Intrus-ML: An Intrusion Detection Model Based on Machine Learning

In recent years, machine learning (ML) has played an important role in attaining security and privacy in various applications in recent years. It is being utilized to handle severe concerns like data security, threat detection, sensitivity assessments, and a variety of others. ML significantly meets the present scenario's demanding security and privacy needs in various areas such as cycle time reduction, reduced cost, real-time decisiveness, large data management, cycle time reduction, cost reduction, etc. In this research, we propose a machine learning-based security model for intrusion detection, called Intrus-ML. The proposed method builds a generic intrusion detection model utilising just the specified key characteristics and prioritises the ranking of security aspects based on their importance. After that, experiments were conducted using cybersecurity datasets to evaluate the efficacy of our strategy. We compare the final results of Intrus-ML with a variety of commonly used traditional machine learning approaches in order to evaluate the effectiveness of the security model that results.