Siwei Qiao;Xinghua Liu;Gaoxi Xiao;Peng Wang;Shuzhi Sam Ge
A reliable adaptive-memory-derivative (AMD) event-triggered quantized sliding mode load frequency control (QSMLFC) method is proposed for the multiarea interconnected wind power system under frequency-based deception attacks. An AMD event-trigger scheme is proposed to promote the wind power system operation while saving the network resources, and the reliable AMD event-triggered QSMLFC method aims to reduce the frequency deviations of the interconnected wind power systems. A frequency-based deception attack model is developed for analyzing the security issues in network communications for wind power systems. The hysteresis quantizer is used to lower the communication rate. To validate the correctness of the control method, a sufficient reliability criterion is derived to prove the applicability of the AMD event-triggered QSMLFC. Three numerical examples and an IEEE 39-bus system simulation are presented to demonstrate that the reliable AMD event-triggered QSMLFC method can provide satisfactory stability performance for the wind power system under frequency-based deception attacks.
{"title":"Reliable Control of Wind Power Systems Under Frequency-Based Deception Attacks: AMD Event-Triggered Strategy","authors":"Siwei Qiao;Xinghua Liu;Gaoxi Xiao;Peng Wang;Shuzhi Sam Ge","doi":"10.1109/TR.2024.3384063","DOIUrl":"10.1109/TR.2024.3384063","url":null,"abstract":"A reliable adaptive-memory-derivative (AMD) event-triggered quantized sliding mode load frequency control (QSMLFC) method is proposed for the multiarea interconnected wind power system under frequency-based deception attacks. An AMD event-trigger scheme is proposed to promote the wind power system operation while saving the network resources, and the reliable AMD event-triggered QSMLFC method aims to reduce the frequency deviations of the interconnected wind power systems. A frequency-based deception attack model is developed for analyzing the security issues in network communications for wind power systems. The hysteresis quantizer is used to lower the communication rate. To validate the correctness of the control method, a sufficient reliability criterion is derived to prove the applicability of the AMD event-triggered QSMLFC. Three numerical examples and an IEEE 39-bus system simulation are presented to demonstrate that the reliable AMD event-triggered QSMLFC method can provide satisfactory stability performance for the wind power system under frequency-based deception attacks.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 1","pages":"2027-2040"},"PeriodicalIF":5.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141195988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The subdomain adaptation (SA) based intelligent cross-domain fault diagnosis methods aim to reduce the conditional distribution shift caused by variable working conditions. However, existing SA methods may be limited by the quality of pseudolabels, since misclassified pseudolabels will lead to alignment between irrelevant subdomains, resulting in erroneous category-invariant knowledge being accumulated. To tackle this, we present a dynamic subdomain pseudolabel correction and adaptation (DSPC-A) framework. Specifically, we propose an end-to-end pseudolabel correction algorithm, which integrates an auxiliary network to learn clean and general target label distribution from noisy pseudolabels. So that, the auxiliary network can guide the SA model to perform precise subdomain alignment using learned label distribution. Moreover, to allow the synergy training of the additional auxiliary network and SA model, we introduce an iterative learning strategy to dynamically perform pseudolabel correction and subdomain alignment. The iterative training makes two models complement each other, thus achieving better SA ability and diagnosis performance. The DSPC-A framework has been thoroughly verified under three fault diagnostic scenarios: cross load, cross fault severity, and cross mechanical equipment. Case study results demonstrate the superiority of the DSPC-A, which improves the SA performance by solely implementing simple pseudolabel correction methods without other complex techniques.
{"title":"Dynamic Subdomain Pseudolabel Correction and Adaptation Framework for Multiscenario Mechanical Fault Diagnosis","authors":"Chenxi Li;Huan Wang;Te Han","doi":"10.1109/TR.2024.3397913","DOIUrl":"10.1109/TR.2024.3397913","url":null,"abstract":"The subdomain adaptation (SA) based intelligent cross-domain fault diagnosis methods aim to reduce the conditional distribution shift caused by variable working conditions. However, existing SA methods may be limited by the quality of pseudolabels, since misclassified pseudolabels will lead to alignment between irrelevant subdomains, resulting in erroneous category-invariant knowledge being accumulated. To tackle this, we present a dynamic subdomain pseudolabel correction and adaptation (DSPC-A) framework. Specifically, we propose an end-to-end pseudolabel correction algorithm, which integrates an auxiliary network to learn clean and general target label distribution from noisy pseudolabels. So that, the auxiliary network can guide the SA model to perform precise subdomain alignment using learned label distribution. Moreover, to allow the synergy training of the additional auxiliary network and SA model, we introduce an iterative learning strategy to dynamically perform pseudolabel correction and subdomain alignment. The iterative training makes two models complement each other, thus achieving better SA ability and diagnosis performance. The DSPC-A framework has been thoroughly verified under three fault diagnostic scenarios: cross load, cross fault severity, and cross mechanical equipment. Case study results demonstrate the superiority of the DSPC-A, which improves the SA performance by solely implementing simple pseudolabel correction methods without other complex techniques.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 1","pages":"2421-2433"},"PeriodicalIF":5.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141196069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
At present, deep neural networks are at risk from backdoor attacks, but natural language processing (NLP) lacks sufficient research on backdoor attacks. To improve the invisibility of backdoor attacks, some innovative textual backdoor attack methods utilize modern language models to generate poisoned text with backdoor triggers, which are called feature space backdoor attacks. However, this article find that texts generated by the same language model without backdoor triggers also have a high probability of activating the backdoors they injected. Therefore, this article proposes a multistyle transfer-based backdoor attack that uses multiple text styles as the backdoor trigger. Furthermore, inspired by the ability of modern language models to distinguish between texts generated by different language models, this article proposes a paraphrase-based backdoor attack, which leverages the shared characteristics of sentences generated by the same paraphrase model as the backdoor trigger. Experiments have been conducted to demonstrate that both backdoor attack methods can be effective against NLP models. More importantly, compared with other feature space backdoor attacks, the poisoned samples generated by paraphrase-based backdoor attacks have improved semantic similarity.
{"title":"Leverage NLP Models Against Other NLP Models: Two Invisible Feature Space Backdoor Attacks","authors":"Xiangjun Li;Xin Lu;Peixuan Li","doi":"10.1109/TR.2024.3375526","DOIUrl":"10.1109/TR.2024.3375526","url":null,"abstract":"At present, deep neural networks are at risk from backdoor attacks, but natural language processing (NLP) lacks sufficient research on backdoor attacks. To improve the invisibility of backdoor attacks, some innovative textual backdoor attack methods utilize modern language models to generate poisoned text with backdoor triggers, which are called feature space backdoor attacks. However, this article find that texts generated by the same language model without backdoor triggers also have a high probability of activating the backdoors they injected. Therefore, this article proposes a multistyle transfer-based backdoor attack that uses multiple text styles as the backdoor trigger. Furthermore, inspired by the ability of modern language models to distinguish between texts generated by different language models, this article proposes a paraphrase-based backdoor attack, which leverages the shared characteristics of sentences generated by the same paraphrase model as the backdoor trigger. Experiments have been conducted to demonstrate that both backdoor attack methods can be effective against NLP models. More importantly, compared with other feature space backdoor attacks, the poisoned samples generated by paraphrase-based backdoor attacks have improved semantic similarity.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"73 3","pages":"1559-1568"},"PeriodicalIF":5.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140596448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the recent years, there has been a lot of focus on designing security for in-vehicle networks and detecting intrusions. Still, no countermeasure is perfect and most of the existing intrusion detection systems have a nonzero false negative rate, which implies that adversarial frames may still go undetected on the bus. Unfortunately, answers are largely missing for what will happen with the vehicle in such circumstances, i.e., how is the safety of the vehicle and bystanders affected by adversarial actions that go undetected, while there are little or no answers on the acceptable misclassification rates in real-world deployments. In this article, we attempt to provide such answers by pursuing an impact assessment for adversarial actions on the bus assuming low false negative rates. The assessment is based on the effects of such attacks on models for automatic emergency braking and adaptive cruise control systems that are implemented in Simulink, a commonly used tool for designing such systems in the automotive industry. To achieve this, we embed adversarial behavior into the Simulink model, according to recently reported attacks on in-vehicle controller area network buses. This allows us to assess the impact of adversarial actions according to existing safety standards and regulations.
{"title":"Cyberattacks on Adaptive Cruise Controls and Emergency Braking Systems: Adversary Models, Impact Assessment, and Countermeasures","authors":"Adriana Berdich;Bogdan Groza","doi":"10.1109/TR.2024.3373810","DOIUrl":"10.1109/TR.2024.3373810","url":null,"abstract":"In the recent years, there has been a lot of focus on designing security for in-vehicle networks and detecting intrusions. Still, no countermeasure is perfect and most of the existing intrusion detection systems have a nonzero false negative rate, which implies that adversarial frames may still go undetected on the bus. Unfortunately, answers are largely missing for what will happen with the vehicle in such circumstances, i.e., how is the safety of the vehicle and bystanders affected by adversarial actions that go undetected, while there are little or no answers on the acceptable misclassification rates in real-world deployments. In this article, we attempt to provide such answers by pursuing an impact assessment for adversarial actions on the bus assuming low false negative rates. The assessment is based on the effects of such attacks on models for automatic emergency braking and adaptive cruise control systems that are implemented in Simulink, a commonly used tool for designing such systems in the automotive industry. To achieve this, we embed adversarial behavior into the Simulink model, according to recently reported attacks on in-vehicle controller area network buses. This allows us to assess the impact of adversarial actions according to existing safety standards and regulations.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"73 2","pages":"1216-1230"},"PeriodicalIF":5.9,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140299256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Unmanned aerial vehicle (UAVs) have the advantages of high flexibility and ease of deployment, making it possible to provide mobile edge computing services as an aerial server for remote or hot spot areas, e.g., computation offloading. However, there are bottlenecks in guaranteeing the reliability of computing resource allocation and incentivizing their participation in edge services. In view of this, we study the computation offloading and resource pricing joint optimization problem in the UAV-enabled vehicular edge computing network. In this article, we first formulate the interaction between vehicles and one UAV as a Stackelberg game, which maximizes the profits of the UAV and the utilities of vehicles considering delay, energy consumption, and urgency. Then, we analyze the existence and uniqueness of Stackelberg equilibrium (SE) under uniform and discriminatory pricing schemes applying backward induction. Finally, we implement such SE in both complete interaction information and incomplete interaction information scenarios. Specifically, one Stackelberg game-based dynamic iterative decision algorithm (SDID) and one reinforcement learning (RL)-based joint optimization offloading and pricing algorithm (RLOP) are proposed to intelligently obtain offloading and pricing strategies, respectively. Simulation results show that our proposed SDID and RLOP achieve significant improvements in the utility, compared to other baseline algorithms.
{"title":"Stackelberg Game-Based Computation Offloading and Pricing in UAV Assisted Vehicular Networks","authors":"Liwei Geng;Hongbo Zhao;Changming Zou","doi":"10.1109/TR.2024.3399389","DOIUrl":"10.1109/TR.2024.3399389","url":null,"abstract":"Unmanned aerial vehicle (UAVs) have the advantages of high flexibility and ease of deployment, making it possible to provide mobile edge computing services as an aerial server for remote or hot spot areas, e.g., computation offloading. However, there are bottlenecks in guaranteeing the reliability of computing resource allocation and incentivizing their participation in edge services. In view of this, we study the computation offloading and resource pricing joint optimization problem in the UAV-enabled vehicular edge computing network. In this article, we first formulate the interaction between vehicles and one UAV as a Stackelberg game, which maximizes the profits of the UAV and the utilities of vehicles considering delay, energy consumption, and urgency. Then, we analyze the existence and uniqueness of Stackelberg equilibrium (SE) under uniform and discriminatory pricing schemes applying backward induction. Finally, we implement such SE in both complete interaction information and incomplete interaction information scenarios. Specifically, one Stackelberg game-based dynamic iterative decision algorithm (SDID) and one reinforcement learning (RL)-based joint optimization offloading and pricing algorithm (RLOP) are proposed to intelligently obtain offloading and pricing strategies, respectively. Simulation results show that our proposed SDID and RLOP achieve significant improvements in the utility, compared to other baseline algorithms.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 1","pages":"2333-2347"},"PeriodicalIF":5.0,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141153967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qifang Liu;Lu Jin;Hon Keung Tony Ng;Qingpei Hu;Dan Yu
Multiple performance characteristics (PCs) are common in modern products with complex structures and diverse functions. These PCs are usually dependent, with significant unit-specific variability among the multivariate degradation processes. Therefore, the associated degradation modeling for dependent multivariate degradation processes is important. This article proposes a novel multivariate $t$ degradation model for this purpose. Specifically, the dependence between multivariate degradation processes is captured by random drift parameters that follow a multivariate normal distribution, and the variation in diffusion parameters and variance–covariance is characterized by a gamma distribution. An expectation-maximization (EM) algorithm is employed for likelihood inference, and confidence intervals of the model parameters are constructed by normal approximation and bootstrap method. A theoretical exploration investigating the effects of model misspecification in multivariate degradation modeling is addressed. Monte Carlo simulation studies are performed to validate the effectiveness of the EM algorithm and the theoretical properties of the multivariate $t$ model. Finally, two illustrative examples are used to demonstrate the applicability and advantages of the proposed methods.
{"title":"Multivariate $t$ Degradation Processes for Dependent Multivariate Degradation Data","authors":"Qifang Liu;Lu Jin;Hon Keung Tony Ng;Qingpei Hu;Dan Yu","doi":"10.1109/TR.2024.3398652","DOIUrl":"10.1109/TR.2024.3398652","url":null,"abstract":"Multiple performance characteristics (PCs) are common in modern products with complex structures and diverse functions. These PCs are usually dependent, with significant unit-specific variability among the multivariate degradation processes. Therefore, the associated degradation modeling for dependent multivariate degradation processes is important. This article proposes a novel multivariate <inline-formula><tex-math>$t$</tex-math></inline-formula> degradation model for this purpose. Specifically, the dependence between multivariate degradation processes is captured by random drift parameters that follow a multivariate normal distribution, and the variation in diffusion parameters and variance–covariance is characterized by a gamma distribution. An expectation-maximization (EM) algorithm is employed for likelihood inference, and confidence intervals of the model parameters are constructed by normal approximation and bootstrap method. A theoretical exploration investigating the effects of model misspecification in multivariate degradation modeling is addressed. Monte Carlo simulation studies are performed to validate the effectiveness of the EM algorithm and the theoretical properties of the multivariate <inline-formula><tex-math>$t$</tex-math></inline-formula> model. Finally, two illustrative examples are used to demonstrate the applicability and advantages of the proposed methods.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 1","pages":"2265-2279"},"PeriodicalIF":5.0,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141146890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaodong Gou;Ao Zhang;Chengguang Wang;Yan Liu;Xue Zhao;Shunkun Yang
Accurate fault localization renders software test resource allocation and maintenance cost-efficient. However, this is challenging when there are false alarm repercussions caused by module coupling of complex software. In this article, therefore, we propose a new method for multiple software fault localization from the perspective of network spectrum based on a graph neural network model. First, we constructed the network model of the software under test to represent the coupling relationships among software modules based on complex network theory. In addition, test suits were executed and recorded to construct the program spectrum. Subsequently, the software network and program spectrum were fused into the network spectrum, and we reprocessed it with feature dimension reduction, normalization, and graph-based class-imbalance treatment. The graph neural network was then used to construct a multiple-fault location model based on the processed network spectrum. Empirical studies were performed on the Defects4J dataset. The experimental results indicated that the proposed method outperformed six baseline methods (with an average improvement of 13.03% on the T-EXAMscore). This study is expected to provide insights into more smart software quality and reliability assurance.
{"title":"Software Fault Localization Based on Network Spectrum and Graph Neural Network","authors":"Xiaodong Gou;Ao Zhang;Chengguang Wang;Yan Liu;Xue Zhao;Shunkun Yang","doi":"10.1109/TR.2024.3374410","DOIUrl":"10.1109/TR.2024.3374410","url":null,"abstract":"Accurate fault localization renders software test resource allocation and maintenance cost-efficient. However, this is challenging when there are false alarm repercussions caused by module coupling of complex software. In this article, therefore, we propose a new method for multiple software fault localization from the perspective of network spectrum based on a graph neural network model. First, we constructed the network model of the software under test to represent the coupling relationships among software modules based on complex network theory. In addition, test suits were executed and recorded to construct the program spectrum. Subsequently, the software network and program spectrum were fused into the network spectrum, and we reprocessed it with feature dimension reduction, normalization, and graph-based class-imbalance treatment. The graph neural network was then used to construct a multiple-fault location model based on the processed network spectrum. Empirical studies were performed on the Defects4J dataset. The experimental results indicated that the proposed method outperformed six baseline methods (with an average improvement of 13.03% on the T-EXAMscore). This study is expected to provide insights into more smart software quality and reliability assurance.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"73 4","pages":"1819-1833"},"PeriodicalIF":5.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140201747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accelerated life testing (ALT) experiments are widely used in reliability studies on extremely durable products having large mean times to failure. Simple step-stress ALT (SSALT) is a special class of ALT that tests the units under investigation on two different conditions by changing the stress factor (e.g., temperature, voltage, or pressure) at a predetermined time point of the experiment. In this study, we propose the maximum product of spacings (MPS) technique for estimating the unknown lifetime parameters as an alternative to the maximum likelihood (ML), which in some cases is not possible to be used. The MPS estimator is defined for a simple SSALT model under Type-II censoring and proved to be asymptotically equivalent to the corresponding ML estimator. The specific case of Weibull lifetimes sharing a common shape parameter on both stress levels under the tampered failure rate assumption is considered in more detail. Existence and uniqueness results are shown for the point estimators of both methods and an adjusted bootstrap algorithm is suggested for constructing interval inference procedures. Further, the ML and MPS approaches are compared via a simulation study and applied to two real lifetime data examples.
加速寿命试验（ALT）广泛应用于对平均失效时间较长的耐用产品进行可靠性研究。简单阶跃应力加速寿命测试（SSALT）是一种特殊的加速寿命测试，它通过在实验的预定时间点改变应力因子（如温度、电压或压力），在两种不同的条件下对被测单元进行测试。在本研究中，我们提出了最大间距积（MPS）技术，用于估计未知寿命参数，以替代在某些情况下无法使用的最大似然法（ML）。MPS 估计器是为 II 型普查下的简单 SSALT 模型定义的，并证明其与相应的 ML 估计器在渐近上是等效的。更详细地考虑了在篡改失效率假设下，两个应力水平上的 Weibull 寿命具有共同形状参数的特定情况。结果显示了两种方法的点估计器的存在性和唯一性，并提出了一种用于构建区间推断程序的调整自举算法。此外，还通过模拟研究对 ML 和 MPS 方法进行了比较，并将其应用于两个实际寿命数据实例。
{"title":"Product of Spacings Estimation in Step-Stress Accelerated Life Testing: An Alternative to Maximum Likelihood","authors":"Maria Kateri;Nikolay I. Nikolov","doi":"10.1109/TR.2024.3369977","DOIUrl":"10.1109/TR.2024.3369977","url":null,"abstract":"Accelerated life testing (ALT) experiments are widely used in reliability studies on extremely durable products having large mean times to failure. Simple step-stress ALT (SSALT) is a special class of ALT that tests the units under investigation on two different conditions by changing the stress factor (e.g., temperature, voltage, or pressure) at a predetermined time point of the experiment. In this study, we propose the maximum product of spacings (MPS) technique for estimating the unknown lifetime parameters as an alternative to the maximum likelihood (ML), which in some cases is not possible to be used. The MPS estimator is defined for a simple SSALT model under Type-II censoring and proved to be asymptotically equivalent to the corresponding ML estimator. The specific case of Weibull lifetimes sharing a common shape parameter on both stress levels under the tampered failure rate assumption is considered in more detail. Existence and uniqueness results are shown for the point estimators of both methods and an adjusted bootstrap algorithm is suggested for constructing interval inference procedures. Further, the ML and MPS approaches are compared via a simulation study and applied to two real lifetime data examples.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"73 3","pages":"1433-1445"},"PeriodicalIF":5.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140166272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ponzi contracts are fraudulent investment scams that promise high returns with little risk to investors. However, existing methods for detecting Ponzi contracts have several limitations. For example, they struggle to deal with the class imbalance problem, and their analysis of function call transactions is inadequate, resulting in redundant features. To tackle the challenges of detecting Ponzi contracts, we present PonziFinder, a novel approach that leverages convolutional-based edge-enhanced graph neural network and attention mechanism for the classification of contract transaction graphs. In contrast to previous methods, we not only consider transaction value and timestamp but also analyze transaction input to standardize and sort transactions. We extract node and edge features that capture the unique characteristics of Ponzi contracts. The edge feature, reflecting interaccount correlation, enhances the propagation and updating of node features for effective Ponzi contract detection. To prevent oversmoothing of node embedding caused by the shallow transaction graph and extract important account node information, we introduce an attention-based global layerwise aggregation mechanism (ALGA) for generating the final contract graph representation for classification. Moreover, we optimize the node feature set and use an effective strategy based on undersampling and ensemble learning to address the issue of class imbalance. Experimental results show that PonziFinder can detect all types of Ponzi contracts (100%) with 97% accuracy when there is sufficient transaction data, outperforming other models. The analysis of input values and the ALGA mechanism are experimentally shown to improve accuracy by 4% and 2%, respectively. In summary, PonziFinder is a novel and effective method for detecting Ponzi contracts. Our approach addresses the limitations of existing methods and demonstrates significant improvements in accuracy and efficiency.
{"title":"PonziFinder: Attention-Based Edge-Enhanced Ponzi Contract Detection","authors":"Yingying Chen;Bixin Li;Yan Xiao;Xiaoning Du","doi":"10.1109/TR.2024.3370734","DOIUrl":"10.1109/TR.2024.3370734","url":null,"abstract":"<italic>Ponzi contracts</i> are fraudulent investment scams that promise high returns with little risk to investors. However, existing methods for detecting Ponzi contracts have several limitations. For example, they struggle to deal with the class imbalance problem, and their analysis of function call transactions is inadequate, resulting in redundant features. To tackle the challenges of detecting Ponzi contracts, we present PonziFinder, a novel approach that leverages convolutional-based edge-enhanced graph neural network and attention mechanism for the classification of contract transaction graphs. In contrast to previous methods, we not only consider transaction value and timestamp but also analyze transaction input to standardize and sort transactions. We extract node and edge features that capture the unique characteristics of Ponzi contracts. The edge feature, reflecting interaccount correlation, enhances the propagation and updating of node features for effective Ponzi contract detection. To prevent oversmoothing of node embedding caused by the shallow transaction graph and extract important account node information, we introduce an attention-based global layerwise aggregation mechanism (ALGA) for generating the final contract graph representation for classification. Moreover, we optimize the node feature set and use an effective strategy based on undersampling and ensemble learning to address the issue of class imbalance. Experimental results show that PonziFinder can detect all types of Ponzi contracts (100%) with 97% accuracy when there is sufficient transaction data, outperforming other models. The analysis of input values and the ALGA mechanism are experimentally shown to improve accuracy by 4% and 2%, respectively. In summary, PonziFinder is a novel and effective method for detecting Ponzi contracts. Our approach addresses the limitations of existing methods and demonstrates significant improvements in accuracy and efficiency.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 1","pages":"2305-2319"},"PeriodicalIF":5.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140166166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Huixiong Wang;Xing Pan;Zeqing Liu;Yuheng Dang;Dongpao Hong
In recent years, network-based resilience assessment has aroused attention because of its strong link to the stability and dependability of complex systems. Previous network-based studies have contributed to the definition and quantification of system resilience, but an integral and consistent framework is still lacking for the procedure of resilience analysis for general complex systems, and system responses and strains induced by multiple rounds of disruptions have not been well studied. In this manuscript, dynamic resilience is defined as a system's ability to resist loss of resilience and to adapt to successive resilience processes. We employ a four-factor measurement system, instead of a single-factor measurement, for the resilience analysis. A comprehensive framework for resilience assessment is proposed for dynamic resilience modeling in general complex systems to address various concerns in complex systems. A case study demonstrates the application of the proposed framework by simulating disruption intensity and recovery volume on a model communication system. We find that the assessment of dynamic resilience produces distinct results for different resilience aspects, while optimizations can help us identify solutions when all resilience factors are stabilized in the long-term dynamic resilience process. The dependability of the simulation results is verified using noise techniques in signal processing.
{"title":"A Framework for the Network-Based Assessment of System Dynamic Resilience","authors":"Huixiong Wang;Xing Pan;Zeqing Liu;Yuheng Dang;Dongpao Hong","doi":"10.1109/TR.2024.3371215","DOIUrl":"10.1109/TR.2024.3371215","url":null,"abstract":"In recent years, network-based resilience assessment has aroused attention because of its strong link to the stability and dependability of complex systems. Previous network-based studies have contributed to the definition and quantification of system resilience, but an integral and consistent framework is still lacking for the procedure of resilience analysis for general complex systems, and system responses and strains induced by multiple rounds of disruptions have not been well studied. In this manuscript, dynamic resilience is defined as a system's ability to resist loss of resilience and to adapt to successive resilience processes. We employ a four-factor measurement system, instead of a single-factor measurement, for the resilience analysis. A comprehensive framework for resilience assessment is proposed for dynamic resilience modeling in general complex systems to address various concerns in complex systems. A case study demonstrates the application of the proposed framework by simulating disruption intensity and recovery volume on a model communication system. We find that the assessment of dynamic resilience produces distinct results for different resilience aspects, while optimizations can help us identify solutions when all resilience factors are stabilized in the long-term dynamic resilience process. The dependability of the simulation results is verified using noise techniques in signal processing.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 1","pages":"2448-2458"},"PeriodicalIF":5.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140166162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：