A Comprehensive Review using Machine Learning and Deep Learning Methods for Intelligent Intrusion Detection in the Domain of Cybersecurity

Organizations today depend heavily on cloud computing, IoT devices, and large-scale well-connected systems. This expansion brings serious cybersecurity risks though. Threats like ransomware, zero-day exploits, DDoS attacks, and advanced persistent threats are happening more often and getting more sophisticated. Current signature-based intrusion detection systems (IDS) struggle with these evolving attacks because they depend on predefined rules and known patterns. This limits how well they work against new threats. That's why researchers are exploring machine learning (ML) and deep learning (DL) techniques to build smarter intrusion detection solutions. This paper reviews ML-based, DL-based, and hybrid ML-DL methods for intrusion detection. We examine benchmark datasets including UNSW-NB15 and CIC-IDS2017, looking at their features, benefits, and drawbacks. Class imbalance and high-dimensional feature spaces create problems. The review covers several important models: Random Forest, XGBoost, Convolutional Neural Networks, Long Short-Term Memory networks, and Autoencoders. Hybrid architectures look most promising since they combine deep feature extraction with traditional machine learning classifiers, getting better detection accuracy and fewer false positives. We also identify research gaps, discuss computational challenges, and suggest future directions for building scalable, real-time, and interpretable IDS solutions that meet today's cybersecurity needs.