Soft computing methods in cybersecurity
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In this paper, the aim is to show how computational intelligence methods (fuzzy logic, neural
networks, genetic algorithms) can be used for detecting and preventing the most common attacks
(Evasion, Data Poisoning, DoS and DDoS) to test its performance and weaknesses against
adopted malware and to identify advanced persistent threats early on its life cycle using
prescriptive analytics. This research also propose some soft computing based general solutions
to minimize the alert fatigue caused by generating too many false alerts by getting insights from
security information and event management (SIEM) signals to make the prevention systems learn
from the environment to keep false signals to a minimum In this paper, the aim is to show how computational intelligence methods (fuzzy logic, neural
networks, genetic algorithms) can be used for detecting and preventing the most common attacks
(Evasion, Data Poisoning, DoS and DDoS) to test its performance and weaknesses against
adopted malware and to identify advanced persistent threats early on its life cycle using
prescriptive analytics. This research also propose some soft computing based general solutions
to minimize the alert fatigue caused by generating too many false alerts by getting insights from
security information and event management (SIEM) signals to make the prevention systems learn
from the environment to keep false signals to a minimum