An Efficient Cluster Based Multi-Label Classification Model for Advanced Persistent Threat Attacks Detecting

In response to escalating cyber threats, there is an urgent need for adaptive detection mechanisms.This study introduces a cyber threat detection framework employing ensemble learning and a hybrid feature ranking approach.Designed to address diverse and evolving threats, the framework aims to enhance detection accuracy in dynamic environments.The framework comprises three key components.Firstly, an ensemble feature ranking algorithm identifies influential features in imbalanced datasets, ensuring effective threat detection while mitigating imbalanced class impact.Secondly, a hybrid feature ranking measure (HFRM) integrates fusion entropy to assess feature importance comprehensively.HFRM combines information gain, entropy, and proposed fusion entropy for a holistic ranking.Thirdly, the framework includes a multi-class k-means rank-based classification for efficient clustering and threat categorization.Evaluation using diverse datasets underscores the framework's effectiveness in achieving high detection accuracy and robustness across threat scenarios.The ensemble approach, hybrid feature ranking, and rank-based classification collectively provide an adaptive solution for cyber threat detection.In conclusion, this research introduces an innovative framework integrating ensemble learning, hybrid feature ranking, and k-means clustering, promising more resilient cybersecurity in the face of sophisticated threats.