Intrusion Detection System is one of the worthwhile areas for researchers for a long. Numbers of researchers have worked for increasing the efficiency of Intrusion Detection Systems. But still, many challenges are present in modern Intrusion Detection Systems. One of the major challenges is controlling the false positive rate. In this paper, we have presented an efficient soft computing framework for the classification of intrusion detection dataset to diminish a false positive rate. The proposed processing steps are described as; the input data is at first pre-processed by the normalization process. Afterward, optimal features are chosen for the dimensionality decrease utilizing krill herd optimization. Here, the effective feature assortment is utilized to enhance classification accuracy. Support value is then estimated from ideally chosen features and lastly, a support value-based graph is created for the powerful classification of data into intrusion or normal. The exploratory outcomes demonstrate that the presented technique outperforms the existing techniques regarding different performance examinations like execution time, accuracy, false-positive rate, and their intrusion detection model increases the detection rate and decreases the false rate.