Real-Time Threat Detection in Cybersecurity

Abstract

The rapid evolution of cyber threats necessitates advanced real-time detection systems capable of identifying anomalies as they arise. This study explores the effectiveness of machine learning algorithms in enhancing real-time anomaly detection in cybersecurity. Using datasets such as CICIDS2017, UNSW-NB15, and simulated data, various models including Random Forest, SVM, and LSTM were trained and tested. The results highlight that LSTM networks exhibit superior performance in accuracy, precision, and recall, particularly in scenarios requiring temporal data analysis. However, the study also identifies challenges related to latency and computational demands, emphasizing the need for scalable and efficient solutions. The importance of feature selection and hyperparameter tuning in optimizing model effectiveness is also discussed. Comparative analysis and real-world testing validate the potential of machine learning to significantly improve the speed and accuracy of threat detection in cybersecurity, offering valuable contributions to the field.

Topics & Concepts