Multiobjective genetic generation of fuzzy classifiers using the iterative rule learning

Cardenas, Edward Hinojosa; Camargo, Heloisa de Arruda

doi:10.1109/fuzz-ieee.2012.6251183

Cited by 6 publications

(4 citation statements)

References 36 publications

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“…Machine learning based prediction tools can be used to guess the next expected values; thus, they can be used in ADS to build the reference model by predicting normal incoming events, given the current ones. It is then possible to detect anomalies by selecting those next events which are not the ones anticipated by the prediction tools [13]- [15]. Machine learning approaches study algorithms that allow systems to derive general behaviors from data, and which can be either supervised or unsupervised.…”

Section: B Anomaly Detection Systemsmentioning

confidence: 99%

BAD: A Blockchain Anomaly Detection Solution

et al. 2020

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

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Section: B Anomaly Detection Systemsmentioning

confidence: 99%

BAD: A Blockchain Anomaly Detection Solution

et al. 2020

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“…Cárdenas et al 15 proposed a multi-objective genetic process to generate fuzzy sets of rules. The algorithm was divided into three stages.…”

Section: Moeas For Classificationmentioning

confidence: 99%

“…Some examples and state of the art in MOEAs are described in an extensive survey 10 . However, there are only few multi-objective algorithms for classification and their empirical assessment is quite limited 11,12,13,14,15 .…”

Section: Introductionmentioning

confidence: 99%

A gene expression programming algorithm for discovering classification rules in the multi-objective space

Guerrero-Enamorado¹,

Morell²,

Ventura³

2018

IJCIS

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Multi-objective evolutionary algorithms have been criticized when they are applied to classification rule mining, and, more specifically, in the optimization of more than two objectives due to their computational complexity. It is known that a multi-objective space is much richer to be explored than a single-objective space. In consequence, there are only few multi-objective algorithms for classification and their empirical assessed is quite limited. On the other hand, gene expression programming has emerged as an alternative to carry out the evolutionary process at genotypic level in a really efficient way. This paper introduces a new multi-objective algorithm for discovering classification rules, AR-NSGEP (Adaptive Reference point based Non-dominated Sorting with Gene Expression Programming). It is a multi-objective evolution of a previous single-objective algorithm. In AR-NSGEP, the multi-objective search was based on the wellknown R-NSGA-II algorithm, replacing GA with GEP technology. Four objectives led the rules-discovery process, three of them (sensitivity, specificity and precision) were focused on promoting accuracy and the fourth (simpleness) on the interpretability of rules. AR-NSGEP was evaluated on several benchmark data sets and compared against six rule-based classifiers widely used. The AR-NSGEP, with four-objectives, achieved a significant improvement of the AUC metric with espect to most of the algorithms assessed, while the predictive accuracy and number of rules in the obtained models reached to acceptable results.

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“…In the whole process of data classification, the most important part is to select an appropriate algorithm. Recently, machine learning approaches, such as rule learning [5], hidden Markov model [6], support vector machine [7] and neural network [4,8], have been employed in the field of data classification.…”

Section: Introductionmentioning

confidence: 99%

Data Classification in Internet of Things Based on Evolutionary Neural Network

Zhang

Wen

Sun

2013

AMR

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Data classification is the foundation for the intelligent identification and management of massive information in the internet of things. To classify the massive data accurately, an evolutionary neural network is presented. The input features and the structure of neural network are evolved simultaneously to consider their joint contribution to the performance of neural network. The sensitivity analysis is performed to guide the evolutionary algorithm to search the optimum solution. It can be seen from the experimental results that the proposed evolutionary algorithm optimized the structure of neural network and eliminate the tedious input features at the same time. The excellent classification accuracy is achieved finally.

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Multiobjective genetic generation of fuzzy classifiers using the iterative rule learning

Cited by 6 publications

References 36 publications

BAD: A Blockchain Anomaly Detection Solution

BAD: A Blockchain Anomaly Detection Solution

A gene expression programming algorithm for discovering classification rules in the multi-objective space

Data Classification in Internet of Things Based on Evolutionary Neural Network

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