Intrusion detection has achievement a comprehensive interest and turns into a substantial scope for various researches, and still being the subject of common concern by researchers. The intrusion detection group still faces facing complicated problems even after many years of research. Decreasing the huge number of false warnings throughout the process of detecting anonymous attack patterns residue a vague problem. Though, numerous research results lately have exposed that there are possible solutions to this problem. Anomaly detection is the main issue of intrusion detection in which alarms of normal ac-tions show a presence of destined or unintentional prompted attacks, faults, de-fects, and others. These papers represent an attentive literature review of ma-chine learning approaches for intrusion detection, specifically Random Forest and Support Vector Machines that are used. Founded on the reference number and the significance of an evolving method, papers demonstrating every method were specified, read, and shortened