Machine Learning for Cybersecurity Threat Detection
Authors-Rizky Pratama
Keyword-Machine Learning, Cybersecurity, Threat Detection, Intrusion Detection Systems (IDS), Anomaly Detection, Malware Analysis, Network Security, Deep Learning, Big Data Analytics, Adversarial Attacks, Data Imbalance, Security Analytics, Artificial Intelligence, Real-Time Detection, Cyber Defense
The increasing sophistication and frequency of cyber threats have made traditional security mechanisms insufficient for protecting modern digital infrastructures. Machine learning (ML) has emerged as a powerful approach for enhancing cybersecurity by enabling systems to automatically detect, analyze, and respond to potential threats in real time. This study provides a comprehensive analysis of the application of machine learning techniques in cybersecurity threat detection, focusing on anomaly detection, intrusion detection systems (IDS), malware classification, and network traffic analysis. It explores various ML models, including supervised, unsupervised, and deep learning approaches, and their effectiveness in identifying known and unknown attack patterns. The integration of ML with big data analytics and cloud-based security platforms is also examined, highlighting the ability to process large volumes of data and improve detection accuracy. Additionally, the study addresses key challenges such as data imbalance, false positives, adversarial attacks, and model interpretability, along with potential solutions to mitigate these issues. The findings demonstrate that machine learning significantly enhances the capability of cybersecurity systems to detect and prevent evolving threats, making it a critical component of modern security frameworks.