Cyber-attacks are becoming increasingly sophisticated, prompting more interest in Anomaly-based Intrusion Detection Systems for their ability to detect zero-day threats. As networks expand, outsourcing detection services to third parties raises concerns about data security demanding a need for privacy preservation techniques. Homomorphic Encryption (HE) offers a solution, enabling privacy-preserving anomaly detection by allowing computations without compromising privacy. Our work focuses on developing Private classifiers using Cheon-Kim-Kim-Song (CKKS) scheme of HE for intrusion detection. Experiments on the CICIDS2017 dataset using five Machine Learning (ML) classifiers reveal that private Deep Learning (DL) methods achieve performance levels comparable to traditional classifiers. Among these, the Convolutional Neural Network (CNN) strikes a balance between classification speed and detection performance, making it the preferred choice for privacy-preserving intrusion detection.