Development of Optimized Machine Learning Oriented Models
Ratnesh Kumar Dubey, Dilip Kumar Choubey, Shubha Mishra · 2025
Computer assaults are becoming more frequent, which makes it difficult for network administrators to defend the computer from them. Although there are several conventional intrusion detection systems (IDS) in place, they are not able to fully protect computer systems. Since more individuals are connecting to networks more often and utilising them to store or access vital information, there is a greater need than ever for network security. In this research, we evaluate and analyse different machine learning algorithms, and then we suggest a system that is built around the algorithm that performs the best. Here, we presented the XG Boost learning approach, which improvises on the model's stability and predictive capacity by merging a varied group of learners (individual models) together. Significant progress has been made in the subject of machine learning in recent years, which has resulted in the creation of several algorithms and methods for handling challenging issues. But there is still a significant obstacle in optimising these models for particular uses. The construction of optimised machine learning-oriented models has been the subject of current research, which is thoroughly reviewed in this work. This paper discusses many facets of model optimisation, such as discovering new algorithms, refining interpretability and robustness of models, creating explainable artificial intelligence (XAI), advancing deep learning and reinforcement learning, developing federated learning, pushing transfer learning, and boosting model privacy. Along with offering suggestions for further study in this field, the report also identifies some of the difficulties and restrictions connected to these methodologies. In summary, the goal of this work is to present a thorough review of the state of the art in the creation of optimised machine learning-oriented models and to suggest future research avenues that show promise.