Machine Learning Approaches for Detecting and Mitigating Cybersecurity Threats.

Abstract

Machine learning is super important in cybersecurity. The main goal of using machine learning in cybersecurity is to improve how we detect malware. This makes the process doable, scalable, and way more effective than old-school methods that need people to step in. Dealing with machine learning challenges in cybersecurity needs some serious handling. Different methods like deep learning, support vector machines, and Bayesian classification have shown they work well in stopping cyber-attacks. It's crucial to find hidden insights from network data and use a data-driven machine learning model to stop these attacks before they happen. No research has been done to understand how vulnerable ML techniques are against security threats and how they can be defended against. We need researchers, scientists, and engineers to start focusing on cybersecurity with ML. This survey looks at the machine learning techniques used on cybersecurity data to secure systems. However, ML can be attacked during training and testing, causing problems with security. We also talk about current cybersecurity threats and how machine-learning techniques help fight them off. We point out the flaws in these fancy models and how attack tactics have changed over the years. We aim to see if these machine-learning techniques work against the growing problem of malware causing trouble online.