Relative Analysis Prediction for Threat Detection with Random Forest Algorithm Over Knearest Neighbour Algorithms

An important part of security system is threat detection. The system must identify the potential threats accurately and in real time so as to minimize failure risks and improve security. This research aims to refine an established framework model for proficiently detecting intrusion threats. This is achieved using machine learning algorithms like random forest. This study has a direct comparison between two distinct approaches including novel random forest and KNN. The central idea remains to predict fraudulent attacks, and to accomplish this, a sample size of 10 was meticulously chosen for each prediction. The study applied a Gpower value of 80%, utilized a threshold set at 0.05 %, and established a 95% confidence interval to ensure the reliability and robustness of the results. The Novel Random Forest algorithm notably enhances data accuracy, achieving an impressive accuracy rate of 92.59% as compared to 77.49 % for the k_ nearest neighbor method. This performance difference is statistically significant, denoted by a low p-value of 0.002 (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{p}&lt; 0. 0 5$</tex>), underscoring the algorithm's effectiveness in detecting fraudulent attacks. HEnce demonstrated substantial enhancements in the detection of fraudulent Attacks when contrasted with the k _nearest neighbor method, largely attributed to its superior accuracy.