Donghan Chen, Zhihui Lu, Chenchi Luo, J I N Y I Lin ¡ 7 authors
The growth of decentralized finance (DeFi) has been accompanied by an increase in rug pull scams, in which developers misappropriate investorsâ funds, rendering the associated tokens worthless. Existing detection methods struggle to capture dynamic on-chain information and provide interpretable risk assessments. This paper presents RugKeeper, a multi-agent framework leveraging large language models for rug pull detection. RugKeeper constructs comprehensive token contexts via a two-step question-driven process and performs multi-path collaborative reasoning, with a Judger Agent validating results to reduce model hallucinations. Evaluations on historical datasets demonstrate that RugKeeper outperforms state-of-the-art methods, achieving 93.55% accuracy, 95.92% F1-score and robust generalization across model backbones. In a real-world sampled dataset from the BNB Chain, 638 previously undetected rug pull tokens were identified. These results highlight the effectiveness of RugKeeper in enhancing DeFi security and supporting risk mitigation.
Akash Shinde, K. S. Radha, Eesh Pratap Singh, Amresh Kushwaha
Event ticketing systems have long faced challenges such as counterfeiting, scalping, and lack of transparency in resale markets. To address these issues, this research presents the design and development of a blockchain-based ticketing platform that leverages Non-Fungible Tokens (NFTs) to ensure secure, transparent, and verifiable ticket distribution. The primary aim of the study is to explore how blockchain technology can enhance trust, eliminate fraud, and provide users with full ownership of their tickets. The proposed system employs smart contracts to automate ticket creation, distribution, and resale, thereby minimizing the need for intermediaries. Each ticket is represented as a unique NFT, guaranteeing authenticity and enabling traceability throughout its lifecycle. The methodology involves implementing a decentralized application where event organizers can mint NFT tickets, and users can securely purchase, transfer, or resell them using blockchain infrastructure. The results demonstrate that NFT-based tickets effectively prevent duplication and unauthorized sales while providing an immutable record of ownership. Additionally, organizers gain better control over pricing policies, while buyers benefit from secure transfers and enhanced transparency. In conclusion, this platform contributes to solving long-standing issues in the ticketing industry by combining blockchainâs immutability with NFTsâ uniqueness. The study highlights the potential of decentralized technologies to revolutionize digital ticketing, improve user trust, and create a more efficient event management ecosystem.
Hakam Dzakwan Diash, Vannesa Nathania, Mohammad Idhom, Trimono Trimono
The volatile and dynamic Ethereum (ETH) market demands an accurate predictive model to support investment decision making. The complexity of ETH time series data and the influence of various external factors make price prediction a challenge in itself. This study aims to develop an ETH price prediction model using a combined architecture of Convolutional Neural Network (CNN) and also Bidirectional Long Short-Term Memory (BiLSTM). CNN is used to extract local features from historical ETH closing price data, while BiLSTM models bidirectional temporal patterns. The dataset used includes ETH daily price from January 2020 to January 2025, which are obtained from Yahoo Finance and have gone through a normalization process and transformation into sequential form. The model is trained for 100 epochs with an early stopping mechanism to prevent overfitting and evaluated using the MAPE and coefficient of determination (R²) metrics. The evaluation results show that the CNN-BiLSTM model is able to predict ETH prices with a MAPE value of 2.8546% and an R² of 0.9415, indicating high performance in capturing actual data trends. This study shows that the hybrid CNN-BiLSTM approach is effective for Ethereum price prediction.
The rise of Bitcoin and other cryptocurrencies has transformed the financial landscape, especially emerging markets in Indonesia, where adoption rates have grown significantly in recent years. With Indonesia ranked among the top countries in terms of global crypto usage, the demand for innovative strategies to predict Bitcoin price movements has increased. This study compares the ARIMA and Facebook Prophet models for forecasting Bitcoin price trends using historical data from Indodax, one of Indonesia's largest cryptocurrency exchanges, covering the period from 2019 to 2024. Pre-processing involved handling missing data and applying feature engineering techniques such as moving averages and rolling statistics. Results indicate that ARIMA outperformed FBProphet in accuracy, achieving an RMSE of$\mathbf{2 6, 8 9 6, 5 5 8}$and MAPE of$\mathbf{1. 8 1 \%}$. While FBProphet excelled in capturing seasonal patterns despite its higher RMSE and MAPE, ARIMA demonstrated superior precision but struggled with high market volatility. This study highlights the complementary strengths of both models and provides insights to enhance cryptocurrency price prediction in dynamic markets such as Indonesia.
Nominative signatures allow us to indicate who can verify a signature, and they can be employed to construct a non-transferable signature verification system that prevents the signature verification by a third party in unexpected situations. For example, this system can prevent IOU/loan certificate verification in unexpected situations. However, nominative signatures themselves do not allow the verifier to check whether the funds will be transferred in the future or have been transferred.It would be desirable to verify the fact simultaneously when the system involves a certain money transfer such as cryptocurrencies/cryptoassets. In this paper, we propose a smart contract-based non-transferable signature verification system using nominative signatures. We pay attention to the fact that the invisibility, which is a security requirement to be held for nominative signatures, allows us to publish nominative signatures on the blockchain. Our system can verify whether a money transfer actually will take place, in addition to indicating who can verify a signature. We transform the Hanaoka-Schuldt nominative signature scheme (ACNS 2011, IEICE Trans. 2016) which is constructed over a symmetric pairing to a scheme constructed over an asymmetric pairing, and evaluate the gas cost when a smart contract runs the verification algorithm of the modified Hanaoka-Schuldt nominative signature scheme.
Digital systems, networks, and data require robust cyber security measures to counter evolving cyber threats and unauthorized intrusions. Steganography enhances secure communication by embedding information within digital media such as images, audio, and video, rendering hidden messages nearly undetectable. However, traditional steganography suffers from vulnerabilities to steganalysis, limited data capacity, and exposure to statistical and machine learning-based attacks. To overcome these limitations, modern steganography systems integrate advanced cryptographic methods to enhance security and resilience. This review examines solutions combining the Advanced Encryption Standard (AES) for symmetric encryption, Rivest-Shamir- Adelman (RSA) for asymmetric encryption, Quantum Key Distribution (QKD) for secure key exchange, Elliptic Curve Digital Signature Algorithm (ECDSA) for lightweight authentication, and Zero-Knowledge Proof (ZKP) for privacy-preserving verification. These integrated techniques improve data confidentiality, prevent unauthorized access, and strengthen defences against steganalysis attacks. The study evaluates the performance, limitations, and prospects of these intelligent cybersecurity applications, highlighting their potential to advance secure data transmission in the digital landscape.
Advanced Steganography and Watermarking Techniques
Kazi Hassan Shakib, Mizanur Rahman, Mhafuzul Islam, Mashrur Chowdhury
Blockchain-based Vehicular Ad-hoc Network (VANET) is widely considered a secure communication architecture for a connected transportation system. With the advent of quantum computing, there are concerns regarding the vulnerability of this architecture against attack algorithms implemented in a quantum computer. In this study, a potential threat is investigated in a blockchain-based VANET with an impersonation attack utilizing Shorâs algorithm implemented in a quantum computer. Specifically, the impersonation attack using Shorâs algorithm is created by compromising the Rivest-Shamir-Adleman (RSA) encrypted digital signatures of VANET, and thus a threat to the trust-based blockchain scheme of VANET is successfully established. We implemented an integrated simulation platform combining OMNET++, vehicles-in-network simulation (VEINS), and simulation of urban mobility (SUMO) traffic simulator. In addition, we incorporated vehicle-to-everything (V2X) communication in OMNET++ using the extended INET library. An impersonation attack on a blockchain-based VANET is implemented using IBM Qiskit, which is an open-source quantum software development kit. The findings reveal that an impersonation attack is feasible on the Blockchain-based VANET, which compromises the trust chain of a blockchain-based VANET. This research highlights the need for a quantum-secured blockchain for VANET.
A diploma is a document or certificate given to someone who has completed formal education. Diplomas are generally used as a benchmark for someone to get a job and identity in the eyes of the social environment. Many people think that a diploma is something meaningful or essential, so diplomas are often faked which violates legal norms and violates someone's Intellectual Property Rights (IPR). To anticipate counterfeiting, in Indonesia, there is currently a National Diploma Numbering (PIN) system and an Online Diploma Verification System (SIVIL), but unfortunately, the diploma database storage is still centralized which still allows illegal hacking to occur. On this basis, this research was created to able to provide a safer and more reliable diploma recording system solution, by utilizing Blockchain technology it is possible that every diploma issued can also be turned into a digital asset in the form of an NFT diploma, which is easy to track without having to face traditional bureaucratic obstacles. The NFT diploma functions as a representation of ownership, academic credentials, or identity as a sign of a student's educational history. This research aims to determine the performance of the Blockchain storage system on the Polygon network using smart contracts and IPFS. Apart from that, this research will compare the performance with previous research that used Polygon's layer-1, namely Ethereum. In smart contract cost testing, it was found that each Polygon transaction fee only requires 2.26% of the Ethereum transaction fee. Meanwhile, Quality of Service testing resulted in a throughput of 48.6-49.6 Kbps, packet loss of 0%, and latency of 42.07-44.13 m/s. The results show the potential for better cost efficiency and performance on the Polygon network compared to Ethereum.
Abstract The image protection of nonâfungible tokens based on zeroâwatermark methods has received widespread attention. However, on the one hand, existing zeroâwatermark methods are often limited to complex texture changes in the host image, and the features for constructing the zeroâwatermark are vulnerable to geometric attacks. On the other hand, a single watermark image cannot adapt to the diverse usage scenarios of nonâfungible tokens. This paper proposes a robust personalized zeroâwatermark scheme to address the challenges above. Firstly, the image regions suitable for constructing zeroâwatermarks are highlighted by the nonâuniform weighted reconstruction, and the UâNet is introduced for feature extraction against the geometric attacks. At the same time, the watermark images generated by generative models that are suitable for the usage scenario have achieved zeroâwatermark addition and protection for nonâfungible token image content. The proposed method has been experimentally verified to have a certain degree of robustness in geometric and nonâgeometric attacks.
Open access
Advanced Steganography and Watermarking Techniques
Bitcoin, as one of the leading cryptocurrencies, has garnered significant attention due to its highly volatile market behavior. Accurate prediction of Bitcoin prices is crucial for investors, traders, and financial analysts who seek to navigate the uncertainties of this digital currency market. In recent years, machine learning algorithms have emerged as powerful tools for forecasting financial trends, with various models being tested for their ability to predict Bitcoin prices. Among these, the Novel Decision Tree Algorithm and K-Nearest Neighbor (K-NN) algorithm have been recognized for their potential in making accurate predictions. This study aims to improve the efficiency of Bitcoin market price prediction using the Novel Decision Tree Algorithm and to evaluate its performance in comparison with the K-Nearest Neighbor (K-NN) Algorithm. The analysis was conducted with a sample size of 20 for both groups, and a pretest power analysis was performed at an 80% power level. The software implementation of both algorithms resulted in a prediction precision of 87.80% for the Novel Decision Tree Algorithm and 86.91% for the K-NN Algorithm. To assess the statistical significance of the results, an independent sample t-test was conducted, revealing that the difference in accuracy between the two algorithms was statistically negligible, with a value of 0.745 ($p > 0.05$). Despite the small difference, the Novel Decision Tree Algorithm outperformed the K-NN Algorithm in terms of accuracy, demonstrating a higher precision in Bitcoin price prediction.
Cryptocurrency price prediction is all about the digital currency called as cryptocurrency this software helps the new user to raise the understanding level regarding the cryptocurrency. Cryptocurrency like Bitcoin, Ethereum and etc. Which helps the new user to invest in the cryptocurrency without any fear the user can get his prediction about the desire cryptocurrency the stats of the specific cryptocurrency can be explained with the help of the different graphs. In this software four types of the algorithm are being used Python, CNN, TensorFlow, LSTM which helps to find out the best prediction level which decreases the level of loses to the users. As per the new cryptocurrency bill 2021 the government officially taking steps into the cryptocurrency and the government can issue their own cryptocurrency issued by Reserve Bank of India. Which leads the opportunity for the new investor are software will help that new investor to get a detailed explanation regarding the cryptocurrency.
Rollups have emerged as a promising approach to improving blockchains' scalability by offloading transactions execution off-chain. Existing rollup solutions either leverage complex zero-knowledge proofs or optimistically assume execution correctness unless challenged. However, these solutions suffer from high gas costs and significant withdrawal delays, hindering their adoption in decentralized applications. This paper introduces TEERollup, an efficient rollup protocol that leverages Trusted Execution Environments (TEEs) to achieve both low gas costs and short withdrawal delays. Sequencers (system participants) execute transactions within TEEs and upload signed execution results to the blockchain with confidential keys of TEEs. Unlike most TEE-assisted blockchain designs, TEERollup adopts a practical threat model where the integrity and availability of TEEs may be compromised. To address these issues, we first introduce a distributed system of sequencers with heterogeneous TEEs, ensuring system security even if a certain proportion of TEEs are compromised. Second, we propose a challenge mechanism to solve the redeemability issue caused by TEE unavailability. Furthermore, TEERollup incorporates Data Availability Providers (DAPs) to reduce on-chain storage overhead and uses a laziness penalty mechanism to regulate DAP behavior. We implement a prototype of TEERollup in Golang, using the Ethereum test network, Sepolia. Our experimental results indicate that TEERollup outperforms zero-knowledge rollups (ZK-rollups), reducing on-chain verification costs by approximately 86% and withdrawal delays to a few minutes.
Blockchain is a distributed digital ledger that stores any data and can record data about the transaction of cryptocurrency and Non-Fungible Token (NFT) ownership.Recently, cryptocurrency has become the most commonly used blockchain, it provides the potential to help a large wide range of applications.People prefer online transactions for speed, convenience, and the ability to generate financial activities from anywhere while also benefiting from features such as increased security measures and digital record-keeping.In the blockchain, hashing generates the network for transactions.However, due to unexpected conditions or external attacks, transaction failures can occur and require high security.This research proposes the Edwards-curve Digital Signature Algorithm (EdDSA) based on the Proof of Work (PoW) consensus algorithm for mobile payment transfer to provide effective transaction and security.In the verification phase, the receivers utilize the sender's public key and the received signature to verify the message using the PoW approach.When compared to existing approaches like Robust Payment Routing with an Approximation Guarantee (RobustPay+) and Multiple Charges Payment Channel Network based on Routing Protocol (MPCN-RP), EdDSA-PoW achieves a better success ratio of 1.2 in 50 nodes and success ratio and fee of 0.96 and 0.10 in 200 nodes.The proposed method achieves better values of 1.5 in Average maximum fee and 39 in Average accepted payment in 50 nodes compared to existing methods like e-commerce payment, RobustPay+, and Cryptocurrency Transactionbased Graph Convolutional Network (CTGAN).
Lin Ni, Qiukai Ye, Jun Yang, Shuai Zhang ¡ 5 authors
Blockchain has the characteristics of decentralisation, security, reliability and non-tampering, and its core is to reach consensus reliably and quickly through a suitable consensus mechanism. The Raft consensus algorithm in blockchain has advantages such as high performance and high availability, which is suitable for asset management business, but the existing Raft algorithm improvement strategy still has the problems of possible voting divergence and insufficient consideration of malicious node shielding, so the paper proposes a Raft algorithm election improvement scheme and potential malicious node defence improvement scheme, which has more rigorous logic, and is able to reduce the election failure rate, verify the correctness of the leader node, for all the malicious nodes in the system can be accurately identified and shielded, to protect the security of the system, so that the Raft algorithm has a wider range of applications.
Poorva Nayyar, K. K. Bhardwaj, Saiyam Gupta, Ravi Prakash Chaturvedi ¡ 6 authors
The development of financial technology has given rise to a new kind of asset called cryptocurrency, which has presented a significant potential for study. Forecasting cryptocurrency prices is challenging because of their dynamism and unpredictability. Each of the three recurrent neural network, or RNN, algorithms proposed in this paper may be used to predict the prices of three distinct cryptocurrency types: Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The algorithms generate accurate forecasts based on the average absolute percentage error (MAPE). For both cryptocurrency variations, the gated recurrent unit (GRU) fared better in terms of prediction than the long short-term memory (LSTM) and bidirectional LSTM (bi-LSTM) models, according to the models' results. It is therefore regarded as the best algorithm.
A rollup is a scaling solution built on top of an existing blockchain. Rollups separate execution from consensus, but are required to post the data used for state updates to the underlying blockchain. This data is required to ensure that execution of state updates are performed correctly. As writing data to a public blockchain is not free, rollups are incentivized to minimize the amount of data they post on-chain. Rollups therefore aggregate and compress the data used for executions in order to save on fees associated with writing data to the blockchain. In this work, we explore the methods for posting data on-chain and the compression techniques used by real-world rollups. We explore differences in implementations and contrast the approaches used by both optimistic and zero-knowledge rollups. We also explore approaches which enable domain-specific compression, consider upcoming changes to data storage on Ethereum, and suggest improvements for rollup compression.
Blockchain-based steganography enables data hiding via encoding the covert data into a specific blockchain transaction field. However, previous works focus on the specific field-embedding methods while lacking a consideration on required field-generation embedding. In this paper, we propose GBSF, a generic framework for blockchain-based steganography. The sender generates the required fields, where the additional covert data is embedded to enhance the channel capacity. Based on GBSF, we design R-GAN that utilizes the generative adversarial network (GAN) with a reversible generator to generate the required fields and encode additional covert data into the input noise of the reversible generator. We then explore the performance flaw of R-GAN and introduce CCR-GAN as an improvement. CCR-GAN employs a counter-intuitive data preprocessing mechanism to reduce decoding errors in covert data. It incurs gradient explosion for model convergence and we design a custom activation function. We conduct experiments using the transaction amount of the Bitcoin mainnet as the required field. The results demonstrate that R-GAN and CCR-GAN allow to embed 11-bit (embedding rate of 17.2%) and 24-bit (embedding rate of 37.5%) covert data within a transaction amount, and enhance the channel capacity of state-of-the-art works by 4.30% to 91.67% and 9.38% to 200.00%, respectively.
Advanced Steganography and Watermarking Techniques
Richa Gupta, Shailesh Kumar, Vivek Kumar, T D M Sundriyal ¡ 6 authors
In the era of interstitial tax challenges, lack of area, inefficiencies, and an orchestra of fraud, a phoenix of reform appears in which the Ethereum-based blockchain technology acts as a warrior to prevent frauds and misconception. This research, now not most effective, may require some extra innovations and technology implies the essence of blockchain to reconsider toll management system. In the view of interstitial tax annoying conditions, lack of location, inefficiencies, and web of frauds, a phoenix of reforms like Ethereum will be the saviour for the same. This research emphasizes the significant shift towards using blockchain technology to enhance data integrity and decentralization of data. By decentralizing power and ensuring immutability, it aims to promote transparency and reliability in data management. This new system functions like the intricate web of a spider, using blockchain's decentralized structure to smoothly manage the workflow. It elegantly handles the complexities of maintaining data integrity. Real-time audits may not be longer just be a display, but a practical feature that can be utilized effectively. This propped system will act as a spider web technology of blockchain decentralized system which enables flawless transparency, maintains data integrity and will be user friendly. Ethereum will be the revolution changer with all the requisite tools and infrastructure for the implementation of this blockchain-powered toll collection system. Beyond the problematic rectification of existing issues, Ethereum expansion will extend to the realms of data analytics, smart contracts, and the decentralized networks of transactions.
Smart Parking Services (SPSs) enable cruising drivers to find the nearest parking lot with available spots, reducing the traveling time, gas, and traffic congestion. However, drivers risk the exposure of sensitive location data during parking query to an untrusted Smart Parking Service Provider (SPSP). Our motivation arises from a repetitive query to an updated database, i.e., how a driver can be repetitively paired with a previously-matched-but-forgotten lot. Meanwhile, we aim to achieve repetitive query in an oblivious and unlinkable manner. In this work, we present Mnemosyne2 : decentralized and privacy-preserving smart parking with secure repetition and full verifiability. Specifically, we design repetitive, oblivious, and unlinkable Secure k Nearest Neighbor (SkNN) with basic verifiability (correctness and completeness) for encrypted-andupdated databases. We build a local Ethereum blockchain to perform driver-lot matching via smart contracts. To adapt to the lot count update, we resort to the immutable blockchain for advanced verifiability (truthfulness). Last, we utilize decentralized blacklistable anonymous credentials to guarantee identity privacy. Finally, we formally define and prove privacy and security. We conduct extensive experiments over a real-world dataset and compare Mnemosyne2 with existing work. The results show that a query only needs 8 seconds (175 ms) on average for service waiting (verification) among 500 drivers.
Digital art is becoming increasingly popular, and there is a growing demand for online platforms where art can be bought and sold. The purpose of this project is to develop a website that provides such a platform, where art listers can list their digital art pieces for sale, and users can bid on them. Non-fungible tokens (NFTs) provide a new way for artists and photographers to sell their work online. NFT photos and NFT pictures (digital art) can sell for millions of dollars â and there are a growing number of platforms that allow you to buy and sell these items. But what if there is a platform where you can build digital art as well as bid it without any pain. In this project we are building an Auction Application and with help of Python Flask with a feature called neural style transfer where we can build digital arts.
The advancements in telecommunications are significantly benefiting the Internet of Vehicles (IoV) in various ways. Minimal latency, faster data transfer, and reduced costs are transforming the landscape of IoV. While these advantages accompany the latest improvements, they also expand cyberspace, leading to security and privacy concerns. Vehicles rely on trusted authorities for registration and authentication processes, resulting in bottleneck issues and communication delays. Moreover, the central trusted authority and intermediate nodes raise doubts regarding transparency, traceability, and anonymity. This paper proposes a novel vehicle authentication handover framework leveraging blockchain, IPFS, and hybrid computing. The framework uses a Proof of Reputation (PoR) consensus mechanism to improve transparency and traceability and the Elliptic Curve Cryptography (ECC) cryptosystem to reduce computational delays. The suggested system assures data availability, secrecy, and integrity while maintaining minimal latency throughout the vehicle re-authentication. Performance evaluations show the systemâs scalability, with creating keys, encoding, decoding, and registration operations done rapidly. Simulation is performed using SUMO to handle vehicle mobility in an IoV environment. The findings demonstrate the practicality of the proposed framework in vehicular networks, providing a reliable and trustworthy approach for IoV communication.