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• Evaluating different models' perspectives for real-time adversarial attack detection in VANETs. • Identifying and preprocessing DSRC adversarial data using standard and fusion preprocessing approaches. • Selection for the golden ML model using a fuzzy decision-making framework for VANETs. • Evaluating the efficiency of the developed framework in four assessment processes. This paper addresses various issues in the literature concerning adversarial attack detection in Vehicular Ad-hoc Networks (VANETs). These issues include the failure to consider both normal and adversarial attack perspectives simultaneously in Machine Learning (ML) model development, the lack of diversity preprocessing techniques for VANETs communication datasets, the inadequate selection guidelines for real-time adversarial attack detection models, and the limited emphasis on explainability in adversarial attack detection. In this study, we propose an original fuzzy decision-making framework that incorporates multiple fusion standpoints. Our framework aims to evaluate multi-ML models for real-time adversarial attack detection in VANETs, focusing on three stages. The first stage involves identifying and preprocessing Dedicated Short-Range Communication (DSRC) data using standard and fusion preprocessing approaches. Two communication scenarios, normal and jammed, are considered, resulting in two DSRC datasets. In the second stage, we develop multi-ML models based on the DSRC datasets using standard preprocessing and feature fusion preprocessing for dataset-1 and dataset-2, respectively. The third stage evaluates the multi-ML models using a fuzzy decision-making approach based on the Fuzzy Decision by Opinion Score Method (FDOSM) and an adversarial attack decision fusion matrix. The External Fusion Decision (EFD) settings of the FDOSM address individual ranking variance, provide a unique rank and select the best model. Experimental results demonstrate that the K-Nearest Neighbors Algorithm (kNN) model achieves the highest explain score of 0.2048 in dataset-1 using standard preprocessing, while the Random Forest (RF) model applied to dataset-2 using fusion preprocessing emerges as the most robust and golden model against adversarial attacks, with a score of 0.1819. This finding suggests that the fusion preprocessing approach using Principal Component Analysis (PCA) is more suitable for addressing normal and adversarial attack perspectives. Furthermore, our fuzzy framework undergoes evaluation in terms of systematic rank, sensitivity analysis, explainability analysis, and comparison analysis. Overall, this framework provides valuable insights for researchers and practitioners in VANETs, informing the execution, selection, and interpretation of multi-ML models to tackle adversarial attack detection problems effectively. The new fuzzy framework demonstrates that multi-ML models based on feature fusion preprocessing are more effective. [ABSTRACT FROM AUTHOR]

Concept-cognitive learning (CCL), an emerging intelligence learning paradigm, has recently become a popular research subject in artificial intelligence and cognitive computing. A central notion of CCL is cognitive and learning things via concepts. In this process, concepts play a fundamental role when mining and fusing knowledge from data to wisdom. With the in-depth research and expansion of CCL in scopes, goals, and methodologies, some difficulties have gradually emerged, including some vague terminology, ambiguous views, and scattered research. Hence, a systematic and comprehensive review of the development process and advanced research about CCL is particularly necessary at the moment. This paper summarizes the theoretical significance, application value, and future development potential of CCL. More importantly, by synthesizing the reviewed related research, we can acquire some interesting results and answer three essential questions: (1) why examine a cognitive and learning framework based on concept? (2) what is the concept-cognitive learning? (3) how to make concept-cognitive learning? The findings of this work could act as a valuable guide for related studies in quest of a clear understanding of the closely related research issues around concept-cognitive learning. • It is the first paper that attempts to provide an in-depth analysis of the advancement of CCL. • It is a multi-view categorization of CCL in research scopes, goals, and methodologies. • It is an elucidation of the main research gaps and suggestions for the related study of CCL. • It acquires some interesting results by synthesizing the reviewed related research of CCL. [ABSTRACT FROM AUTHOR]

Traffic classification groups similar or related traffic data, which is one main stream technique of data fusion in the field of network management and security. With the rapid growth of network users and the emergence of new networking services, network traffic classification has attracted increasing attention. Many new traffic classification techniques have been developed and widely applied. However, the existing literature lacks a thorough survey to summarize, compare and analyze the recent advances of network traffic classification in order to deliver a holistic perspective. This paper carefully reviews existing network traffic classification methods from a new and comprehensive perspective by classifying them into five categories based on representative classification features, i.e., statistics-based classification, correlation-based classification, behavior-based classification, payload-based classification, and port-based classification. A series of criteria are proposed for the purpose of evaluating the performance of existing traffic classification methods. For each specified category, we analyze and discuss the details, advantages and disadvantages of its existing methods, and also present the traffic features commonly used. Summaries of investigation are offered for providing a holistic and specialized view on the state-of-art. For convenience, we also cover a discussion on the mostly used datasets and the traffic features adopted for traffic classification in the review. At the end, we identify a list of open issues and future directions in this research field. • Comprehensive evaluation criteria to assess classification methods. • A thorough review on traffic classification methods. • A discussion on advantages and disadvantages of classification methods. • An analysis on classification granularity by dividing it into four levels. • Exploration of open issues and future research directions. [ABSTRACT FROM AUTHOR]

Estimating interest rates is a typical multivariate prediction problem that has garnered considerable attention in the finance industry. However, the rising complexity of the prediction environment has presented significant obstacles to the methodology and application of traditional single-scale rational prediction models. Considering this challenge, the paper presents a new multivariate fusion prediction system that aims to tackle the following issues: (1) How to mine prediction information from multi-scale information systems (MSISs)? (2) How to describe the irrational behavior of decision-makers (DMs) in prediction processes? (3) How to improve the prediction performance and generalization capability of the models? Specifically, the score function of each feature under MSISs is primarily established by applying scale rules, regret theory (RT), and dominance relations to thoroughly explore prediction information across varying scales. Second, an adaptive S3WD model is developed for feature selection, which leverages the feature score distribution and the sequential three-way decision (S3WD) concept to avoid the problem of dimensional disasters, thereby improving the accuracy of selected features and reducing the learning cost. Furthermore, to enhance the effectiveness and rationality of the kernel extreme learning machine (KELM), an improved version known as IKELM is developed, which combines the regret-based comprehensive score of each sample. Finally, a multivariate fusion prediction system is constructed and abbreviated as an S3WD-IKELM-PSO system, where the particle swarm optimization (PSO) algorithm is employed to determine optimal parameters. It is worth noting that actual interest rate estimation datasets are utilized to verify the feasibility and validity of the proposed prediction system. According to the experimental results, the S3WD-IKELM-PSO system exhibits superior prediction performance and generalization ability compared to existing machine learning models. • A regret theory-based multivariate prediction system in MSISs is explored. • A regret-based feature evaluation system in an MSIS is designed. • An adaptive S3WD model for feature selection is proposed. • An improved KELM model is proposed. [ABSTRACT FROM AUTHOR]

• We sum up a group of main challenges that data fusion might face. • We propose a thorough list of requirements to evaluate data fusion methods. • We review the literature of data fusion based on machine learning. • We comment on how a machine learning method can ameliorate fusion performance. • We present significant open issues and valuable future research directions. Data fusion is a prevalent way to deal with imperfect raw data for capturing reliable, valuable and accurate information. Comparing with a range of classical probabilistic data fusion techniques, machine learning method that automatically learns from past experiences without explicitly programming, remarkably renovates fusion techniques by offering the strong ability of computing and predicting. Nevertheless, the literature still lacks a thorough review of the recent advances of machine learning for data fusion. Therefore, it is beneficial to review and summarize the state of the art in order to gain a deep insight on how machine learning can benefit and optimize data fusion. In this paper, we provide a comprehensive survey on data fusion methods based on machine learning. We first offer a detailed introduction to the background of data fusion and machine learning in terms of definitions, applications, architectures, processes, and typical techniques. Then, we propose a number of requirements and employ them as criteria to review and evaluate the performance of existing fusion methods based on machine learning. Through the literature review, analysis and comparison, we finally come up with a number of open issues and propose future research directions in this field. [ABSTRACT FROM AUTHOR]