A Comparative Study of Deep Learning and Machine Learning Methods for Next- Generation Intrusion Detection

Abstract

A crucial component of cybersecurity is intrusion detection, which assists in spotting malicious activity in network traffic. Using the NSL-KDD dataset, this study compares machine learning and deep learning-based intrusion detection techniques. The efficiency of Random Forest (RF), XGBoost (The XGB), and CNN (Convolutional Neural Networks) is compared in this study in order to determine which models are most effective at detecting cyberthreats.The experimental results indicate that RF and XGB are accurate and computationally light-weight, which makes them relevant for online intrusion detection systems. Nonetheless, the CNN model shows great potential in discovering complex patterns in network traffic, even if it needs more computing resources. The comparative study helps shed light on the trade-offs of interpretability, computation cost and detection accuracy.This work will be valuable for both academic as well as cybersecurity researchers to make the correct model selection based on their network security requirements. Continued research could look into hybrid variants that incorporate elements from both approaches to improve the advantages for intrusion detection.