Nowadays, cyber-attacks have become a common and persistent issue affecting various human activities in modern societies. Due to the continuously evolving landscape of cyber-attacks and the growing concerns around “black box” models, there has been a strong demand for novel explainable and interpretable intrusion detection systems with online learning abilities. In this paper, a novel soft prototype-based autonomous fuzzy inference system (SPAFIS) is proposed for network intrusion detection. SPAFIS learns from network traffic data streams online on a chunk-by-chunk basis and autonomously identifies a set of meaningful, human-interpretable soft prototypes to build an IF-THEN fuzzy rule base for classification. Thanks to the utilization of soft prototypes, SPAFIS can precisely capture the underlying data structure and local patterns, and perform internal reasoning and decision-making in a human-interpretable manner based on the ensemble properties and mutual distances of data. To maintain a healthy and compact knowledge base, a pruning scheme is further introduced to SPAFIS, allowing itself to periodically examine the learned solution and remove redundant soft prototypes from its knowledge base. Numerical examples on public network intrusion detection datasets demonstrated the efficacy of the proposed SPAFIS in both offline and online application scenarios, outperforming the state-of-the-art alternatives.