Blockchain Papers

Mohamed Boudra, Ahmed Bendahmane

No abstract is available for this record.

Liu Jin, Yahya M.H., Saidatunur Fauzi Saidin, Li Lu

Abstract Multivariate cryptocurrency forecasting is challenging because market series exhibit non-stationarity, cross-variable dependence, heterogeneous temporal scales, and abrupt short-term fluctuations. Although Transformer-based forecasting models can capture long-range temporal relationships, directly modeling raw high-frequency sequences may obscure dominant periodic structures and increase computational cost. This study proposes a frequency-guided multi-scale decomposition and patch Transformer, termed FMDP-Transformer, for multivariate cryptocurrency time-series forecasting. First, a frequency-guided multi-scale representation module estimates dominant temporal periods from the Fourier amplitude spectrum and constructs scale-specific representations through period-dependent average pooling. This module is designed to extract multi-scale periodic information and attenuate short-term disturbances rather than to perform explicit anomaly detection. Second, the resulting representation is decomposed into trend and residual components. A lightweight linear projection is used for parsimonious trend extrapolation, while the residual component is divided into overlapping patches and processed by a Transformer encoder to model local and long-range temporal dependencies. The forecasts produced by the two branches are subsequently combined. Experiments on Bitcoin, Dogecoin, and Binance Coin data derived from the G-Research Crypto Forecasting dataset evaluate the model under multiple forecasting horizons. Comparisons with recurrent, decomposition-based, patch-based, inverted-Transformer, and multi-scale forecasting models, together with component ablations and computational-complexity analysis, are used to assess its effectiveness. The results indicate that frequency-guided multi-scale representation, decomposition, and patch tokenization provide complementary benefits for multivariate cryptocurrency forecasting. Nevertheless, the proposed frequency-guided smoothing operation does not explicitly identify statistical anomalies, and abrupt market movements may contain predictive information rather than noise.

Ch Nanda Krishna, S.J.R.K. Padminivalli V, Aravapalli Rama Satish, Kondragunta Rama Krishnaiah · 6 authors

No abstract is available for this record.

Nattawat Songsom

No abstract is available for this record.

Jean Chrysostome NDAYISABYE

Abstract Cryptocurrency rug pulls, in which token developers withdraw liquidity or otherwise abandon a project after attracting investment, remain a persistent and costly form of decentralized finance (DeFi) fraud. Prior detection approaches, whether feature-engineered supervised classifiers or temporal graph learning models, are typically trained and periodically retrained on labeled data, without an explicit, continuously operating mechanism to detect when the underlying decision boundary has drifted between retraining cycles. Malware threat intelligence has separately developed and validated concept-drift-aware incremental clustering specifically to address this class of problem for evolving malware families. This paper adapts that methodology to the cryptocurrency rug-pull domain and evaluates it empirically against a periodically retrained supervised baseline on a real, temporally-hygienic dataset of 633 labeled Ethereum token projects. Using a strict chronological warm-start, validation, and held-out test split explicitly designed to avoid the hyperparameter-tuning leakage identified and corrected during this study we find that the periodic-retrain baseline achieves marginally higher F1-score (mean 0.891, std 0.008) than the drift-aware clustering approach (mean 0.873, std 0.014) across five independent chronological splits, and is substantially more stable, exhibiting far lower variance in precision across time periods (std 0.015 versus 0.064 for clustering). The clustering approach's principal advantage is a markedly lower false-positive rate in several splits, at the cost of reduced and less stable recall. These findings offer a specific, mechanistically grounded, and negative-leaning result for the transfer of drift-aware clustering from malware detection to on-chain fraud detection under this feature set, and motivate concrete directions richer behavioral features, principled hyperparameter search, and comparison against temporal graph learning baselines for future work.

Dushyant Manghani, Devidas S, Usha Chouhan

Most blockchain anomaly detection research is model-centric, focusing on either proposing new models or comparing benchmarks. Very little work addresses how such methods behave when actually deployed. Data ingestion, feature updates, and inference stability are among the practical concerns that usually get ignored. This work targets this: design, implementation, and evaluation of an AutoML-based platform for anomaly detection targeting Ethereum wallets. The emphasis is on operational behavior, rather than algorithmic novelty. The system is an AutoGluon ensemble that is trained offline from historical, labeled data. Each wallet behavior is represented as a fixed-length feature vector. During inference, live blockchain data is fetched through the blockchain API. This data is transformed into features required by the trained model. The predictor then outputs probabilistic risk scores along with feature-level explanations. Feature computation and model inference are treated as separate processes. This separation allows repeated inference without online learning or continuous retraining. The experimental evaluation discusses several deployment-relevant factors, including class imbalance during training and the contribution of different feature groups. It also examines the stability of the risk score under repeated feature recomputation. Results show that imbalance-aware training improves the reliability of detection. They also indicate that anomaly detection depends on the combined effect of multiple behavioral feature categories.

Vasanthan Athiththan, Pavithira Sivasothy

Blockchain technology has emerged as a secure and decentralized solution for data management across various domains. However, existing consensus mechanisms face challenges related to security, scalability, and energy efficiency, while blockchains remain vulnerable to sophisticated attacks such as double spending, selfish mining, and Sybil attacks. This paper proposes a novel hybrid blockchain security framework that integrates a Hybrid Consensus Algorithm (HCA) combining Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT) with Machine Learning based attack detection. The hybrid consensus improves transaction finality and reduces energy consumption, while the ML module detects anomalous behaviors in real time. Experimental evaluation using a private Ethereum based blockchain demonstrates that the proposed approach improves attack detection accuracy up to 96.8 %, reduces consensus latency by 34 %, and enhances throughput by 27 % compared to traditional PoW based systems. The results confirm that integrating hybrid consensus with intelligent security mechanisms significantly strengthens blockchain resilience.

Bo Xiao, Wei Yin

No abstract is available for this record.

Tong Gu, Min Han, Songlin He, Xiaotong Chen · 5 authors

No abstract is available for this record.

Haoyu Jiang, Xiaoliang Chen, Duoqian Miao, Hongyun Zhang · 7 authors

No abstract is available for this record.

Azath Mubarakali, S. Sharavanan, Hashim Elshafie, Suresh Babu Changalasetty

No abstract is available for this record.

P. Bhuvaneshwari, A Krishnaveni, Harold Robinson, E. Golden Julie

The deep learning technique has emerged as an exemplary model for managing the Artificial Intelligence-based Blockchain framework with technological enhancements to guarantee reliable data through the consensus procedure. The deep learning-enabled blockchain transaction model has involved the development of security to solve the problems of confidentiality and data anonymity. The Hybrid techniques of the Blockchain with the Deep Learning technique are proposed to generate enhanced data durability and its propagation through the enhanced convolutional temporal network (EnCTN) for transaction analysis in a blockchain-enabled Auto Encoder technique. The sliding window extraction technique is used to extract information from a particular window size to evaluate the needed input values from the temporal series. The dilated Convolution is used to capture the long-range dependencies. The proposed technique is implemented in the Ethereum environment using Python, and experimental results show that it has produced an improved performance than the relevant technique in several performance parameters. The anomaly classification accuracy is improved than the relevant technique and it is evaluated using the NSL-KDD dataset. The proposed framework delivers an efficient solution for the real-world anomaly detection application while accurate discovery of temporal anomalies and computational efficiency is enhanced.

Balireddi Durga Anuja, Suneetha Eluri

Ethereum is one of the key platforms in public blockchain networks. Ethereum used for various critical applications like decentralized finance (DeFi), smart contract based and supply chain operations etc., Because of its growing popularity along with its open and anonymous nature, network can contribute fraudulent or abnormal transactions. The existing anomaly detection methods suffer from real time detection as they use rule based or statistical methods. These methods are failed to identify dynamic or structural transactional anomalies and has no consensus verification. To address these challenges, in this study we designed an anomaly detection model that features rule based logic, Graph Neural Networks with GraphSAGE and Consensus-driven Complex Event Processing (CEP). We developed this model by combining three key procedures primarily the transactions are fetched from live Ethereum network and presented to rule based filtering. Graph construction and feature extraction will be done at second stage in the model to understand structural pattern and detects dynamically evolving abnormal patterns. In final phase consensus verification introduced as novel contribution in this model to reduce false positives. The framework tested for live Ethereum data that shows improvements detection accuracy of GNN model with low latency time. On the whole, the proposed system model supports practical security monitoring in Ethereum networks. This framework efficiently helps in detecting fraud or abnormal transactions that degrades the performance, reliability or accuracy of the decentralized network.

Shiji Mol F R, J. Ayyana Prabu, R. Santhana Krishnan, S. Jeeva Paulin · 6 authors

Ensuring vaccine integrity requires rigorous cold-chain management, as even minor temperature deviations can compromise safety, cause financial losses, and endanger public health. Conventional monitoring systems, dependent on centralized databases and manual oversight, remain prone to delays, inaccuracies, and tampering. To address these limitations, we propose an integrated framework that combines deep learning and blockchain for secure, predictive, and real-time cold-chain monitoring. A distributed network of IoT sensors captures temperature, humidity, vibration, and GPS data at one-minute intervals. Long Short-Term Memory (LSTM) networks forecast short-term temperature trends, Autoencoders (AE) detect anomalies, and one-dimensional Convolutional Neural Networks (1D CNN) classify shipment states as Safe, At Risk, or Spoiled. A decision engine fuses outputs from these models into a unified risk score, enabling timely and data-driven interventions. Critical events and metadata are immutably recorded on the Ethereum blockchain, while raw sensor data is stored off-chain using IPFS to ensure auditability and efficiency. The system also supports automated alerts, real-time monitoring through Grafana dashboards, and adaptive model retraining for continuous improvement. Evaluation covers forecasting accuracy, anomaly detection precision, classification performance, and blockchain efficiency. By uniting predictive analytics with tamper-proof logging, the proposed framework reduces vaccine spoilage, strengthens supply chain resilience, and offers a scalable solution for broader temperature-sensitive logistics applications.

Jeyakumar Samantha Tharani, E.Y.A. Charles, Punit Rathore, Zhé Hóu · 6 authors

Blockchain is a distributed ledger technology that provides pseudo-anonymity among participants to maintain privacy. However, malicious actors utilise this property to hide their illegal rewards received through cyber attacks, dark market trades, money laundering and Ponzi schemes. The recent confiscation by the FBI of more than $4 million USD worth of bitcoin from the ‘Silk Road’ dark marketplace indicates the scale of the problem faced by financial regulators and law enforcement authorities. Analysing and identifying harmful actors is, therefore, necessary to regulate the transactions of digital assets. Machine learning models can assist in detecting patterns and correlations between the actors in blockchain networks that may not be apparent through traditional methods. In blockchain networks, the number of actors linked to illegal activities is significantly smaller than that of regular activities. Also, only very limited labelled transaction data is available about these malicious actors. These limitations make it harder to train supervised learning models to provide real-time proactive responses. This article represents a pioneering effort in thoroughly examining the different unsupervised learning methods for clustering suspicious behaviour of actors within blockchain networks. The proposed unsupervised learning-based analysis considers metadata and interconnectivity information of blockchain transactions. The metadata contains time-based and amount-based information. Interconnectivity data represents centrality measures and embedding vectors of the blockchain network. The quality of the identified clusters is validated using internal and external cluster validation measures. The validation results were used to identify influential features using the eXplainable AI technique Shapley (ShAP) values. The results reveal that the features related to the spending and receiving transactions strongly influenced cluster identification. Overall, the centroid-based and connectivity-based approaches identified well-separated clusters for metadata and centrality-based features of blockchain transactions.

Khalid Al‐Khatib, Fares Ayoub, Qais Marji

No abstract is available for this record.

Bo Xiao, Wei Yin

Bitcoin transaction anomaly detection is essential for maintaining financial market stability. A significant challenge is capturing the dynamically evolving transaction patterns within transaction networks. Dynamic graph models are effective for characterizing the temporal evolution of transaction systems. However, current methods struggle to mine long-range temporal dependencies and address the class imbalance caused by the scarcity of abnormal samples. To address these issues, we propose a novel approach, the Bidirectional EvolveGCN with Class-Balanced Learning Network (Balanced-BiEGCN), for Bitcoin transaction anomaly detection. This model integrates two key components: (1) a bidirectional temporal feature fusion mechanism (Bi-EvolveGCN) that enhances the capture of long-range temporal dependencies and (2) a Sample Class Transformation (CSCT) classifier that generates difficult-to-distinguish abnormal samples to balance the positive and negative class distribution. The generation of these samples is guided by two loss functions: the adjacency distance adaptive loss function and the symmetric space adjustment loss function, which optimize the spatial distribution and confusion of abnormal samples. Experimental results on the Elliptic dataset demonstrate that Balanced-BiEGCN outperforms existing baseline methods in anomaly detection.

Noha E. El-Attar, Marwa Salama, Mohamed Abdelfattah, Sanaa Taha

Detecting, tracking, and preventing cryptocurrency money laundering within blockchain systems is a major challenge for governments worldwide. This paper presents an anomaly detection model based on blockchain technology and machine learning to identify cryptocurrency money-laundering accounts within Ethereum blockchain networks. The proposed model employs Particle Swarm Optimization (PSO) to select optimal feature subsets. Additionally, three machine learning algorithms—XGBoost, Isolation Forest (IF), and Support Vector Machine (SVM)—are employed to detect suspicious accounts. A Genetic Algorithm (GA) is further applied to determine the optimal hyperparameters for each machine learning model. The evaluations demonstrate the superiority of the XGBoost algorithm over SVM and IF, particularly when enhanced with GA. It achieved accuracy, precision, recall, and F1-score values of 0.98, 0.97, 0.98, and 0.97, respectively. After applying GA, XGBoost’s performance metrics improved to 0.99 across all categories.

Muhammed Ersin Durmuşkaya, Mustafa Kara

Satellite communication (SATCOM) networks are essential in delivering long-range and high-capacity data transmission on a global basis. With emerging satellite-ground-air integrated networks (SAGIN) responding to growing demands for communication and low-latency connections, their unique, dynamic infrastructure raises novel issues in security. Traditional centralized defenses are increasingly ineffective against advanced attacks such as distributed denial-of-service (DDoS). This research advocates an integrated solution that combines blockchain infrastructure with deep learning approaches to address these security challenges. The model was simulated in an NS-3 environment, and normal and attack traffic were generated to train a hybrid CNN-LSTM-based anomaly detection model. Distinct types of threats were recorded on a private Ethereum-based blockchain using smart contracts, enabling decentralized blacklist control and automated response behaviors. With decentralized control of threats, detection efficiency is enhanced by application of AI-driven analysis, and trust is ensured by virtue of immutable logging. The test results hold promise for this solution in delivering scalable, robust, and autonomous security for modern SATCOM networks.

Xng Ai, Lin, Shudan, Zhong Li, Kai Zhou · 6 authors

Decentralized Finance (DeFi) attacks have resulted in significant losses, often orchestrated through Adversarial Exploiter Contracts (AECs) that exploit vulnerabilities in victim smart contracts. To proactively identify such threats, this paper targets the explainable detection of AECs. Existing detection methods struggle to capture semantic dependencies and lack interpretability, limiting their effectiveness and leaving critical knowledge gaps in AEC analysis. To address these challenges, we introduce SEASONED, an effective, self-explanatory, and robust framework for AEC detection. SEASONED extracts semantic information from contract bytecode to construct a semantic relation graph (SRG), and employs a self-counterfactual explainable detector (SCFED) to classify SRGs and generate explanations that highlight the core attack logic. SCFED further enhances robustness, generalizability, and data efficiency by extracting representative information from these explanations. Both theoretical analysis and experimental results demonstrate the effectiveness of SEASONED, which showcases outstanding detection performance, robustness, generalizability, and data efficiency learning ability. To support further research, we also release a new dataset of 359 AECs.

Chigozie Athanasius Nnadiekwe, Collins Izuchukwu Okafor, Ikechi Saviour Igboanusi, Jae Min Lee · 5 authors

SoldierCare is a real-time, blockchain-enabled Internet of Medical Things (IoMT) framework designed to enhance military personnel safety through integrated health monitoring and cyberattack detection. The system employs a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model trained on the WUSTL-EHMS-2020 dataset, combining physiological sensor data and network traffic to identify both health anomalies and malicious activities with high accuracy. A smart contract-enabled Ethereum blockchain ensures the integrity and traceability of alerts by immutably logging metadata, while detailed data is stored off-chain in IPFS to reduce on-chain overhead. The architecture supports edge deployment, enabling low-latency inference and autonomous operation in mission-critical environments. Experimental results demonstrate a detection accuracy of 98.6%, with efficient scaling and minimal false detections. Optimizations such as transaction batching enhance blockchain performance under increasing load. SoldierCare represents a secure, scalable solution that fuses AI, fog computing, and blockchain to provide resilient operational support in dynamic battlefield scenarios.

Hari Suresh Babu Gummadi, Mohan Sankaran, R. D. Shelke, Venkata Siva Kumar Tankani · 6 authors

In the advancing domain of drone systems, cybersecurity is a critical issue owing to the rising threat of advanced cyberattacks. This study presents an innovative framework for drone cybersecurity that utilizes the integration of deep learning and blockchain technologies to efficiently detect and prevent malicious intrusions. The proposed architecture consists of four main stages: data normalization, feature selection utilizing the greylag goose optimization algorithm (GLGOA), long short-term memory (LSTM)-based cyberattack detection, and blockchain-based data validation. Initially, raw drone sensors and network data are standardized using normalization techniques to ensure consistency and minimize noise. GLGOA is utilized to extract the most pertinent features, thereby improving detection efficiency and reducing computational burden. The enhanced feature set is input into an LSTM model designed to capture temporal dependencies and classify potential cyber threats. Ultimately, blockchain integration guarantees the immutable recording of drone interactions and improves overall data security and reliability. Comprehensive experimental assessment illustrates the superiority of the proposed GLGOA-LSTM-BC model compared to traditional methods such as SVM, random forest, CNN, and GRU regarding -score. The proposed method demonstrates a 97.8% accuracy and a 97.5% f1-score, establishing it as a robust and reliable solution for real-time cyberattack detection in drone environments. The amalgamation of bio-inspired optimization, deep learning, and distributed ledger technologies facilitates the development of secure, intelligent, and autonomous drone systems within contemporary digital infrastructure.

Jayameena Desikan, Sushil Kumar Singh, A. Jayanthiladevi

No abstract is available for this record.

Zhanqi Cui, Xiguo Gu, Xinhong Duan, Xinian Gu · 7 authors

No abstract is available for this record.