A proposed hybrid framework for improving supervised classifiers detection aecuraev over intrusion trace

The presence of intrusion attack traces in network traffic pattern seems to be major threatening to cyber community. During a decade, many preventive and detection measures have had been developed to overcome these illicit activities but the evolution of zero-day exploits which has common behavior as intrusion traces find difficult to resolve the critics presence in network traffic patterns. The other critics faced by preventive and detection measures are majority of intrusion traces resembles as normal behavior in network traffic pattern analysis. Contemporary preventive or detective measures have been evolved either as neither one-hand approach nor hybrid approaches. Objective of this paper is to elaborate discuss the detection and preventive measures evolved still and their flaws incurred in their approaches. Also, suggesting the new meta-heuristics algorithm called as 'Grey Wolf Optimizer approach and its working attitude towards the fittest solution resolved from critics. Also, discuss the effectiveness of utilizing this new meta-heuristics approach in designing the efficient intrusion detection model by extensively compare with other methodologies with an intent to find prominent solution which will be effectively used by cyber Researchers in future.