Evaluating Contemporary Machine Learning and Deep Learning Strategies for Intrusion Detection

As cyber threats evolve with increasing complexity, the imperative for advanced Network Intrusion Detection Systems (NIDS) becomes paramount. This study offers a rigorous comparative analysis of traditional Machine Learning (ML) algorithms and cutting-edge Deep Learning (DL) techniques within the framework of the contemporary CICIDS-2018 dataset, aimed at evaluating their proficiency in network intrusion detection. The machine learning classifiers used are K-Nearest Neighbors, Logistic Regression, Random Forest, and Support Vector Machines, against a hybrid DL model combining Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). The study reveals that while ML algorithms maintain the benefit of interpretability and lower computational demands, the hybrid CNN-GRU model showcases exceptional capability in discerning intricate and latent patterns indicative of intrusive activities, hence yielding superior detection accuracy and demonstrating resilience against overfitting. The complex dynamics of network security require adaptive model selection, and this comparison research shows the efficacy of merging ML and DL approaches. The results affirm the viability of deploying a hybrid analytical framework that capitalizes on the strengths of both ML and DL, leveraging the comprehensive and diverse nature of the CICIDS-2018 dataset to bolster the security posture of digital infrastructures against the burgeoning wave of cyber threats.