Intrusion detection systems (IDS) are essential for protecting network infrastructures from cyber threats. This paper provides an in-depth analysis of authorship networks in IDS research over the last decade, employing bibliometric techniques to visualize collaborations and pinpoint influential researchers. By analyzing data from a comprehensive papers database, we uncover collaborative clusters within the field, identify key research groups and their contributions. Our study highlights significant partnerships and emerging research groups, offering valuable insights into the development of IDS research. The research reveals deep learning techniques as the dominant approach in intrusion detection systems, with significant focus on IoT security applications including smart grids and UAV networks. Advanced datasets and feature selection methods address class imbalance challenges in big data environments, while hybrid ensemble approaches combining multiple methodologies show promise for detecting evolving cyber threats across diverse network architectures. These findings serve as a crucial resource for understanding the current landscape and guiding future research directions in intrusion detection.