Malicious Code Family Classification Based on Multi-feature Fusion Fractional Kalman Filter

Wang, Junling; Cuifang, Guo; Wang, Zhendong; Yan, Cai

doi:10.1109/icaibd49809.2020.9137472

Cited by 2 publications

(1 citation statement)

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“…A FKF solution is proposed for the problem of identifying malicious code and then, categorizing it according to its type (viruses, Trojans, spyware, etc.) [ 115 ]. A two-dimensional model commonly used in imagery is first used and adapted to a fractional-order state-space system representation.…”

Section: Applications Of Fractional-order Filtersmentioning

confidence: 99%

A Review of Recent Advances in Fractional-Order Sensing and Filtering Techniques

Muresan

Birs

Dulf

et al. 2021

Sensors

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The present manuscript aims at raising awareness of the endless possibilities of fractional calculus applied not only to system identification and control engineering, but also into sensing and filtering domains. The creation of the fractance device has enabled the physical realization of a new array of sensors capable of gathering more information. The same fractional-order electronic component has led to the possibility of exploring analog filtering techniques from a practical perspective, enlarging the horizon to a wider frequency range, with increased robustness to component variation, stability and noise reduction. Furthermore, fractional-order digital filters have developed to provide an alternative solution to higher-order integer-order filters, with increased design flexibility and better performance. The present study is a comprehensive review of the latest advances in fractional-order sensors and filters, with a focus on design methodologies and their real-life applicability reported in the last decade. The potential enhancements brought by the use of fractional calculus have been exploited as well in sensing and filtering techniques. Several extensions of the classical sensing and filtering methods have been proposed to date. The basics of fractional-order filters are reviewed, with a focus on the popular fractional-order Kalman filter, as well as those related to sensing. A detailed presentation of fractional-order filters is included in applications such as data transmission and networking, electrical and chemical engineering, biomedicine and various industrial fields.

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Section: Applications Of Fractional-order Filtersmentioning

confidence: 99%