Review: machine learning techniques applied to cybersecurity

This article explores the application of machine learning in cybersecurity, focusing on improving model accuracy and efficiency against evolving threats like malware. It examines three key aspects of machine learning models: structure, learning process, and complexity. The authors emphasize the need for adaptable cybersecurity systems due to the self-updating nature of malware, highlighting the potential of machine learning to address this challenge by reducing computation time and enhancing detection capabilities. The article further includes various machine learning models and their applications in cybersecurity, showing their effectiveness in detecting unknown threats like spam, malware, and phishing attempts. It is also encouraging readers who are interested in this field to delve deeper into these techniques.