Enhancing Classification Accuracy using Feature Subset Selection in Intrusion Detection System (IDS) - Margaret, SA; Padmavathi, S (International Journal of Computer Sciences and Engineering, 2025)
Intrusion detection system (IDS) look into field has developed immensely in the previous decade. Enhancing the detection rate of client to root (C2R) assault class is an open research issue. Current IDS utilizes all information elements to recognize intrusions. A portion of the elements might be excess to the detection procedure. The reason for this experimental examination is to distinguish the vital elements to enhance the detection rate and diminish the false detection rate. The researched highlight subset choice strategies enhance the general exactness, detection rate of C2R assault class and furthermore diminish the computational cost. The exact outcomes have demonstrated a recognizable change in detection rate of C2R assault class with include subset determination methods.