Enhancing Cybersecurity With Intelligent AI and Machine Learning Using Automl Techniques

Automated Machine Learning, also known as AutoML, is a collection of methods and procedures designed to make machine learning understandable to non-experts. For specific datasets, autoML can recommend the best models or show optimal improvement of an existing model. The goal of the new discipline of automated machine learning, or autoML, is to automate the process of creating machine learning models. When machine learning is used, autoML was developed to boost efficiency and production by automating as much of the repetitive, unproductive work as feasible. Research has long been done on technologies that can efficiently create high-quality models with the least amount of model creators' involvement in the process, from selecting and fine-tuning algorithms to preparing data. The data processing requirements for AutoML techniques are compiled and explained in detail in this semantic review investigation. Through the internet, the latter can safely make the local Jenkins address available to the public. As a result, the suggested pipeline is a hybrid software design that incorporates elements from the machine learning operations (MLOps) theme. Additionally, it looks into security intelligence modeling as a way to provide useful information for enhancing organizational resilience. This paper also aims to give readers a comprehensive grasp of how artificial intelligence is changing cybersecurity and to suggest future directions for this ever-evolving field's research and development.