Artificial Intelligence (AI) Driven Threat Detection and Mitigation using Machine Learning Techniques

In today's flood of online dangers existing safety measures often can't keep up with the need for quick and effective protection. This research tackles these issues by setting up an advanced system to spot threats using machine learning. This work used ML methods such as Random Forest (RF), Decision Trees (DT), Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN). Obtained results shows that the Random Forest Model outperforms in terms of best accuracy and strength. The team used the CIC-IDS2017 dataset, which has many types of attacks to test. Important measures like accuracy, precision, recall, and F1-score show how well RF cuts down on false alarms and adjusts to new attack styles. By using AI to spot and react to threats, this approach makes cybersecurity stronger offering a flexible and forward-thinking way to safe guard against changing threats.