Blockchain Papers

Enis Karaarslan, Doğan Aydın

No abstract is available for this record.

Gangili Divija Arya, Kothuri Venkata Sai Harika, Deeptimahanti Venkata Rahul, S. Vijaya Narasimhan · 5 authors

No abstract is available for this record.

Elena V. Feldman, Alexey Ruchay, Veronica K. Matveeva, Valeria D. Samsonova

No abstract is available for this record.

Abdelaziz Elbaghdadi, Soufiane Mezroui, Ahmed El Oualkadi

No abstract is available for this record.

Hao Tian, Yang Li, Yue Cai, Xiaohong Shi · 5 authors

No abstract is available for this record.

Wenbo Zhang, Jiaxing Wang, Guangjie Han, Shuqiang Huang · 6 authors

The continuously increasing number of connected smart devices has led to the emergence of a crucial fault detection challenge to the Internet of Things (IoT). In this study, we aim to identify a method for the effective detection of faults in IoT devices. An IoT network model is first established, and a data edge verification mechanism based on blockchain is proposed; the blockchain is used to ensure that the data cannot be tampered with, and their accuracy is verified using the edge. Finally, a data set accuracy weighted random forest based on particle swarm optimization is proposed. The simulation results demonstrate that the proposed detection algorithm is both effective and efficient.

Qian Liao, Yijun Gu, Junfan Liao, Wenzheng Li

Anomaly detection is one of the research hotspots in Bitcoin transaction data analysis. In view of the existing research that only considers the transaction as an isolated node when extracting features, but has not yet used the network structure to dig deep into the node information, a bitcoin abnormal transaction detection method that combines the node's own features and the neighborhood features is proposed. Based on the formation mechanism of the interactive relationship in the transaction network, first of all, according to a certain path selection probability, the features of the neighbohood nodes are extracted by way of random walk, and then the node's own features and the neighboring features are fused to use the network structure to mine potential node information. Finally, an unsupervised detection algorithm is used to rank the transaction points on the constructed feature set to find abnormal transactions. Experimental results show that, compared with the existing feature extraction methods, feature fusion improves the ability to detect abnormal transactions.

Kamyar Sabri‐Laghaie, Saeid Jafarzadeh Ghoushchi, Fatemeh Elhambakhsh, Abbas Mardani

A completely new economic system is required for the era of Industry 4.0. Blockchain technology and blockchain cryptocurrencies are the best means to confront this new trustless economy. Millions of smart devices are able to complete transparent financial transactions via blockchain technology and its related cryptocurrencies. However, via blockchain technology, internet-connected devices may be hacked to mine cryptocurrencies. In this regard, monitoring the network of these blockchain-based transactions can be very useful to detect the abnormal behavior of users of these cryptocurrencies. Therefore, the trustworthiness of the transactions can be assured. In this paper, a novel procedure is proposed to monitor the network of blockchain cryptocurrency transactions. To do so, a hidden Markov multi-linear tensor model (HMTM) is utilized to model the transactions among nodes of the blockchain network. Then, a multivariate exponentially weighted moving average (MEWMA) control chart is applied to the monitoring of the latent effects. Average run length (ARL) is used to evaluate the performance of the MEWMA control chart in detecting blockchain network anomalies. The proposed procedure is applied to a real dataset of Bitcoin transactions.

Xueshuo Xie, Yaozheng Fang, Zhaolong Jian, Ye Lu · 6 authors

No abstract is available for this record.

Yusuf Muhammad Tukur, Dhavalkumar Thakker, Irfan‐Ullah Awan

Abstract Internet of Things (IoT) platforms are responsible for overall data processing in the IoT System. This ranges from analytics and big data processing to gathering all sensor data over time to analyze and produce long‐term trends. However, this comes with prohibitively high demand for resources such as memory, computing power and bandwidth, which the highly resource constrained IoT devices lack to send data to the platforms to achieve efficient operations. This results in poor availability and risk of data loss due to single point of failure should the cloud platforms suffer attacks. The integrity of the data can also be compromised by an insider, such as a malicious system administrator, without leaving traces of their actions. To address these issues, we propose in this work an edge‐based blockchain enabled anomaly detection technique to prevent insider attacks in IoT. The technique first employs the power of edge computing to reduce the latency and bandwidth requirements by taking processing closer to the IoT nodes, hence improving availability, and avoiding single point of failure. It then leverages some aspect of sequence‐based anomaly detection, while integrating distributed edge with blockchain that offers smart contracts to perform detection and correction of abnormalities in incoming sensor data. Evaluation of our technique using real IoT system datasets showed that the technique remarkably achieved the intended purpose, while ensuring integrity and availability of the data which is critical to IoT success.

Ramiro Daniel Camino

Financial services must monitor their transactions to prevent being used for money laundering and combat the financing of terrorism. Initially, organizations in charge of fraud regulation were only concerned about financial institutions such as banks. However, nowadays, the Fintech industry, online businesses, or platforms involving virtual assets can also be affected by similar criminal schemes. Regardless of the differences between the entities mentioned above, malicious activities affecting them share many common patterns. This dissertation's first goal is to compile and compare existing studies involving machine learning to detect and analyze suspicious transactions. The second goal is to synthesize methodologies from the last goal for tackling different use cases in an organized manner. Finally, the third goal is to assess the applicability of deep generative models for enhancing existing solutions. In the first part of the thesis, we propose an unsupervised methodology for detecting suspicious transactions applied to two case studies. One is related to transactions from a money remittance network, and the other is related to a novel payment network based on distributed ledger technologies. Anomaly detection algorithms are applied to rank user accounts based on recency, frequency, and monetary features. The results are manually validated by domain experts, confirming known scenarios and finding unexpected new cases. In the second part, we carry out an analogous analysis employing supervised methods, along with a case study where we classify Ethereum smart contracts into honeypots and non-honeypots. We take features from the source code, the transaction data, and the funds' flow characterization. The proposed classification models proved to generalize well to unseen honeypot instances and techniques and allowed us to characterize previously unknown techniques. In the third part, we analyze the challenges that tabular data brings into the domain of deep generative models, a particular type of data used to represent financial transactions in the previous two parts. We propose a new model architecture by adapting state-of-the-art methods to output multiple variables from mixed types distributions. Additionally, we extend the evaluation metrics used in the literature to the multi-output setting, and we show empirically that our approach outperforms the existing methods. Finally, in the last part, we extend the work from the third part by applying the presented models to enhance classification tasks from the second part, commonly containing a severe class imbalance. We introduce the multi-input architecture to expand models alongside our previously proposed multi-output architecture. We compare three techniques to sample from deep generative models defining a transparent and fair large-scale experimental protocol and interesting visual analysis tools. We showed that general machine learning detection and visualization techniques could help address the fraud detection domain's many challenges. In particular, deep generative models can add value to the classification task given the imbalanced nature of the fraudulent class, in exchange for implementation and time complexity. Future and promising applications for deep generative models include missing data imputation and sharing synthetic data or data generators preserving privacy constraints.

Shiva Ahir, Dipali Telavane, Riya Thomas

The pandemic of Coronavirus Disease 2019 (COVID-19) is proliferating across the globe obnoxiously and it is the most heard buzzword in recent times. Every person ranging from older people, persons with disabilities, youth, indigenous people have become a part of this chain and are most likely to suffer in the upcoming chronology. Social distancing is likely to become a new norm where “Work from Home”, Online Lectures” and “Meetings” ensue on social media applications. Technology has always lent a helping hand for mankind's problems. The idea focuses on highlighting the advancements in technology in the midst of a bizarre situation. Deep Learning applications to detect the symptoms of COVID-19, AI based robots to maintain social distancing, Blockchain technology to maintain patient records, Mathematical modeling to predict and assess the situation and Big Data to trace the spread of the virus and other technologies. These technologies have immensely contributed to curtailing this pandemic. Strong will power, patience and optimistic guidelines catered by the respective government are some of the altercations to COVID-19.

Bin Jia, Yongquan Liang

With rapid development of blockchain technology, blockchain and its security theory research and practical application have become crucial. At present, a new DDoS attack has arisen, and it is the DDoS attack in blockchain network. The attack is harmful for blockchain technology and many application scenarios. However, the traditional and existing DDoS attack detection and defense means mainly come from the centralized tactics and solution. Aiming at the above problem, the paper proposes the virtual reality parallel an-ti-DDoS chain design philosophy and distributed anti-D Chain detection framework based on hybrid ensemble learning. Here, AdaBoost and Random Forest are used as our ensemble learning strategy, and some different lightweight classifiers are integrated into the same ensemble learning algorithm, such as CART and ID3. Our detection framework in block-chain scene has much stronger generalization performance, universality and complementarity to identify accurately the onslaught features for DDoS attack in P2P network. Extensive experimental results confirm that our distributed heterogeneous anti-D chain detection method has better performance in six important indicators (such as Precision, Recall, F-Score, True Positive Rate, False Positive Rate, and ROC curve).

Meng Li, Keli Zhang, Jiamou Liu, Hanxiao Gong · 5 authors

No abstract is available for this record.

Konstantinos Demertzis, Lazaros Iliadis, Nikos Tziritas, Panagiotis Kikiras

No abstract is available for this record.

Cüneyt Gürcan Akçora, Yitao Li, Yulia R. Gel, Murat Kantarcıoğlu

Recent proliferation of cryptocurrencies that allow for pseudo-anonymous transactions has resulted in a spike of various e-crime activities and, particularly, cryptocurrency payments in hacking attacks demanding ransom by encrypting sensitive user data. Currently, most hackers use Bitcoin for payments, and existing ransomware detection tools depend only on a couple of heuristics and/or tedious data gathering steps. By capitalizing on the recent advances in Topological Data Analysis, we propose a novel efficient and tractable framework to automatically predict new ransomware transactions in a ransomware family, given only limited records of past transactions. Moreover, our new methodology exhibits high utility to detect emergence of new ransomware families, that is, detecting ransomware with no past records of transactions.

Rui Zhang, Guifa Zhang, Lan Liu, Chen Wang · 5 authors

No abstract is available for this record.

Joana Lorenz, Maria Inês Silva, David Aparício, João Tiago Ascensão · 5 authors

Every year, criminals launder billions of dollars acquired from serious felonies (e.g., terrorism, drug smuggling, or human trafficking), harming countless people and economies. Cryptocurrencies, in particular, have developed as a haven for money laundering activity. Machine Learning can be used to detect these illicit patterns. However, labels are so scarce that traditional supervised algorithms are inapplicable. Here, we address money laundering detection assuming minimal access to labels. First, we show that existing state-of-the-art solutions using unsupervised anomaly detection methods are inadequate to detect the illicit patterns in a real Bitcoin transaction dataset. Then, we show that our proposed active learning solution is capable of matching the performance of a fully supervised baseline by using just 5% of the labels. This solution mimics a typical real-life situation in which a limited number of labels can be acquired through manual annotation by experts.

Francesco Zola, Jan L. Bruse, Xabier Etxeberria Barrio, Mikel Galar · 5 authors

In Bitcoin entity classification, results are strongly conditioned by the ground-truth dataset, especially when applying supervised machine learning approaches. However, these ground-truth datasets are frequently affected by significant class imbalance as generally they contain much more information regarding legal services (Exchange, Gambling), than regarding services that may be related to illicit activities (Mixer, Service). Class imbalance increases the complexity of applying machine learning techniques and reduces the quality of classification results, especially for underrepresented, but critical classes. In this paper, we propose to address this problem by using Generative Adversarial Networks (GANs) for Bitcoin data augmentation as GANs recently have shown promising results in the domain of image classification. However, there is no "one-fits-all" GAN solution that works for every scenario. In fact, setting GAN training parameters is non-trivial and heavily affects the quality of the generated synthetic data. We therefore evaluate how GAN parameters such as the optimization function, the size of the dataset and the chosen batch size affect GAN implementation for one underrepresented entity class (Mining Pool) and demonstrate how a "good" GAN configuration can be obtained that achieves high similarity between synthetically generated and real Bitcoin address data. To the best of our knowledge, this is the first study presenting GANs as a valid tool for generating synthetic address data for data augmentation in Bitcoin entity classification.

Clément Bénard, Gérard Biau, Sébastien da Veiga, Erwan Scornet

We introduce SIRUS (Stable and Interpretable RUle Set) for regression, a stable rule learning algorithm which takes the form of a short and simple list of rules. State-of-the-art learning algorithms are often referred to as "black boxes" because of the high number of operations involved in their prediction process. Despite their powerful predictivity, this lack of interpretability may be highly restrictive for applications with critical decisions at stake. On the other hand, algorithms with a simple structure-typically decision trees, rule algorithms, or sparse linear models-are well known for their instability. This undesirable feature makes the conclusions of the data analysis unreliable and turns out to be a strong operational limitation. This motivates the design of SIRUS, which combines a simple structure with a remarkable stable behavior when data is perturbed. The algorithm is based on random forests, the predictive accuracy of which is preserved. We demonstrate the efficiency of the method both empirically (through experiments) and theoretically (with the proof of its asymptotic stability). Our R/C++ software implementation sirus is available from CRAN.

Hadi Mansourifar, Lin Chen, Weidong Shi

Increasingly growing Cryptocurrency markets have become a hive for scammers to run pump and dump schemes which is considered as an anomalous activity in exchange markets. Anomaly detection in time series is challenging since existing methods are not sufficient to detect the anomalies in all contexts. In this paper, we propose a novel hybrid pump and dump detection method based on distance and density metrics. First, we propose a novel automatic thresh-old setting method for distance-based anomaly detection. Second, we propose a novel metric called density score for density-based anomaly detection. Finally, we exploit the combination of density and distance metrics successfully as a hybrid approach. Our experiments show that, the proposed hybrid approach is reliable to detect the majority of alleged P & D activities in top ranked exchange pairs by outperforming both density-based and distance-based methods.

Shirshak Raja Maskey, Shahriar Badsha, Shamik Sengupta, Ibrahim Khalil

With the rise of smart cities, transportation systems are getting smarter every day. An Intelligent Transportation System (ITS) should be secure, autonomous, capable of discerning safeness levels at the roads, and provide services to improve human experience. To reach the gold standard, the ITS faces several issues such as centralization, trust, and data integrity. The Transportation System and the data generated from the vehicles can be intercepted, manipulated and corrupted with coordinated attacks. Moreover, every system might have bad actors who want to manipulate the system or data to his or her favor by exploiting the system. In order to guarantee data integrity, immutability, and availability for the ITS, we propose Blockchain based architecture with outlier detection to prevent malicious activity by the vehicles while preserving integrity in sharing information. The Outlier Detection is designed to reside before the consensus process, to identify and prevent participation of malicious vehicles in consensus process or block mining. In our proposed Blockchain based Intelligent Transportation system with Outlier Detection for Smart City (BITS), we used machine learning to detect the anomaly in the data. The proposed model can be used in various applications of ITS such as traffic monitoring, criminal activity profiling, accident detection and reporting, etc.

Moin Hussain Moti, Dimitris Chatzopoulos, Pan Hui, Boi Faltings · 5 authors

No abstract is available for this record.

Matteo Signorini, Matteo Pontecorvi, Waël Kanoun, Roberto Di Pietro

Anomaly detection tools play a role of paramount importance in protecting networks and systems from unforeseen attacks, usually by automatically recognizing and filtering out anomalous activities. Over the years, different approaches have been designed, all focused on lowering the false positive rate. However, no proposal has addressed attacks specifically targeting blockchain-based systems. In this paper, we present BAD: Blockchain Anomaly Detection. This is the first solution, to the best of our knowledge, that is tailored to detect anomalies in blockchain-based systems. BAD is a complete framework, relying on several components leveraging, at its core, blockchain meta-data in order to collect potentially malicious activities. BAD enjoys some unique features: (i) it is distributed (thus avoiding any central point of failure); (ii) it is tamper-proof (making it impossible for a malicious software to remove or to alter its own traces); (iii) it is trusted (any behavioral data is collected and verified by the majority of the network); and, (iv) it is private (avoiding any third party to collect/analyze/store sensitive information). Our proposal is described in detail and validated via both experimental results and analysis, that highlight the quality and viability of our Blockchain Anomaly Detection solution.