An extended Takagi–Sugeno–Kang inference system (TSK+) with fuzzy interpolation and its rule base generation

Li, Jie; Yang, Longzhi; Qu, Yanpeng; Sexton, Graham

doi:10.1007/s00500-017-2925-8

Cited by 57 publications

(55 citation statements)

References 33 publications

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“…For future improvements, it is important to evaluate the proposed method with a threshold value of the similarity scores to establish the effect of these similarity scores and further enhance the fuzzy hashing algorithm itself, thus increasing the clustering performance of FCM. This proposed fuzzy analysis approach could be automated by generating sparse fuzzy rules based on the best results of FCM [31] and employing an adaptive fuzzy rule interpolation technique [32], [33], [34], [35]. Moreover, this sparse fuzzy rule base can be updated dynamically by employing dynamic fuzzy rule interpolation (D-FRI) method [36], [37], [38], [39], [40], [41], [42].…”

Section: Discussionmentioning

confidence: 99%

Cyberthreat Hunting - Part 2: Tracking Ransomware Threat Actors using Fuzzy Hashing and Fuzzy C-Means Clustering

Naik

Jenkins

Savage

et al. 2019

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Threat actors are constantly seeking new attack surfaces, with ransomeware being one the most successful attack vectors that have been used for financial gain. This has been achieved through the dispersion of unlimited polymorphic samples of ransomware whilst those responsible evade detection and hide their identity. Nonetheless, every ransomware threat actor adopts some similar style or uses some common patterns in their malicious code writing, which can be significant evidence contributing to their identification. The first step in attempting to identify the source of the attack is to cluster a large number of ransomware samples based on very little or no information about the samples, accordingly, their traits and signatures can be analysed and identified. Therefore, this paper proposes an efficient fuzzy analysis approach to cluster ransomware samples based on the combination of two fuzzy techniques fuzzy hashing and fuzzy c-means (FCM) clustering. Unlike other clustering techniques, FCM can directly utilise similarity scores generated by a fuzzy hashing method and cluster them into similar groups without requiring additional transformational steps to obtain distance among objects for clustering. Thus, it reduces the computational overheads by utilising fuzzy similarity scores obtained at the time of initial triaging of whether the sample is known or unknown ransomware. The performance of the proposed fuzzy method is compared against k-means clustering and the two fuzzy hashing methods SSDEEP and SDHASH which are evaluated based on their FCM clustering results to understand how the similarity score affects the clustering results.

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Section: Discussionmentioning

confidence: 99%

Cyberthreat Hunting - Part 2: Tracking Ransomware Threat Actors using Fuzzy Hashing and Fuzzy C-Means Clustering

Naik

Jenkins

Savage

et al. 2019

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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“…x k .weight ← 0 14: for each R j ∈ R do 15: if x k is used by R j then 16: Sort Ascending (X) 21: X = first b features in X 22: end procedure set [41]. However, depending on the real-world application, a TSK-style rule base may also readily be generated using the existing TSK rule base generation approaches [42]. Note that the resultant rule base has only b antecedents, which is a subset of the input features.…”

Section: Rule Base Initialisationmentioning

confidence: 99%

Curvature-based sparse rule base generation for fuzzy rule interpolation

Tan

Shum

Chao

et al. 2019

IFS

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Fuzzy inference systems have been successfully applied to many real-world applications. Traditional fuzzy inference systems are only applicable to problems with dense rule bases covering the entire problem domains, whilst fuzzy rule interpolation (FRI) works with sparse rule bases that do not cover certain inputs. Thanks to its ability to work with a rule base with less number of rules, FRI approaches have been utilised as a means to reduce system complexity for complex fuzzy models. This is implemented by removing the rules that can be approximated by their neighbours. Most of the existing fuzzy rule base generation and simplification approaches only target dense rule bases for traditional fuzzy inference systems. This paper proposes a new sparse fuzzy rule base generation method to support FRI. In particular, this approach uses curvature values to identify important rules that cannot be accurately approximated by their neighbouring ones for initialising a compact rule base. The initialised rule base is then optimised using an optimisation algorithm by fine-tuning the membership functions of the involved fuzzy sets. Experiments with a simulation model and a real-world application demonstrate the working principle and the actual performance of the proposed system, with results comparable to the traditional methods using rule bases with more rules.

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“…Classification: After the process of feature extraction and feature selection, a ready-to-use training data set is utilized and can be applied to the classification algorithm, such as Gaussian Naïve Bayes (GNB) classifier, AdaBoost, the logistic regression (LR) [10] and fuzzy interpolation [11], [12], [13] for system modeling. Note that the performance of classifier can be enhanced by involving an extra step of further normalizing the selected features prior to the classification phase.…”

Section: A Grooming Detectionmentioning

confidence: 99%

Grooming Detection using Fuzzy-Rough Feature Selection and Text Classification

Zuo

Anderson

et al. 2018

2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Online child grooming detection has recently attracted intensive research interests from both the machine learning community and digital forensics community due to its great social impact. The existing data-driven approaches usually face the challenges of lack of training data and the uncertainty of classes in terms of the classification or decision boundary. This paper proposes a grooming detection approach in an effort to address such uncertainty based on a data set derived from a publicly available profiling data set. In particular, the approach firstly applies the conventional text feature extraction approach in identifying the most significant words in the data set. This is followed by the application of a fuzzy-rough feature selection approach in reducing the high dimensions of the selected words for fast processing, which at the same time addressing the uncertainty of class boundaries. The experimental results demonstrate the efficiency and efficacy of the proposed approach in detecting child grooming.

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An extended Takagi–Sugeno–Kang inference system (TSK+) with fuzzy interpolation and its rule base generation

Cited by 57 publications

References 33 publications

Cyberthreat Hunting - Part 2: Tracking Ransomware Threat Actors using Fuzzy Hashing and Fuzzy C-Means Clustering

Cyberthreat Hunting - Part 2: Tracking Ransomware Threat Actors using Fuzzy Hashing and Fuzzy C-Means Clustering

Curvature-based sparse rule base generation for fuzzy rule interpolation

Grooming Detection using Fuzzy-Rough Feature Selection and Text Classification

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