Cyber security Intrusion Detection Systems using Machine Learning Applications

The increasing complexity of cyber threats have made it more challenging to detect them accurately using the traditional Intrusion Detection Systems (IDS)..Machine Learning (ML)- based IDS have gained prominence due to their ability to analyze vast amounts of network traffic, detect anomalies, and classify cyber threats with high accuracy. However, challenges such as data imbalance, high-dimensional feature spaces, and false positive rates remain. This paper presents the complete analysis of ML techniques for IDS, in particular, supervised, unsupervised, and hybrid approaches. Feature selection and dimensionality reduction methods, such as Principal Component Analysis (PCA) and clustering-based Stacking Feature Embedding, are explored to enhance model efficiency. The study evaluates various ML algorithms, including Decision Trees (DT), Random Forest (RF), and Extreme Trees (ET), using benchmark datasets such as UNSW-NB15, CIC-IDS-2017, and CIC-IDS-2018. The experimental results show that the deep learning models and ensemble techniques can achieve up to 99.99% accuracy, which is a big improvement over traditional IDS methods. Additionally, the study discusses key challenges, including adversarial attacks, scalability concerns, and interpretability issues. It suggests future research directions, such as Explainable AI (XAI), federated learning, and blockchain-based IDS solutions. The findings underscore the potential of ML-driven IDS in enhancing cybersecurity resilience and mitigating emerging cyber threats.