Real-Time Detection
Deepika Malve, H. Meenal, C. Kishor Kumar Reddy, Kari J. Lippert · 2025
The integration of machine learning in cybersecurity, emphasizing its capability to address dynamic and sophisticated cyber threats. It highlights the importance of real-time detection, delves into machine learning algorithms such as supervised, unsupervised, and reinforcement learning, and examines the role of feature engineering and diverse data sources like network logs and endpoint data. Various cyber threats, including APTs, malware, and phishing, are analyzed, along with a comparison of detection methods like anomaly detection and behavior-based approaches. Challenges such as false positives, adversarial attacks, and large-scale data handling are discussed, supported by real-world case studies of successful implementations. The discussion concludes with a look at emerging trends like federated learning, explainable AI, and quantum computing, which hold promise for enhancing future threat detection and cybersecurity defenses.