Basics of Machine Learning and its capabilities in Cybersecurity

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As cyber threats become more complex, Machine Learning (ML) is crucial in modern cybersecurity. ML, a subset of AI, enables computers to learn from data and make predictions without explicit programming. Deep Learning, within ML, excels at tasks with unstructured data. ML involves supervised, unsupervised, and reinforcement learning. The iterative ML process includes problem definition, data gathering, exploration, preprocessing, model creation, evaluation, and deployment. Feature engineering transforms data for ML algorithms by creating relevant numerical attributes. Decision Trees and Ensemble Techniques like Random Forests and Gradient Boosting enhance accuracy. ML is applied in cybersecurity for malware and phishing detection, anomaly identification, and clustering data. Clustering algorithms group similar data for better processing, while ML aids in decision support, though data quality is key. Embracing ML is vital for robust digital defenses against evolving cyber threats.