Critical incidents targeting National Critical Infrastructures are happening more and more often. Attacks, that happens to be both more sophisticated and persistent, can even replicate life. As per CERT-In’s data, the number of cyber security incidents reported in the years: 2014-16 are more than 45000 and in 2017 (till June) are approx 27,482. Wannacry, Erebus & Petya are some big cyber-attacks, which crippled more than 10,000 organizations and 200,000 individuals in over 100 countries. From the above data, it’s notable that the number of cyber security incidents has been growing steadily in India. The goal of this examination is to survey the relative performance of some notable hybrid classification techniques. We used KDD CUP 99 data to play out a controlled experiment in which the data characteristics are efficiently changed to present defects, for example, nonlinearity, multi-co-linearity, unequal covariance, and so forth. Our analyses recommend that datasets attributes significantly impact the classification execution of the strategies. Here we created and analyzed the diverse hybrid strategies in soft computing such as GWO-EBG, GWO-KNN, GWO-SVM and GWO-GRNN. The results of the diverse hybrid strategies can help in the structure of classification frameworks in which several classification techniques can be utilized to expand the reliability and consistency of the classification.

Intrusion detection systems (IDS), SVM, Gray wolf optimizer (GWO), Entropy Based Graph, KNN etc

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