The detection of cyber threats and cybersecurity can be benefitted greatly from the advances in artificial intelligence (AI). Cyber threat detection is the main focus of cybersecurity. It has used various tools and techniques to find unauthorized access to systems, attacks, effective security incidents or applications, and networks. Owing to data flow presence over networks and extensive devices, the possibility of intrusion detection and cyberattacks has increased. Monitorization of this immense volume of data traffic is found to be tough, though machine learning (ML) techniques effectively support this task. This study develops an auction-based optimization with machine learning for cyber threat detection and classification (ABOML-CTDC) technique. The presented ABOML-CTDC technique concentrates on the identification and classification of cyber threats in accomplishing cybersecurity. The proposed ABOML-CTDC technique makes use of the ABO-based feature subset selection (ABO-FSS) technique to elect optimal features. For cyber threat detection, the ABOML-CTDC technique utilizes a variational autoencoder (VAE) model. Finally, the moth flame optimization (MFO) algorithm is exploited for the parameter selection of the VAE method del to improve the detection rate. The experimental validation of the ABOML-CTDC approach is tested on benchmark datasets and the results pointed out the promising results on detecting threats over recent state-of-the-art methods.