Sequence Of Transactions Research Articles

DFier: A directed vulnerability verifier for Ethereum smart contracts

Smart contracts are self-executing digital agreements that automatically enforce the terms between parties, playing a crucial role in blockchain systems. However, due to the potential losses of digital assets caused by vulnerabilities, the security issues of Ethereum smart contracts have garnered widespread attention. To address this, researchers have developed various techniques to detect vulnerabilities in smart contracts, with fuzzing techniques achieving promising results. Nonetheless, current fuzzers are unable to effectively exercise suspicious targets because they overlook two key factors: comprehensively exploring all paths to the targets and providing high-quality directed seed inputs. This paper presents a Directed vulnerability veriFier (DFier), which elaborates effective transaction sequences with directed inputs for the fuzzer. This focuses on exploring target paths and automatically validating whether the specified locations are vulnerable. Specifically, DFier employs static analysis to help locate target paths, facilitating their comprehensive exploration. Additionally, we devise three heuristic strategies to enable our fuzzing technique to generate directed inputs that effectively validate the targets. Extensive experiments demonstrate that DFier is effective in verifying contract security, compared with three existing contract fuzzers (i.e., contractFuzzer, sFuzz, and conFuzzius), while the performance losses are in an acceptable range.

A Hybrid Neural Network-Based Fast Financial Fraud Detection Model

With the increasing number of financial transactions, financial fraud has become increasingly serious for financial institutions and the public. The core idea of this model is to integrate multiple neural network structures and utilize their respective advantages to improve the performance of fraud detection. Firstly, we employed the convolutional neural network with interpretable blocks (CNNIB) convolutional neural network (CNN) to extract key features from the data to capture patterns and patterns in fraud cases. Secondly, we introduced the autoencoder generative adversarial network (AE-GAN) adversarial network to perform feature analysis on sequence data to capture temporal features in transaction sequences. Finally, we used differential detection for classification to determine whether transactions were fraudulent. An independent detection module was established to accelerate the recognition of financial fraud, and parameter indicators were optimized. Finally, a hybrid neural network model was established. The experimental results indicate that our model has achieved significant results in quickly detecting financial fraud; compared with traditional single neural network models, hybrid neural network models have significant improvements in accuracy and efficiency. In addition, we conducted in-depth analysis of the model and revealed its performance stability under different training set sizes and data distributions. Our research findings provide an effective tool for financial institutions to quickly identify financial fraud.

A Critical Analysis of Machine Learning Techniques for Online Transaction Security

The widespread adoption of Unified Payments Interface (UPI) as the preferred digital transaction method has led to a sharp increase in fraudulent activities, presenting a considerable challenge to the security and reliability of online payments.Top of Form To address this pressing concern, our project focuses on developing a sophisticated fraud detection system tailored specifically for UPI transactions. We utilize Convolutional Neural Networks (CNNs), an advanced deep learning architecture, to intricately capture the sequence of operations involved in UPI transaction processing. Through extensive data collection and preprocessing, we curate a diverse dataset comprising various transaction types, amounts, and temporal patterns, ensuring comprehensive coverage of transaction behaviour. The CNN is meticulously trained on this dataset, leveraging its innate ability to discern subtle patterns and dependencies within transaction sequences. Notably, our approach emphasizes the importance of minimizing false positives to prevent genuine transactions from being erroneously flagged as fraudulent. In real-time, incoming UPI transactions are subjected to scrutiny by the trained CNN, which evaluates their conformity to learned normal behaviour patterns. Transactions exhibiting deviations indicative of potential fraud are promptly flagged for further investigation, thus enabling proactive mitigation of fraudulent activities. Most importantly, our study contains a comprehensive comparative analysis that compares the efficacy of conventional machine learning methods such as Support Vector Machines (SVM), Random Forests, and Logistic Regression with CNN-based fraud detection. Through meticulous evaluation, we demonstrate the superior efficacy of CNNs in detecting fraudulent UPI transactions, owing to their inherent capacity to capture intricate spatial and temporal dependencies. Our findings underscore the transformative potential of deep learning techniques in bolstering the security and resilience of digital payment ecosystems, thereby safeguarding users and fostering trust in online transactions.

Multiscale Feature Fusion and Graph Convolutional Network for Detecting Ethereum Phishing Scams

With the emergence of blockchain technology, the cryptocurrency market has experienced significant growth in recent years, simultaneously fostering environments conducive to cybercrimes such as phishing scams. Phishing scams on blockchain platforms like Ethereum have become a grave economic threat. Consequently, there is a pressing demand for effective detection mechanisms for these phishing activities to establish a secure financial transaction environment. However, existing methods typically utilize only the most recent transaction record when constructing features, resulting in the loss of vast amounts of transaction data and failing to adequately reflect the characteristics of nodes. Addressing this need, this study introduces a multiscale feature fusion approach integrated with a graph convolutional network model to detect phishing scams on Ethereum. A node basic feature set comprising 12 features is initially designed based on the Ethereum transaction dataset in the basic feature module. Subsequently, in the edge embedding representation module, all transaction times and amounts between two nodes are sorted, and a gate recurrent unit (GRU) neural network is employed to capture the temporal features within this transaction sequence, generating a fixed-length edge embedding representation from variable-length input. In the time trading feature module, attention weights are allocated to all embedding representations surrounding a node, aggregating the edge embedding representations and structural relationships into the node. Finally, combining basic and time trading features of the node, graph convolutional networks (GCNs), SAGEConv, and graph attention networks (GATs) are utilized to classify phishing nodes. The performance of these three graph convolution-based deep learning models is validated on a real Ethereum phishing scam dataset, demonstrating commendable efficiency. Among these, SAGEConv achieves an F1-score of 0.958, an AUC-ROC value of 0.956, and an AUC-PR value of 0.949, outperforming existing methods and baseline models.

Space efficiency and throughput performance in AVS/RS under variant lane depths

An automated vehicle storage and retrieval system (AVS/RS) is a widespread automated warehouse solution that hosts hundreds of stock-keeping units (SKU) and counts thousands of incoming and outgoing unit loads corresponding to a sequence of time-dependent storage and retrieval transactions. AVS/RS ensures high storage density, reduced cycle time, and high productivity. This study introduces and applies an original data-driven comparative and competitive multi-scenario methodology to measure and control the performance of a multi-deep tier-captive AVS/RS. This original methodology measures and controls the impact of lane depth (1), assignment strategy (2), opening strategy (3), and dispatching strategy (4) on the storage capacity, system throughput, and space efficiency in the design and configuration of an AVS/RS. The proposed methodology was applied to a real case study, demonstrating that the combination of the four leverages significantly affects system performance.

SN-RNSP: Mining self-adaptive nonoverlapping repetitive negative sequential patterns in transaction sequences

Negative sequential patterns (NSP) focus on non-occurring events and play a role that cannot be replaced by positive sequential patterns (PSP). Considering the repetitive occurrence of sequential patterns in a sequence, repetitive NSP (RNSP) mining captures frequent NSP across different sequences from a database. Those patterns benefit many tasks of transaction services, e.g., fraud detection and medical diagnosis. However, limited studies focusing on mining RNSP are proposed, e.g., e-RNSP and ONP-Miner, and they are devised under strict constraints and are inefficient in practice. To address these issues, this paper proposes a Self-adaptive Nonoverlapping RNSP mining method SN-RNSP to mine nonoverlapping RNSP with the self-adaptive gap between successive elements from transaction sequences, which requires that each element cannot be reused at the same position in occurrences, and the gap value does not need to be specified in advance. First, this paper develops a method that maintains occurrences of pattern candidates via the bitmap structure to capture all repetitive PSP (RPSP), which utilizes the bitmap-based operation to calculate support efficiently. Second, SN-RNSP leverages bitmaps to record the locations of RPSP and RNSP in the database and query the repetition times of corresponding RPSP for the support calculation of RNSP. Conducted on real-world and synthetic datasets, extensive experiments demonstrate that SN-RNSP can discover more patterns with better mining performance than the state-of-the-art RNSP mining algorithms in transaction sequence databases.

CrossFuzz: Cross-contract fuzzing for smart contract vulnerability detection

Context:Smart contracts are computer programs that run on a blockchain. As the functions implemented by smart contracts become increasingly complex, the number of cross-contract interactions within them also rises. Consequently, the combinatorial explosion of transaction sequences poses a significant challenge for smart contract security vulnerability detection. Existing static analysis-based methods for detecting cross-contract vulnerabilities suffer from high false-positive rates and cannot generate test cases, while fuzz testing-based methods exhibit low code coverage and may not accurately detect security vulnerabilities. Objective:The goal of this paper is to address the above limitations and efficiently detect cross-contract vulnerabilities. To achieve this goal, we present CrossFuzz, a fuzz testing-based method for detecting cross-contract vulnerabilities. Method:First, CrossFuzz generates parameters of constructors by tracing data propagation paths. Then, it collects inter-contract data flow information. Finally, CrossFuzz optimizes mutation strategies for transaction sequences based on inter-contract data flow information to improve the performance of fuzz testing. Results:We implemented CrossFuzz, which is an extension of ConFuzzius, and conducted experiments on a real-world dataset containing 396 smart contracts. The results show that CrossFuzz outperforms xFuzz, a fuzz testing-based tool optimized for cross-contract vulnerability detection, with a 10.58% increase in bytecode coverage. Furthermore, CrossFuzz detects 1.82 times more security vulnerabilities than ConFuzzius. Conclusion:Our method utilizes data flow information to optimize mutation strategies. It significantly improves the efficiency of fuzz testing for detecting cross-contract vulnerabilities.

Caerus: Low-Latency Distributed Transactions for Geo-Replicated Systems

Distributed deterministic database systems achieve high transaction throughput for geographically replicated data. Supporting transactions with ACID guarantees requires deterministic databases to order transactions globally to dictate execution order. In a geographically distributed environment, ordering transactions globally can take multiple wide-area network (WAN) round trips of messaging, which adds significant latency to transaction response times, leading to poor user experiences. To improve the response time of transactions in deterministic databases, we propose an ordering protocol that can include a transaction in the global order in a single WAN round trip to the primary regions of the data items within the transaction's read and write set. The protocol reduces the cost of determining the global order for all transactions by leveraging deterministic merging of partial sequences of transactions per geographic region. We implement the protocol in Caerus, our geo-replicated deterministic database system that serializably commits and replicates transactions after a delay of only a single WAN round trip of messaging. Using popular workload benchmarks over geographically replicated data in Azure, we show that Caerus outperforms state-of-the-art comparison systems to deliver low-latency transaction execution.

CTCN: a novel credit card fraud detection method based on Conditional Tabular Generative Adversarial Networks and Temporal Convolutional Network.

Credit card fraud can lead to significant financial losses for both individuals and financial institutions. In this article, we propose a novel method called CTCN, which uses Conditional Tabular Generative Adversarial Networks (CTGAN) and temporal convolutional network (TCN) for credit card fraud detection. Our approach includes an oversampling algorithm that uses CTGAN to balance the dataset, and Neighborhood Cleaning Rule (NCL) to filter out majority class samples that overlap with the minority class. We generate synthetic minority class samples that conform to the original data distribution, resulting in a balanced dataset. We then employ TCN to analyze transaction sequences and capture long-term dependencies between data, revealing potential relationships between transaction sequences, thus achieving accurate credit card fraud detection. Experiments on three public datasets demonstrate that our proposed method outperforms current machine learning and deep learning methods, as measured by recall, F1-Score, and AUC-ROC.

Using blockchain technology for monitoring business processes in ERP systems

Database protection is one of the most difficult tasks facing the departments responsible for ensuring information security. On the one hand, to work with the database, it is necessary to provide access to data to all employees who, on duty, must collect, process, store and transfer confidential data. On the other hand, the expansion of databases does not always have a centralized architecture, and therefore the actions of violators are becoming more sophisticated. Thus, there is no clear and precise methodology for a comprehensive solution to the problem of protecting databases that could be applied in all cases; in each specific situation, an individual approach has to be found. Blockchain technology can solve some of these problems. The purpose of the study was to show the possibilities of blockchain technology for tracking possible external and internal attacks on Control Systems and their databases. It is proposed to combine and blockchain-based hashing methods in alphanumeric format and explore its application in tracking and monitoring the sequence of business process transactions in ERP systems. A cyber attack on transaction data in ERP systems will immediately affect the root of the Merkle tree, which can serve as a signal that the system has been hacked. We show that blockchain, considered one of the most disruptive technologies in various industries, certainly has the potential to apply and ensure the security of enterprise management systems in the ERP format.

Davida: A Decentralization Approach to Localizing Transaction Sequences for Debugging Transactional Atomicity Violations

Atomicity is a desirable property for multithreaded programs. In such programs, a transaction is an execution of an atomic code region that may contain memory accesses on an arbitrary number of shared variables. When transactions are not conflicting with one another in a trace, they greatly simplify the reasoning of the program correctness. If a transaction incurs an atomicity violation in a trace, developers have to debug the code, but this is challenging. To achieve practical runtime performances, existing dynamic techniques for detecting such atomicity violations face a challenge: They are designed for either detecting all such atomicity violations without the capability of localizing the corresponding cross-thread transaction sequences or deliberately missing some atomicity violations in the trade of localizing some of them to support their atomicity violation detection. In this article, we propose Davida, a novel technique to address this problem. Davida efficiently tracks selective transactions and cross-thread dependency sequences over transactions reachable from the currently active transactions of all the threads in a decentralized manner. We prove that Davida precisely accomplishes every atomicity violation in a trace with an actual sequence of transactions triggering the violation. The experimental results on 15 subjects showed that Davida outperformed Velodrome, the previous graph-based state-of-the-art technique, in both performance and completeness.

Otizmli Küçük Çocuklar ve Ebeveynlerinde Katılım: Çoklu Olgu Çalışması

This qualitative multiple-case study comprehensively examined the circumstances that supported and hindered social engagement in the three toddlers with autism when they interacted with their mothers. Directive actions of mothers for controlling and teaching purposes and requesting actions of toddlers resulted in instrumental engagement during which power struggles were sometimes observed. The findings indicated that social engagement was established when one interacting partner’s social initiation was accepted with a social response from the other partner. Those socially engaged moments were first built upon the toddlers’ individual interests but maintained only by continuous back-and-forth responses from both mothers and toddlers manifesting a transactional sequence during which the schema of their interaction continuously changed, adapted, and developed. This finding revealed that social, rather than instrumental, engagement may lead, possibly, to a sense of joy in these children while interacting with their mothers. One further approach related to this finding can be concentrating on the child’s social motivation (e.g., having fun), rather than non-social motivation (e.g., receiving a favorite snack or toy) while interacting. Such an approach can be a promising avenue for optimally encouraging social purposes of engagement in toddlers with autism.

FORENSIC CREDIT CARD FRAUD DETECTION USING DEEP NEURAL NETWORK

This paper describes the ways in which forensic credit card fraud detection can be achieved using deep neural network (DNN). The problem that this research addresses is to identify a technique that can be used by forensic experts in credit card fraud investigations to detect if a fraudulent transaction has occurred. It is an obligation that institutions providing financial services must implement relevant safeguards against credit card fraud to prevent possible loss of their investments or clients’ funds. This paper presents a forensic detection model of credit card fraud that is based on sequential data modeling using Long Short-Term Memory (LSTM) DNNs. The current study determines whether LSTM-attention algorithm can identify the most important transactions in an input sequence that provides high-accuracy prediction of fraudulent transactions. The effectiveness of the LSTM-attention model is achieved by selecting the most relevant predictive features, uniform manifold approximation, transaction sequences, and attention mechanisms that improve the performance of the model. The results show that LSTM-attention algorithms can be used to conduct forensic credit card fraud detection with high accuracy and precision. The novelty of the research paper is that it successfully uses an LSTM-attention algorithm to detect credit card fraud instances and proves the applicability of the model in mitigating fraudulent transactions in banking institutions. Keywords: Credit Card Fraud, Machine Learning, Deep Neural Network, Convolutional Neural Network, Long Short-Term Memory DOI： https://doi.org/10.35741/issn.0258-2724.58.1.33

High Utility Periodic Frequent Pattern Mining in Multiple Sequences

Periodic pattern mining has become a popular research subject in recent years; this approach involves the discovery of frequently recurring patterns in a transaction sequence. However, previous algorithms for periodic pattern mining have ignored the utility (profit, value) of patterns. Additionally, these algorithms only identify periodic patterns in a single sequence. However, identifying patterns of high utility that are common to a set of sequences is more valuable. In several fields, identifying high-utility periodic frequent patterns in multiple sequences is important. In this study, an efficient algorithm called MHUPFPS was proposed to identify such patterns. To address existing problems, three new measures are defined: the utility, high support, and high-utility period sequence ratios. Further, a new upper bound, , and two new pruning properties were proposed. MHUPFPS uses a newly defined HUPFPS-list structure to significantly accelerate the reduction of the search space and improve the overall performance of the algorithm. Furthermore, the proposed algorithm is evaluated using several datasets. The experimental results indicate that the algorithm is accurate and effective in filtering several non-high-utility periodic frequent patterns.

TxT: Real-Time Transaction Encapsulation for Ethereum Smart Contracts

Ethereum is a permissionless blockchain ecosystem that supports execution of smart contracts, the key enablers of decentralized finance (DeFi) and non-fungible tokens (NFT). However, the expressiveness of Ethereum smart contracts is a double-edged sword: while it enables blockchain programmability, it also introduces security vulnerabilities, i.e., the exploitable discrepancies between expected and actual behaviors of the contract code. To address these discrepancies and increase the vulnerability coverage, we propose a new smart contract security testing approach called transaction encapsulation. The core idea lies in the local execution of transactions on a fully-synchronized yet isolated Ethereum node, which creates a preview of outcomes of transaction sequences on the current state of blockchain. This approach poses a critical technical challenge — the well-known time-of-check/time-of-use (TOCTOU) problem, i.e., the assurance that the final transactions will exhibit the same execution paths as the encapsulated test transactions. In this work, we determine the exact conditions for guaranteed execution path replicability of the tested transactions. To demonstrate the transaction encapsulation, we implement a transaction testing tool, TxT, which reveals the actual outcomes (either benign or malicious) of Ethereum transactions. To ensure the correctness of testing, TxT deterministically verifies whether a given sequence of transactions ensues an identical execution path on the current state of blockchain. We analyze over 1.3 billion Ethereum transactions and determine that 96.5% of them can be verified by TxT. We further show that TxT successfully reveals the suspicious behaviors associated with 31 out of 37 vulnerabilities (83.8% coverage) in the smart contract weakness classification (SWC) registry. In comparison, the vulnerability coverage of all the existing defense approaches combined only reaches 40.5%.

Qualifying and raising anti-money laundering alarms with deep learning

We propose a deep learning approach to qualify and raise anti-money laundering alarms in banks. The motivating idea is to replace predefined rules with latent features automatically extracted from transaction sequences. To this end, we experiment with recurrent and Transformer encoder layers. We test the approach with a large data set from Spar Nord Bank. Our best model employs gated recurrent units and self-attention. When used to qualify alarms raised by a traditional AML system, the model is able to reduce the number of false positives by more than 33.3% while retaining 98.8% of all true positives. In a small experiment, we also use the model to raise 75 new alarms on clients with high risk scores but with no traditional alarm inquiries in the last 12 months. After expert review, this prompted 26 clients to be reported to Danish authorities.

Understanding the Benefit of Being Patient in Payment Channel Networks

Scaling blockchain efficiency is crucial to its widespread usage in which the payment channel is one of the most prominent approaches. With payment channels and the network they construct, two users can move some transactions off-chain to avoid expensive and time-consuming on-chain settlements. Existing works are devoted to designing high-throughput payment channel networks (PCNs) or efficient PCN routing policies to reduce the fee charged by intermediate nodes. In this paper, we investigate the PCN routing from a different perspective by answering whether the routing fee of transactions can be saved through being a bit more patient. The key idea is to reorder the processing sequence of atomic transactions, other than to handle each of them separately and immediately. We present two mechanisms, one is periodic transaction processing and the other is purely strategic waiting. In the former, the incoming transactions in a short time interval are processed collectively. We formulate an optimization model to minimize their total routing fee and derive the optimal permutation of processing transactions as well as the routing policy for each of them. A <i>Shapley value</i> based scheme is presented to redistribute the benefit of reordering among the transactions efficiently and fairly. In the latter, we model the waiting time of a strategic transaction on a single payment channel as the <i>first passage time</i> problem in queuing theory when the transaction value is higher than the channel balance upon its arrival. By capturing the balance dynamics, we are able to calculate the recursive expression of waiting time distribution that is useful to gauge a user&#x0027;s cost of patience. Experimental results manifest that our cost redistribution mechanism can effectively save routing fees for all the transactions, and the waiting time distribution coincides with the model well.

Credit Card Fraud Detection project

Abstract: For some time, there has been a strong interest in the ethics of banking (Molyneaux, 2007; George, 1992), as well as the moral complexity of fraudulent behavior (Clarke, 1994). Fraud means obtaining services/goods and/or money by unethical means, and is a growing problem all over the world nowadays. Fraud deals with cases involving criminal purposes that, mostly, are difficult to identify. Credit cards are one of the most famous targets of fraud but not the only one; fraud can occur with any type of credit products, such as personal loans, home loans, and retail. Furthermore, the face of fraud has changed dramatically during the last few decades as technologies have changed and developed. A critical task to help businesses and financial institutions including banks is to take steps to prevent fraud and to deal with it efficiently and effectively, when it does happen (Anderson, 2007). Anderson (2007) has identified and explained the different types of fraud, which are as many and varied as the financial institution’s products and technologies, such as Transaction products: credit and debit cards and checks, Relationship to accounts first, second and third parties, Business processes: application and transaction, Manner and timing short versus long term, Identify misrepresentation: embellishment, theft and fabrication, Handling of transaction: lost or stolen, not received, skimming and at hand, Utilization counterfeit, not present, altered or unaltered, Technologies ATM and Internet. Solutions for integrating sequential information in the feature set exist in the literature. The predominant one consists in creating a set of features which are descriptive statistics obtained by aggregating the sequences of transactions of the cardholders (sum of amount, count of transactions, location from where the payment is being made etc..). We used this method as a benchmark feature engineering method for credit card fraud detection. However, this feature engineering strategy raised several research questions. First of all, we assumed that these descriptive statistics cannot fully describe the sequential properties of fraud and genuine patterns and that modelling the sequences of transactions could be beneficial for fraud detection. Moreover, the creation of these aggregated features is guided by expert knowledge whereas sequence modelling could be automated thanks to the class labels available for past transactions. Finally, the aggregated features are point estimates that may be complemented by a multi-perspective univariate description of the transaction context. We proposed a multi-perspective HMM-based automated feature engineering strategy in order to incorporate a broad spectrum of sequential information in the transactions feature sets. In fact, we model the genuine and fraudulent behaviors of the merchants and the card-holders according to two univariate features: the country from where the payment is being made and the amount of each of the transactions being made. Moreover, the HMMbased features are created in a supervised way and therefore lower the need of expert knowledge for the creation of the fraud detection system. In the end, our multiple perspectives HMM-based approach offers automated feature engineering to model temporal correlations so as to complement and possibly supplement the use of transaction aggregation strategies in order to improve the effectiveness of the classification task. Experiments conducted on a large real world credit card transaction dataset (46 million transactions from belgium card-holders between March and May 2015) have shown that the proposed HMMbased feature engineering allows for an increase in the detection of fraudulent transactions when combined with the state-ofthe-art expert-based feature engineering strategy for credit card fraud detection. To conclude, this work leads to a better understanding of what can be considered contextual knowledge for a credit card fraud detection task and how to include it in the classification task in order to get an increase in fraud detection. The method proposed can be extended to any supervised task with sequential datasets. The main aims are, firstly, to identify the different types of credit card fraud, and, secondly, to review alternative techniques that have been used in fraud detection. Indeed, transaction products, including credit cards, are the most vulnerable to fraud. On the other hand, other products such as personal loans and retail are also at risk, and have serious ethical conflicts. Keywords: Behavior and Location Analysis (BLA); Fraud Detection System (FDS); Automated Teller Machine (ATM); Credit Card Fraud Detection; DB: Database.

Market Directional Information Derived From (Time, Execution Price, Shares Traded) Sequence of Transactions. On The Impact From The Future

Market Directional Information Derived From (Time, Execution Price, Shares Traded) Sequence of Transactions. On The Impact From The Future

Attack and protection schemes on fabric isomorphic crosschain systems

Crosschain solves the problem of value transfer and asset interaction between different blockchains with different consensus mechanisms, or different infrastructures. It not only realizes the mutual communication of multiple independent blockchains, but also ensures the data consistency. However, existing crosschain technologies like notary mechanism, hash locking, distributed private key control, and sidechain/relaychain have potential vulnerabilities, resulting in different kinds of attacks. For example, some vulnerabilities that only exist in a blockchain like DDOS, overflow, double-spending, and so on, may have an impact on another secure blockchain due to the openness of the crosschain system. Even if all blockchains are safe enough, the security of the crosschain system cannot be guaranteed, and there are still many loopholes in the process of crosschain, which affect all blockchains. The security of crosschain has emerged as an important issue. In this work, based on BitXHub, an open-source project of crosschain, we are motivated to study the security of Fabric isomorphic crosschain system. First, we analyze the vulnerabilities in different layers of Fabric isomorphic crosschain systems, and discuss the principles of different kinds of attacks. Based on the principles, we discover and define five new crosschain attacks, including crosschain integer overflow attack, crosschain transaction forgery attack, crosschain transaction replay attack, crosschain transaction sequence attack, and crosschain routing attack. Second, we implement all the five attacks, and indicate the applicable scenarios and the boundary conditions in which each attack may occur. Third, we propose five schemes to prevent the corresponding attacks and evaluate the effectiveness of the protection schemes. We comprehensively discuss and analyze the schemes of attacks and their corresponding prevention toward Fabric isomorphic crosschain systems.