Paper Title
ROLE OF MACHINE LEARNING IN DETECTING CYBERSECURITY THREATS

Abstract
The increasing degree of complexity of cyber threats has rendered signature-based intrusion detection systems increasingly insufficient in contemporary digital protection systems. The present paper also focuses on the identification of cybersecurity threats based on machine learning through the systematic empirical consideration of six models, specifically Random Forest, Support Vector Machine, XGBoost, Long Short-Term Memory, Convolutional Neural Network, and Autoencoder using three benchmark datasets, i.e., NSL-KDD, UNSW-NB15, and CICIDS 2017. Prior to the model training of a system, a system of preprocessing by use of a structured preprocessing pipeline that incorporated balancing of the classes by use of SMOTE, feature selection, and normalization was applied. The ratios of the performance were brought to accuracy, precision, recall, F1-score, false positive value, and AUC-ROC. Based on the results of experimental results, the LSTM network recorded the highest detection accuracy at 99.1 percent, an F1-score of 98.7 percent, and the lowest false positive at 0.9 percent, which demonstrates that deep learning architectures are superior in sequential pattern of threats recognition. The HSBoost offered the best accuracy-enviabilitytradeoff of traditional classifiers. To enable the model interpretability to become functional, the explainability analysis with the help of SHAP was also added. The findings affirm that machine learning, in particular deep learning, is a groundbreaking paradigm in the framework of proactive cyber defense and single out adversarial robustness, real-time deployment, and federated learning as crucial foci in future research. Keywords - Machine Learning, cybersecurity, intrusion detection, deep learning, LSTM, anomaly detection, XGBoost, SHAP, adversarial robustness, network security.