ENHANCING INTRUSION DETECTION SYSTEMS WITH AI-BASED NETWORK TRAFFIC ANALYSIS: A FOCUS ON ANOMALY DETECTION

Abstract

ABSTRACT. In the age of advanced cyber threats and increasing network complexity, Intrusion Detection Systems (IDS) play a critical role in safeguarding digital infrastructures. Traditional IDS methods, relying on predefined rules and signatures, are increasingly ineffective against sophisticated attacks. To address this gap, machine learning (ML) techniques, such as XGBoost (Extreme Gradient Boosting), have emerged as powerful tools for enhancing intrusion detection capabilities. This paper explores the application of XGBoost in IDS for the detection of both known and unknown network intrusions. By utilizing an ensemble learning approach, XGBoost leverages decision trees to improve accuracy and efficiency, offering a robust solution for real-time threat detection in dynamic network environments. The study delves into the data collection, preprocessing, model training, and evaluation phases of the XGBoost-based IDS. Additionally, it addresses challenges such as model interpretability, false positives, and the need for continuous learning. The research demonstrates the significant potential of XGBoost in enhancing network security and providing proactive threat detection.

Keywords: intrusion detection system (ids), machine learning, xgboost, network security, real-time detection, anomaly detection, cybersecurity, feature engineering, gradient boosting, false positives, model evaluation, data preprocessing.