Network Attack Prediction Using Machine Learning Techniques

Creating data for an Intrusion Detection System (IDS) typically requires setting up a real working environment to simulate and capture various types of attacks. However, this approach can be costly and resource-intensive. IDS software is designed to monitor network activity and detect unauthorized access attempts, whether they come from external attackers or insiders within the network. By analyzing patterns and identifying suspicious behavior, IDS helps protect computer networks from potential threats Random Forest is employed for its simplicity and efficiency in handling probabilistic data making it suitable for quick threat detection. Meanwhile, the XGBOOST algorithm is employed to enhance the accuracy of the prediction by learning complex patterns and interactions between features. By combining these two algorithms, the system can classify network activities and predict potential attacks with high accuracy and low false positive rates. Experimental results on publicly available datasets show that the proposed hybrid model datasets, demonstrate that the hybrid model significantly improves prediction accuracy compared to using each algorithm independently. This approach offers a scalable and efficient solution for real-time network attack prediction, helping organizations strengthen their cybersecurity defenses by identifying and mitigating threats before they cause damage.