Abstract: A lifelong learning intrusion detection system and methods are provided. The system may capture network data directed to a host node. The host node may include a honeypot. The honeypot may emulate operation of a physical or virtual device to attract malicious activity. The system may classify, based on a supervised machine learning model, the network data as being not malicious or not malicious. The system may classify, based on an unsupervised machine learning model, the network data as being anomalous or not anomalous. The system may alter operation of the honeypot to induce malicious activity. The system may determine, after operation of the honeypot is altered, the honeypot is accessed. The system may retrain the supervised machine learning model and/or unsupervised machine learning model based the network data.