ANCS: Automatic NXDomain Classification System Based on Incremental Fuzzy Rough Sets Machine Learning

Fang, Liming; Yun, Xinyu; Yin, Changchun; Ding, Weiping; Zhou, Lülin; Liu, Zhe; Su, Chunhua

doi:10.1109/tfuzz.2020.2965872

Cited by 9 publications

(2 citation statements)

References 25 publications

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“…Yet deep learning-based methods suffer from several problems including (i) the complexity of the model structure prevents interpretability and explainability, (ii) the features extracted (especially visual feature) consume too much memory and their validity remains vague, (iii) the model can be attacked by gradient based adversarial methods as in image classification tasks [10]- [12]. Recently, fuzzy theory has been incorporated into deep learning system to provide interpretability and robustness [13]- [16]. A large family of fuzzy decision-makers such as fuzzy clustering [17], [18], fuzzy support vector machine [19] and fuzzy decision tree [5], [20], have been proved to be effective in a range of data analysis challenges.…”

Section: Introductionmentioning

confidence: 99%

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Large-Scale Malicious Software Classification With Fuzzified Features and Boosted Fuzzy Random Forest

Wang

Liew

et al. 2021

IEEE Trans. Fuzzy Syst.

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Classification of malicious software, especially in a very large dataset, is a challenging task for machine intelligence. Malware can have highly diversified features, each of which has highly heterogeneous distributions. These factors increase the difficulties for traditional data analytic approaches to deal with them. Although deep learning-based methods have reported good classification performance, the deep models usually lack interpretability and are fragile under adversarial attacks. To solve these problems, fuzzy systems have become a competitive candidate in malware analysis. In this paper, a new fuzzy-based approach is proposed for malware classification. We focused on portable executable files in the Windows platform and analyzed the distributions of static features and content-oriented features. Fuzzification was used to reduce the ubiquitous impact of noise and outliers in a very large dataset. Finally, a novel boosted classifier consisted of fuzzy decision trees and support vector machine is proposed to perform the malware classification. By using fuzzy decision trees, the inner structure of the classifier can be readily interpreted as discriminative rules, while the novel boosting strategy provides state-of-the-art classification performance. Extensive experimental results showed that our method significantly outperformed several state-of-the-art classifiers.

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Section: Introductionmentioning

confidence: 99%

“…In an ordinary decision tree, a node is passed to the leaf through one and only one path. However, in a fuzzy decision tree, a node may go through multiple paths from the definition in (15) and (16). The weight of one path…”

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confidence: 99%