Recent Granular Computing Implementations and its Feasibility in Cybersecurity Domain
Marek Pawlicki, Michał Choraś, Rafał Kozik · 2018
As the importance of data stored in daily-use information system grows, so does the damage a malicious user could inflict. Network traffic can be notoriously complicated and prone to fluctuations. With the prevailing risk of cybersecurity breaches, improving the detection algorithms is of utmost importance. Advanced systems using various facets of artificial intelligence and machine learning exist. We look forward to Granular Computing (GrC) as a novel, promising way to improve network traffic classification, intrusion detection and reduction in the computational cost of real time traffic analysis. In this paper, aquick primer on granular computing is offered, its properties of abstracting data into meaningful, compact packages named granules are looked into. The basic principles of granule creation are explained. Consecutively, a survey of the most recent Granular Computing implementations is presented, with analysis of how certain aspects of Granular Computing are utilized to solve particular real-world problems. In multiple cases, the techniques of GrC allow for an increase in efficiency and calculating speed, better data legibility and improvements in the performance of classifier algorithms the granulated data is supplied to. The examined approaches are then taxonomised with regard to the purpose of granulation, and with regard to the utilized aspect of Granular Computing.