GuardianAI: Smart Intrusion Detection for Modern Threats

Abstract

Finding and stopping breaches is now essential in the rapidly evolving world of cybersecurity. This work involves a range of machine learning algorithms for intrusion detection systems (IDS) as well as a detailed examination and use of sophisticated feature engineering techniques. The study enhances intrusion detection's accuracy and efficacy to counteract the ever-changing nature of cyber threats. We handle missing values, encode categorical characteristics, and prepare and balance datasets as part of our strategy. By examining them, one can have a thorough understanding of the benefits and drawbacks of several machine learning models, including Decision Trees, Random Forests, Naive Bayes, and XGBoost, in the context of intrusion detection. Furthermore, we provide a novel approach that combines Recursive Feature Elimination (RFE) with Random Forests to show how well it maintains high classification accuracy while reducing dimensionality. We test the models on a publically available dataset, and we meticulously evaluate the models based on key performance metrics like accuracy, precision, recall, and training time. A comparison analysis of the models makes it clear if they are suitable for real-world application. Our findings indicate that the XGBoost classifier outperforms other models when paired with a well-selected feature set. This demonstrates how well the model detects common intrusions as well as backdoors. In addition, the Random Forest Classifier with RFE modifications exhibits potential as a feature selection technique, offering a balance between accuracy and computational economy. The work adds to the body of knowledge on intrusion detection and provides cybersecurity experts and system developers with valuable information. The strategy being offered offers a reliable and adaptable means of fending off the always changing cyberthreats, thereby bolstering the ongoing effort to fortify digital systems against malicious activity. This work highlights the importance of combining state-of-the-art feature engineering approaches with intricate machine learning algorithms to produce robust and effective cybersecurity defenses, laying a solid foundation for future advancements in intrusion detection.