Optimization of Intrusion Detection System with Machine Learning for Detecting Distributed Attacks on Server

This study develops an intrusion detection system optimized with machine learning techniques for efficient and effective detection of Distributed Denial-of-Service (DDoS) attacks. Using the Decision Tree algorithm, the system is designed to maximise accuracy in the identification and classification of DDoS attacks. The CIC-DDoS2019 dataset, which consists of various comprehensive simulated attack scenarios, is used as the basis for training and validation, providing the model with robust capabilities in recognizing DDoS attacks with high accuracy. This IDS successfully achieved a 100% detection rate, which is a significant result in the network security environment. The system is integrated into the existing network infrastructure, monitoring data flows in real-time and performing predictive analysis to detect early indications of attacks. Each attack detection immediately triggers a notification sent via a Telegram bot, ensuring that the security team can react quickly to isolate and address the attack. These notifications include details such as the source, type of attack, detection time, and involved protocol information, enabling more informed and strategic response actions. The use of Telegram bots for real-time communication not only enhances the speed of response to threats but also supports system scalability by facilitating adjustments and integration across various operational scenarios. The system's quick detection and response is a big step forward for machine learning-based intrusion detection systems (IDS). It provides opportunities for further research and practical applications that can adapt to various digital security scenarios.