IDS (Intrusion Detection Systems) are critical for protecting networks of computers from hostile activities. The necessity for reliable intrusion detection solutions has increased ascyberthreats become more sophisticated. Because of their ability to learn patterns from big datasets, machine learningal gorith msappearto be potential methods for improving IDS detection capabilities. Several machine-learning methods for intrusion detection, including supervised, unsupervised, and semi-supervised strategies, are thoroughly examined in this work. The study compares the performance of algorithms including “Decision Trees, Random Forests, Support Vector Machines, K-Nearest Neighbours and NaiveBayes.” Algo-rithm efficacy is evaluated by assessment criteria like recall, precision, accuracy, & F1-score, etc. Furthermore, thestudy investigates the strengths, limits, and application of various algorithms in diverse network traffic and attack scenarios. The findings of this investigation provideessential help in determining the best machine-learning technique for developing resilient and efficient Intrusion Detection Systems adapted to varied network landscapes.

IDS (Intrusion Detection System), Machine Learning, Supervised Machine Learning Algorithms