Advancing Intrusion Detection Systems Innovative Approaches for Prevention and Response

A group intruder detection system built on K-Nearest Neighbors, Decision Trees, Neural Networks, Support Vector Machines, and Random Forests is shown in this study. A thorough study on ablation shows how the algorithms work together to make breach detection better. We compare how well the suggested method works in terms of memory, F1 score, false negative rate, accuracy, and detection time. Bar charts, line graphs, pie charts, stacked bar graphs, area plots, and scatter plots all show that the new way is better than the old ones. Tables are also used to organize data. An ensemble takes the best features of Random Forests, Support Vector Machines, Neural Networks, Decision Trees, and K-Nearest Neighbors and puts them all together in a single package. Broad powers make the suggested method better at solving cybersecurity problems across a range of review factors. The ablation study shows why each method is important and how they work together to make an adaptive intruder detection system. In a world where safety is always changing, the proposed answer is scalable, flexible, easy to set up, and cheap.