Verification Latency Research Articles

Subtraction of Hyperledger Fabric: A blockchain-based lightweight storage mechanism for digital evidences

After more than ten years of development, the application of blockchain has expanded beyond the financial field to include other sectors such as E-government and the medical field. One of the most renowned blockchain platforms is Hyperledger Fabric, which is widely adopted by existing works for managing digital evidences (DEs). However, these works overlook the distinct differences between DEs and token-transfer transactions and often adopt the full implementation of Hyperledger Fabric, leveraging chaincode to manage the hash of DEs while keeping the original data off-chain to conserve storage resources. This approach poses challenges such as off-chain data loss, reduced data verification efficiency, and increased resource consumption. In this paper, we introduce a lightweight storage mechanism called Blockchain Zhengju Keeper (BZK) that addresses these issues. In BZK, we subtract Hyperledger Fabric by (1) modifying the endorsement policy to reduce the risk of off-chain data loss, (2) simplifying the DE upload-onchain process while keeping the parallel execution, and (3) abandoning chaincode for DE verification to reduce latency. Comprehensive simulation experiments demonstrate the practicality of the proposed mechanism. Furthermore, compared to Hyperledger Fabric, BZK can reduce storage resource consumption by 68% and lower verification latency by 97%.

Credit Card Fraud Detection Challenges and Solutions: A Review

Credit card fraud has become an increasing problem due to the growing reliance on electronic payment systems and technological advances that have improved fraud techniques. Numerous financial institutions are looking for the best ways to leverage technological advancements to provide better services to their end users, and researchers used various protection methods to provide security and privacy for credit cards. Therefore, it is necessary to identify the challenges and the proposed solutions to address them. This review provides an overview of the most recent research on the detection of fraudulent credit card transactions to protect those transactions from tampering or improper use, which includes imbalance classes, concept drift, and verification latency problems using machine learning and deep learning. It also provides valuable information for academic and industrial researchers and opens new avenues for research aimed at developing robust fraud detection systems.

Anticipative Bayesian classification for data streams with verification latency

Most of the existing adaptive classification algorithms in non-stationary data streams require recent labelled data for their updates. Such recent labels are often missing. For stream classification under verification latency only few approaches exist. Most of them assume clustered data or homogeneous drift in all features, which limits their applicability. We address this by proposing Anticipative Bayesian stream Classifier (ABClass), an approach that is capable of integrating and automatically selecting from different components. In its Bayesian classification framework, ABClass combines density estimation techniques, extended to extrapolate drift patterns over time, with unsupervised parameter tuning and unsupervised model selection. ABClass allows for multivariate density estimation and extrapolation techniques. In this work, we assume conditional independence between features given the class label for modelling feature-specific drift patterns. ABClass is generative and can also be used for explaining and visualising concept drift patterns. It is generic, making it easy to include further types of drift models, both for the class-conditional feature distribution and for the class prior distribution. The experimental evaluation on several real-world data streams shows its competitiveness compared to other state-of-the-art approaches. ABClass is in most cases ten- to hundred-times faster than its competitors, both for model fitting and for prediction.

A blockchain-based scheme for edge–edge collaboration management in time-sensitive networking

Edge–edge collaboration is the main collaboration mode for edge computing (EC) in time-sensitive networking (TSN). This distributed collaboration mode, in which the computing work is implemented collaboratively by the edge computing nodes (ECNs) without the involvement of cloud servers, poses challenges to collaboration management. However, most existing methods of ECN collaboration only focus on the scheduling of computing resources rather than management process design and rarely consider the transparency, security, and fair distribution guarantee mechanism of collaboration benefits. To address this issue, we propose a blockchain-based and decentralized management scheme for ECNs autonomous collaboration in TSN. First, we design an edge–edge collaboration management framework and its workflow based on blockchain for EC in TSN (TECChain), which combines blockchain with TSN technology and EC paradigm. To solve the consensus problem in TECChain, the proof of diligence (PoD) and delegated PoD (DPoD) consensus mechanisms are designed. Furthermore, by leveraging the high-precision synchronous clock foundation of TSN, we propose two time-slot-driven consensus algorithms, named sequential decision-making based on DPoD (S-DPoD) and Byzantine fault tolerance based on DPoD (BFT-DPoD) respectively. Theoretical analysis results show that the proposed algorithms perform better in terms of security, transactions per second (TPS), energy saving, etc., compared to other algorithms. Comparison experiments reveal that S-DPoD consumes fewer interactive messages and has higher Byzantine fault tolerance and success rate of consensus than BFT-DPoD, but BFT-DPoD has lower transaction verification latency and larger TPS.

Adaptation for Automated Drift Detection in Electromechanical Machine Monitoring.

Practical machine learning applications for streaming data can involve concept drift (the change in statistical properties of data over time), one-shot or few-shot learning (starting with only one or a few examples for each class), a scarcity of representative training data, and extreme verification latency (only the initial dataset has ground-truth labels). This work presents a framework for organizing signal processing and machine learning techniques to provide adaptive classification and drift detection. Nonintrusive load monitoring (NILM) serves as an ideal case study, as modern sensing solutions provide a wellspring of electromechanical data sources. There is a lack of training datasets that generalize across different load and fault scenarios. Accordingly, training must be accomplished with a limited set of data when deploying a NILM to a new power system. Also, loads can exhibit concept drift over time either due to faults or normal variation. NILM field data is used as an illustrative case study to demonstrate the proposed framework for adaptation and drift tracking.

Machine Learning Approach for Credit Score Predictions

This paper addresses the problem of managing the significant rise in requests for credit products that banking and financial institutions face. The aim is to propose an adaptive, dynamic heterogeneous ensemble credit model that integrates the XGBoost and Support Vector Machine models to improve the accuracy and reliability of risk assessment credit scoring models. The method employs machine learning techniques to recognise patterns and trends from past data to anticipate future occurrences. The proposed approach is compared with existing credit score models to validate its efficacy using five popular evaluation metrics, Accuracy, ROC AUC, Precision, Recall and F1_Score. The paper highlights credit scoring models’ challenges, such as class imbalance, verification latency and concept drift. The results show that the proposed approach outperforms the existing models regarding the evaluation metrics, achieving a balance between predictive accuracy and computational cost. The conclusion emphasises the significance of the proposed approach for the banking and financial sector in developing robust and reliable credit scoring models to evaluate the creditworthiness of their clients.

Novelty detection for multi-label stream classification under extreme verification latency

Multi-Label Stream Classification (MLSC) is the classification streaming examples into multiple classes simultaneously. Since new classes may emerge during the streaming process (concept evolution) and known classes may change over time (concept drift) it is challenging task. In real situations, concept drift and concept evolution occur in scenarios where the actual labels of arriving examples are never available; hence it is impractical to update decision models in a supervised fashion. This is known as Extreme Verification Latency, a topic that has not been well investigated in MLSC literature. This paper proposes a new method called MultI-label learNing Algorithm for Data Streams with Binary Relevance transformation (MINAS-BR), integrated with a Novelty Detection (ND) procedure for detecting concept evolution and concept drift, updating the model in an unsupervised fashion. Furthermore, since the label space is not static, we propose a new evaluation methodology for MLSC under extreme verification latency. Experiments over synthetic and real-world data sets with different concept drift and concept evolution scenarios confirmed the strategies employed in the MINAS-BR and presented relevant advances for handling streaming multi-label data.

DPB-MA: Low-Latency Message Authentication Scheme Based on Distributed Verification and Priority in Vehicular Ad Hoc Network

Vehicular ad hoc networks (VANETs) serve as crucial means to ensure safe transportation systems and enhance driving convenience. Ensuring authentication between vehicles and units is the most essential line of defense for VANETs systems. Various schemes of message authentication are put forward to realize safe communication in VANETs. However, these schemes mainly pay attention to privacy and security issues, and are not fit for high-density traffic environments where many messages should be verified by a roadside unit (RSU). At present, achieving the low-latency authentication under the security and privacy of messages remains a critical requirement and challenge. For addressing this problem, this study puts forward a novel low-latency message authentication scheme based on the distributed authentication and message priority (DPB-MA), which can minimize message verification latency through cooperation between the RSU and cooperative vehicles (CVs). In addition, a message priority strategy has been designed to ensure that urgent messages are preferentially verified. According to the security proof with tight reduction, the DPB-MA is existentially unforgeable against adaptive chosen message attack (EU-CMA) in the random oracle model (ROM). Furthermore, our DPB-MA achieves full message aggregation, which also greatly optimizes the communication overhead and authentication efficiency. Performance analysis indicates that our scheme works well in high-density traffic scenarios and is more suitable for securing emergency messages with ultra-low latency requirements.

Towards Reliable Online Just-in-Time Software Defect Prediction

Throughout its development period, a software project experiences different phases, comprises modules with different complexities and is touched by many different developers. Hence, it is natural that problems such as Just-in-Time Software Defect Prediction (JIT-SDP) are affected by changes in the defect generating process (concept drifts), potentially hindering predictive performance. JIT-SDP also suffers from delays in receiving the labels of training examples (verification latency), potentially exacerbating the challenges posed by concept drift and further hindering predictive performance. However, little is known about what types of concept drift affect JIT-SDP and how they affect JIT-SDP classifiers in view of verification latency. This work performs the first detailed analysis of that. Among others, it reveals that different types of concept drift together with verification latency significantly impair the stability of the predictive performance of existing JIT-SDP approaches, drastically affecting their reliability over time. Based on the findings, a new JIT-SDP approach is proposed, aimed at providing higher and more stable predictive performance (i.e., reliable) over time. Experiments based on ten GitHub open source projects show that our approach was capable of produce significantly more stable predictive performances in all investigated datasets while maintaining or improving the predictive performance obtained by state-of-art methods.

Adaptive Learning With Extreme Verification Latency in Non-Stationary Environments

Existing Data Stream Mining algorithms assume the availability of labelled and balanced data streams. However, in many real-world applications such as Robotics, Weather Monitoring, Fraud-Detection systems, Cyber Security, and Human Activity Recognition, a vast amount of high-speed data is generated by Internet of Things sensors and real-time data on the Internet are unlabelled. Furthermore, the prediction models need to learn in Non-Stationary Environments due to evolving concepts. Manual labelling of these data streams is not practical due to the need for domain expertise and the time-resource-prohibitive nature of the required effort. To deal with such scenarios, existing approaches are self-Learning or Cluster-Guided Classification (CGC) which predict the pseudo-labels, which further update the prediction models. Previous studies have yet to establish a clear and conclusive view as to when, and why one pseudo-labelling approach should be preferable to another and what causes an approach to fail. In this research, we propose a novel approach, “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Predictor for Streaming Data with Scarce Labels</i> ” (PSDSL), which is capable of intelligently switching between self-learning, CGC and micro-clustering strategies, based on the problem it is applied to, i.e., the different characteristics of the data streams. In PSDSL a novel approach called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Envelope-Clustering</i> has been introduced to resolve the conflict during the cluster labelling which suggested a confidence measure approach to ensure the quality and correctness of labels assigned to the clusters. The auto parameter tuning mechanism of PSDSL eliminates the human dependency and determines the best value of number of centroids from initial labelled data. The predictive performance of the PSDSL is evaluated on non-stationary datasets, synthetic data-streams, and real-world datasets. The approach has shown promising results on randomised datasets as well as on synthetic data-streams, as compared with state-of-the-art approaches. This is the first large-scale study on an adaptive extreme verification approach that supports automatic parameter tuning and intelligent switching of pseudo-labelling strategy, thus reducing the dependency of machine learning on human input.

Stream-based active learning for sliding windows under the influence of verification latency

Stream-based active learning (AL) strategies minimize the labeling effort by querying labels that improve the classifier’s performance the most. So far, these strategies neglect the fact that an oracle or expert requires time to provide a queried label. We show that existing AL methods deteriorate or even fail under the influence of such verification latency. The problem with these methods is that they estimate a label’s utility on the currently available labeled data. However, when this label would arrive, some of the current data may have gotten outdated and new labels have arrived. In this article, we propose to simulate the available data at the time when the label would arrive. Therefore, our method Forgetting and Simulating (FS) forgets outdated information and simulates the delayed labels to get more realistic utility estimates. We assume to know the label’s arrival date a priori and the classifier’s training data to be bounded by a sliding window. Our extensive experiments show that FS improves stream-based AL strategies in settings with both, constant and variable verification latency.

Improvement in credit card fraud detection using ensemble classification technique and user data

Financial fraud a serious problem in banking system. Credit card fraud growing with increasing Internet usage, as it becomes very simple to collect user data and do fraud transaction. Fortunately, all records including fraud and legit transactions are present in the financial record. Improved data mining techniques are now capable to find solutions for such outlier detections. Financial data freely available in many sources, but this data has some challenges like,1) the profile of legit and fraudulent behavior changes constantly, 2) there a class imbalance problem in dataset, because less than 3% transaction are fraud, 3) transaction verification latency also one more problem. All this data issues are handled using pre-processing techniques like cleaning and reduction. Main aim of this research to find out, output attribute is Fraud, with better time complexity. To this end, K-means, Random Forest and J48 algorithm used, and its accuracy rates are compared to find best fit pre-processing and machine learning algorithm. It observed that accuracy rate of Random Forest 93.8% when both global and local dataset used.

Accurate fraud detection in credit card transactions using hybrid heuristic and meta-heuristic algorithms

Credit card based online transactions, payment methods developed rapidly, Credit card fraud also additionally expanded simultaneously. The particular crime in banking system is credit card fraud. The main objective of proposed AFD-HHM technique is, to detect the fraudulent transaction with high accurate rate and solve the user side problems. Some most dangerous credit card crimes are credit card application fraud, phishing scams, credit card skimming, online sales fraud, Credit Card Imprints, which leads to following user problems such as class imbalance problem, verification latency and concept drift. This paper focus to detect the fraud transactions with higher efficeiency rate solves the user side problems, high computational complexity problems. Initially, we propose Improved Egyptian Vulture Optimization (IEVO) algorithm (meta-heuristic) to grouping all cardholders into different groups based on the transaction behaviours. Second, we can aggregate each group transactions and compute the optimal attributes for each cardholder by using of trust rule strategy. Third, we propose Differential Evolution based Neural Network (DENN) classifier for differentiate transaction as normal or fraud. The result of proposed approach gives better accuracy with higher rate which is comparing with existing methods.

Cumulative Message Authentication Codes for Resource-Constrained IoT Networks

In resource-constrained Internet-of-Things networks, the use of conventional message authentication codes (MACs) to provide message authentication and integrity is not possible due to the large size of the MAC output. A straightforward yet naive solution to this problem is to employ a truncated MAC which undesirably sacrifices cryptographic strength in exchange for reduced communication overhead. In this article, we address this problem by proposing a novel approach for message authentication called cumulative MAC (CuMAC), which consists of two distinctive procedures: 1) aggregation and 2) accumulation. In aggregation, a sender generates compact authentication tags from segments of multiple MACs by using a systematic encoding procedure. In accumulation, a receiver accumulates the cryptographic strength of the underlying MAC by collecting and verifying the authentication tags. Embodied with these two procedures, CuMAC enables the receiver to achieve an advantageous tradeoff between the cryptographic strength and the latency in the processing of the authentication tags. Furthermore, for some latency-sensitive messages where this tradeoff may be unacceptable, we propose a variant of CuMAC that we refer to as CuMAC with speculation (CuMAC/S). In addition to the aggregation and accumulation procedures, CuMAC/S enables the sender and receiver to employ a speculation procedure for predicting future message values and precomputing the corresponding MAC segments. For the messages which can be reliably speculated, CuMAC/S significantly reduces the MAC verification latency without compromising the cryptographic strength. We have carried out a comprehensive evaluation of CuMAC and CuMAC/S through simulation and a prototype implementation on a real car.

Delayed labelling evaluation for data streams

A large portion of the stream mining studies on classification rely on the availability of true labels immediately after making predictions. This approach is well exemplified by the test-then-train evaluation, where predictions immediately precede true label arrival. However, in many real scenarios, labels arrive with non-negligible latency. This raises the question of how to evaluate classifiers trained in such circumstances. This question is of particular importance when stream mining models are expected to refine their predictions between acquiring instance data and receiving its true label. In this work, we propose a novel evaluation methodology for data streams when verification latency takes place, namely continuous re-evaluation. It is applied to reference data streams and it is used to differentiate between stream mining techniques in terms of their ability to refine predictions based on newly arriving instances. Our study points out, discusses and shows empirically the importance of considering the delay of instance labels when evaluating classifiers for data streams.

AMANDA: Semi-supervised density-based adaptive model for non-stationary data with extreme verification latency

Concept drift refers to an alteration in the relations between input and output data in the distribution over time. Thus, a gradual concept drift alludes to a smooth and gradual change in these relations. It generates a model obsolescence and quality decrease in predictions. Besides, there is a challenging task: the extreme verification latency to certify the labels. For batch scenarios, state-of-the-art methods do not properly tackle the problems aforementioned due to their high computational time, lack of representing samples of the drift or even for having several hyperparameters for tuning. Therefore, we propose AMANDA, a semi-supervised density-based adaptive model for non-stationary data. It has two variations: AMANDA-FCP, which selects a fixed number of samples; and AMANDA-DCP, which, in turn, dynamically selects samples from data. Our results indicate that these two variations outperform the state-of-the-art methods for almost all synthetic and real datasets, with an improvement up to 27.98% regarding the average error. AMANDA-FCP improved the results for a gradual concept drift, even with a small size of initial labeled data. Moreover, our results indicate that semi-supervised classifiers are improved when they work along with our density-based methods. Therefore, we emphasize the importance of research directions based on this approach.

A Novelty Detector and Extreme Verification Latency Model for Nonstationary Environments

Safe and reliable operation of systems relies on the use of online condition monitoring and diagnostic systems that aim to take immediate actions upon the occurrence of a fault. Model-based solutions are often not practical in nonstationary environments. Thus, the evolving data stream requires the data-driven model to be adaptive. In this paper, we propose a framework for the fault detection and classification that is accomplished on the data stream with both the gradual and abrupt drifts. The framework is only provided with prior information about the possible faults at the initial step; however, despite this, the framework can still detect the novel faults without receiving any update. Furthermore, an efficient fault classification algorithm is presented to maximize the efficiency of the proposed framework. Finally, the proposed framework is applied for diagnosing bearing defects in the induction motors to demonstrate its feasibility for industrial applications.

Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy.

Detecting frauds in credit card transactions is perhaps one of the best testbeds for computational intelligence algorithms. In fact, this problem involves a number of relevant challenges, namely: concept drift (customers' habits evolve and fraudsters change their strategies over time), class imbalance (genuine transactions far outnumber frauds), and verification latency (only a small set of transactions are timely checked by investigators). However, the vast majority of learning algorithms that have been proposed for fraud detection rely on assumptions that hardly hold in a real-world fraud-detection system (FDS). This lack of realism concerns two main aspects: 1) the way and timing with which supervised information is provided and 2) the measures used to assess fraud-detection performance. This paper has three major contributions. First, we propose, with the help of our industrial partner, a formalization of the fraud-detection problem that realistically describes the operating conditions of FDSs that everyday analyze massive streams of credit card transactions. We also illustrate the most appropriate performance measures to be used for fraud-detection purposes. Second, we design and assess a novel learning strategy that effectively addresses class imbalance, concept drift, and verification latency. Third, in our experiments, we demonstrate the impact of class unbalance and concept drift in a real-world data stream containing more than 75 million transactions, authorized over a time window of three years.

Adapting a classification rule to local and global shift when only unlabelled data are available

Abstract For evolving populations the training data and the test data need not follow the same distribution. Thus, the performance of a prediction model will deteriorate over the course of time. This requires the re-estimation of the prediction model after some time. However, in many applications e.g. credit scoring, new labelled data are not available for re-estimation due to verification latency, i.e. label delay. Thus, methods which enable a prediction model to adapt to distributional changes by using only unlabelled data are highly desirable. A shift adaptation method for binary classification is presented here. The model is based on mixture distributions. The conditional feature distributions are determined at the time where labelled data are available, and the unconditional feature distribution is determined at the time where new unlabelled data are accessible. These mixture distributions provide information on the old and the new positions of subpopulations. A transition model then describes how the subpopulations of each class have drifted to form the new unconditional feature distribution. Assuming that the conditional distributions are reorganised using a minimum of energy, a two-step estimation procedure results. First, for a given class prior distribution the transfer of probability mass is estimated such that the energy required to obtain the new unconditional distribution by a local transfer of the old conditional distributions is a minimum. Since the optimal solution of the resulting transportation problem measures the distance between the old and the new distributions, the change of the class prior distribution is found in a second step by solving the transportation problem for varying class prior distributions and selecting the value for which the objective function is a minimum. Using the solution of the transportation problem and the component parameters of the unconditional feature distribution, the new conditional feature distribution can be determined. This thus allows for a shift adaptation of the classification rule. The performance of the proposed model is investigated using a large real-world dataset on default rates in Danish companies. The results show that the shift adaptation improves classification results.

COMPOSE: A semisupervised learning framework for initially labeled nonstationary streaming data.

An increasing number of real-world applications are associated with streaming data drawn from drifting and nonstationary distributions that change over time. These applications demand new algorithms that can learn and adapt to such changes, also known as concept drift. Proper characterization of such data with existing approaches typically requires substantial amount of labeled instances, which may be difficult, expensive, or even impractical to obtain. In this paper, we introduce compacted object sample extraction (COMPOSE), a computational geometry-based framework to learn from nonstationary streaming data, where labels are unavailable (or presented very sporadically) after initialization. We introduce the algorithm in detail, and discuss its results and performances on several synthetic and real-world data sets, which demonstrate the ability of the algorithm to learn under several different scenarios of initially labeled streaming environments. On carefully designed synthetic data sets, we compare the performance of COMPOSE against the optimal Bayes classifier, as well as the arbitrary subpopulation tracker algorithm, which addresses a similar environment referred to as extreme verification latency. Furthermore, using the real-world National Oceanic and Atmospheric Administration weather data set, we demonstrate that COMPOSE is competitive even with a well-established and fully supervised nonstationary learning algorithm that receives labeled data in every batch.