Enhanced Hybrid Model of Support Vector-Grey Wolf Optimizer Technique to Improve the Classifierâ€™s Detection Accuracy in Designing the Efficient Intrusion Detection Model
Keywords:Grey Wolf Optimizer, Support Vector Machine, Enhanced hybrid approach, Detection accuracy.
Research over designing the intrusion detection systems happening during a decade in tremendous way. The specified reason is to study the intrusion presence over network traffic. Based on this, analysis and classification of traffic pattern refined, to projects the importance of enhanced detection approach. Herewith, the methodologies deployed to better understand of â€˜abnormalâ€™ traces and paved the way for descriptive analysis. From the literature review of study, clarifies that hybrid computation approach entitled as fine-tuned work in analyzing intrusion trace illustrative when compared to single approach. The hazard of hybrid methodology is to be high resource computation which leads to step down in its absolute approach. The proposed research has framed to improve the support vector machineâ€™s classifier approach in designing the efficient detection model. This happens by additive support of grey wolf optimizer algorithm with the aim to improve the classifierâ€™s detection accuracy in exact classification of â€˜normalâ€™ and â€˜abnormalâ€™ instance traces from the modified KDDCUP99 intrusion dataset in minimal learning time. Experimentation of work outperform using WEKA simulator tool in WINDOWS operating system background.
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