconfidence, speculative expectations, low barriers to entry, decentralization, and anonymity are the primary reasons for crimes in cryptocurrency. This study suggests studying the socioeconomic impacts of cryptocurrencies, the necessity for standardized global regulation, and the integration of interdisciplinary research. Future research should emphasize exploring the innovation cycle in cryptocurrency assets, understanding cybercrime dynamics, guarding against crypto market manipulation, and developing automated scam prevention.
Peer-to-Peer (P2P) cryptocurrency exchanges are two-sided marketplaces, similar to eBay, where individuals can offer to sell cryptocurrencies in exchange for payment. Due to disintermediation, these marketplaces trade off increased privacy for higher risk (e.g., scams/fraud). Although these marketplaces use feedback systems to encourage healthier transactions, anecdotal evidence suggests that feedback often fails to capture vendor-associated risks. This work documents the online safety of cryptocurrency P2P marketplaces, identifies underlying issues in feedback-based reputation systems, and proposes improved mechanisms for predicting/monitoring risky accounts. We collect data from two cryptocurrency marketplaces, Paxful and LocalCoinSwap (LCS) for 12 months (06/2022--06/2023). The data includes over 396,000 listings, 67,000 vendors, and 4.7 million feedback for Paxful; and about 52,000 listings, 14,000 users, and 146,000 feedback for LCS.First, we show that the current feedback system does not sufficiently convey enough information about risky vendors, and is susceptible to reputation manipulation through user collusion and automation. Second, combining various publicly available information, we build machine learning models to predict account suspension, and achieve a 0.86 F1-score and 0.93 AUC for Paxful. Third, while our models appear to have limited transferability across markets, we identify which features most help account suspension across platforms. Finally, we perform a month-long online evaluation to show that our models are significantly more successful than mere feedback-based reputation schemes at predicting which users will be suspended in the future.
Cryptocurrencies, while revolutionary, have become a magnet for malicious actors. With numerous reports underscoring cyberattacks and scams in this domain, our paper takes the lead in characterizing visual scams associated with cryptocurrency wallets---a fundamental component of Web3. Specifically, scammers capitalize on the omission of vital wallet interface details, such as token symbols, wallet addresses, and smart contract function names, to mislead users, potentially resulting in unintended financial losses. Analyzing Ethereum blockchain transactions from July 2022 to June 2023, we uncovered a total of 24,901,115 visual scam incidents, which include 3,585,493 counterfeit token attacks, 21,281,749 zero-transfer attacks, and 33,873 function name attacks, orchestrated by 6,768 distinct attackers. Shockingly, over 28,414 victims fell prey to these scams, with losses surpassing 27 million USD. This alarming data underscores the pressing need for robust protective measures. By profiling the typical victims and attackers, we are able to propose mitigation strategies informed by our findings.
Blockchain and smart contracts are one of the key technologies promoting Web 3.0. However, due to security considerations and consistency requirements, smart contracts currently only support simple and deterministic programs, which significantly hinders their deployment in intelligent Web 3.0 applications. To enhance smart contracts intelligence on the blockchain, we propose SMART, a plug-in smart contract framework that supports efficient AI model inference while being compatible with existing blockchains. To handle the high complexity of model inference, we propose an on-chain and off-chain joint execution model, which separates the SMART contract into two parts: the deterministic code still runs inside an on-chain virtual machine, while the complex model inference is offloaded to off-chain compute nodes. To solve the non-determinism brought by model inference, we leverage Trusted Execution Environments (TEEs) to endorse the integrity and correctness of the off-chain execution. We also design distributed attestation and secret key provisioning schemes to further enhance the system security and model privacy. We implement a SMART prototype and evaluate it on a popular Ethereum Virtual Machine (EVM)-based blockchain. Theoretical analysis and prototype evaluation show that SMART not only achieves the security goals of correctness, liveness, and model privacy, but also has approximately 5 orders of magnitude faster inference efficiency than existing on-chain solutions.
Mrs. D. Thamizhisai, S. Bharathi, U. Bhuvaneshwaran, S. Mervin Immanuvel · 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.
As the digital environment continues to evolve with the increasing frequency and complexity of cybersecurity threats, there is growing interest in using blockchain (BC) technology. BC is a technology with desirable properties such as decentralization, integrity, and transparency. The decentralized nature of BC eliminates single points of failure, reducing the vulnerability of critical systems to targeted attacks. The complex and rapidly evolving nature of cyber threats requires an earlier and adaptive approach. This review paper examined several papers collected from official websites. Focusing on using BC technology to improve cybersecurity, the main keywords of the review paper were BC technology, supply chain management, proof of work, and proof of stake. This review paper aims to investigate the security components through a threat assessment that compares the security of BC in different classes and real attack environments. It highlights the potential of BC to strengthen cybersecurity measures, citing unique features. The review paper also points out that there is a lack of focus on addressing security challenges related to computer data and digital systems and calling for a deeper discussion on problem-solving.
I explore how criminals use cryptocurrencies in ransomware operations and leverage the vulnerability of virtual currencies to evade legal restrictions and international scrutiny. I do so by examining three drivers of the ‘merger’ between ransomware and cryptocurrency. First, criminal groups have embraced cutting-edge technologies to make their attacks more effective and maximise benefits that cryptocurrency presents, which include the convenience of fast payment and money laundering and the ease of hacking the currencies themselves. Second, ransomware groups have exploited the legal vacuum in the widespread use of rapidly circulating monetary instruments. Finally, groups have adopted cryptocurrencies because states – primary regulators of international financial transactions – remain in such disagreement over the control of digital activities that they have failed to address problems associated with them. In sum, this article presents a set of technical, legal and political reasons why groups have incorporated crypto in their operations.
Student, CSE, Sir MVIT, B Sumangala, Aman Raj, Amritanshu Bhardwaj · 6 authors
Abstract - CrypticReport is a decentralized crime reporting system designed to make public reporting safer, more transparent, and free from identity risks. Citizens often avoid reporting crimes due to fear of exposure, harassment, or data misuse. CrypticReport overcomes these challenges by combining blockchain technology, decentralized IPFS storage, artificial intelligence or zero-knowledge–based authentication. Using Anon Aadhaar, users can verify their identity without revealing any personal information. AI models classify reports to block spam and detect duplicate submissions. All verified reports and evidence are stored in IPFS, and their hashes are recorded on the blockchain for tamper-proof storage. The platform uses a React interface for reporting, a Flask backend for AI processing, Ethereum smart contracts for record immutability, and the Waku protocol for real-time updates between citizens and authorities. Testing shows that the system improves trust, preserves anonymity, and ensures that no data can be altered once submitted. CrypticReport proves that decentralized systems can make crime reporting more secure, reliable, and citizen-friendly. Key Words: Blockchain, IPFS, Anonymous Reporting, AI Classification, Zero-Knowledge Proof, Decentralized Systems
The purpose of this article is to look at recent developments in forensic accounting that have to do with preventing and investigating fraud. The following new developments in forensic accounting are being studied by doing a thorough literature review: data analytics, cyber forensic accounting, and the impact of blockchain and cryptocurrencies on the field. We take a close look at each new trend, breaking it down into its uses, pros, disadvantages, and ethical implications. Case studies and real-world examples back up the findings, showing how effective these fraud prevention and investigation tendencies are. Investigations into financial crimes employing information technology have their own set of challenges, which the report sheds light on. Blockchain technology’s capacity to increase accountability, traceability, and transparency in financial transactions is also explored. To improve fraud detection and prevention efforts, the study finishes with suggestions for researchers, practitioners, and policymakers to adapt to and take advantage of these new trends. To effectively identify and discourage financial crime in the constantly evolving world of new technology, the study finishes by stressing the necessity for continuous research and innovation, highlighting the dynamic character of forensic accounting.
This study investigates the human errors that enable hackers to exploit and carry out social engineering attacks on the non-fungible token (NFT) ecosystem. The aim is to improve the design of decentralized applications that use NFTs to help non-technical users follow security best practices and address remaining user-side vulnerabilities. The study methods included a survey examining participants’ expertise regarding NFTs and cybersecurity, a remote security usability study investigating the pain points and common security best practices and a follow-up interview to examine participants’ experience with a crypto wallet configuration. The results show how human cognitive bias affects users’ decision to be cautious, users’ difficulty with security methods, and improvements to lessen users’ cognitive load. As NFTs expand beyond the cryptocurrency circle, multiple scams and thefts arise due to late adopters not knowing the security best practices. Therefore, increasing the public’s NFT security awareness is key to mitigating potential threats.
Lakshmi P. Krishnan, Iman Vakilinia, Sandeep Reddivari, Sanjay Ahuja
The exponential growth of cryptocurrencies in the last decade has been accompanied by a surge in illicit activities, such as pump-and-dump schemes, Ponzi schemes, and exit scams. These nefarious activities have proliferated through the manipulation of social media platforms. This work employs a multimodal approach to predict cryptocurrency price movements by analyzing social media data, search interest, tweet spikes, and historical cryptocurrency prices. Using data from social media discussions, we extract lexical and behavioral features that highlight sentiment shifts, influence tactics, and cross-platform correlations relevant to cryptocurrency price fluctuations. Using a Variational Autoencoder, this approach predicts cryptocurrency prices and unveils patterns of pump-and-dump attempts. With real-time deployment capabilities, this system not only aids investors in understanding and predicting price trends but also offers protection against scams that may coincide with unusual price fluctuations. Moreover, the techniques developed extend to detecting manipulation and organized inauthenticity beyond the realm of cryptocurrency, ensuring trust and security in digital systems.
Lilita Infante, Roger A. Hallman, J. Ray Hays, Evelyn Cronnon · 5 authors
The increasing use of the Internet and cyber capabilities by criminals have made digital forensics a critical component of criminal investigations and prosecution. Criminal networks were early adopters of the cryptocurrency ecosystem; however, forensics capabilities for cryptocurrency-related investigations have been lacking. While investigators may utilize a number of commercially available digital forensics tools to search for cryptocurrency-related evidence, these tools are inadequate for the task. This paper presents Recovery CAT, a cryptocurrency-specific forensics tool for finding cryptographic artifacts or cryptographic material(s) such as seed phrases, addresses, private keys, wallet applications, hardware wallet usage, exchange domains, etc.
Nayantara K Kumar, Niranjan V Honnungar, M Sharwari Prakash, J J Lohith
In the ever-changing world of blockchain technology, the emergence of smart contracts has completely transformed the way agreements are executed, offering the potential for automation and trust in decentralized systems. Despite their built-in security features, smart contracts still face persistent vulnerabilities, resulting in significant financial losses. While existing studies often approach smart contract security from specific angles, such as development cycles or vulnerability detection tools, this paper adopts a comprehensive, multidimensional perspective. It delves into the intricacies of smart contract security by examining vulnerability detection mechanisms and defense strategies. The exploration begins by conducting a detailed analysis of the current security challenges and issues surrounding smart contracts. It then delves into established frameworks for classifying vulnerabilities and common security flaws. The paper examines existing methods for detecting, and repairing contract vulnerabilities, evaluating their effectiveness. Additionally, it provides a comprehensive overview of the existing body of knowledge in smart contract security-related research. Through this systematic examination, the paper aims to serve as a valuable reference and provide a comprehensive understanding of the multifaceted landscape of smart contract security.