PDF Malware Detection Based on Fuzzy Unordered Rule Induction Algorithm (FURIA)

Mejjaouli, Sobhi; Guizani, Sghaier

doi:10.3390/app13063980

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…The present research employs the k-fold cross-validation technique, where k is equal to 10, to construct the models used for experimentation. The rationale behind utilizing the CV technique is rooted in its ability to withstand data quality issues that may result in model overfitting, as shown in previous studies [4,32,34]. The training and test datasets were randomly partitioned to mitigate the presence of duplicate values or recurring patterns.…”

Section: Methodsmentioning

confidence: 99%

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Software Requirement Risk Prediction Using Enhanced Fuzzy Induction Models

Mamman,

Balogun,

Basri

et al. 2023

Electronics

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The development of most modern software systems is accompanied by a significant level of uncertainty, which can be attributed to the unanticipated activities that may occur throughout the software development process. As these modern software systems become more complex and drawn out, escalating software project failure rates have become a critical concern. These unforeseeable uncertainties are known as software risks, and they emerge from many risk factors inherent to the numerous activities comprising the software development lifecycle (SDLC). Consequently, these software risks have resulted in massive revenue losses for software organizations. Hence, it is imperative to address these software risks, to curb future software system failures. The subjective risk assessment (SRM) method is regarded as a viable solution to software risk problems. However, it is inherently reliant on humans and, therefore, in certain situations, imprecise, due to its dependence on an expert’s knowledge and experience. In addition, the SRM does not allow repeatability, as expertise is not easily exchanged across the different units working on a software project. Developing intelligent modelling methods that may offer more unbiased, reproducible, and explainable decision-making assistance in risk management is crucial. Hence, this research proposes enhanced fuzzy induction models for software requirement risk prediction. Specifically, the fuzzy unordered rule induction algorithm (FURIA), and its enhanced variants based on nested subset selection dichotomies, are developed for software requirement risk prediction. The suggested fuzzy induction models are based on the use of effective rule-stretching methods for the prediction process. Additionally, the proposed FURIA method is enhanced through the introduction of nested subset selection dichotomy concepts into its prediction process. The prediction performances of the proposed models are evaluated using a benchmark dataset, and are then compared with existing machine learning (ML)-based and rule-based software risk prediction models. From the experimental results, it was observed that the FURIA performed comparably, in most cases, to the rule-based and ML-based models. However, the FURIA nested dichotomy variants were superior in performance to the conventional FURIA method, and rule-based and ML-based methods, with the least accuracy, area under the curve (AUC), and Mathew’s correlation coefficient (MCC), with values of approximately 98%.

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Section: Methodsmentioning

confidence: 99%

“…Thus, the minimal generalization of a rule is simply obtained by deleting all antecedents that are not satisfied by the query. Further detail on the FURIA is available in [31][32][33]. Figure 1 depicts the flowchart for the working operation of the FURIA.…”

Section: Fuzzy Unordered Rule Induction Algorithm (Furia)mentioning

confidence: 99%