Zero-day exploits detection with adaptive WavePCA-Autoencoder (AWPA) adaptive hybrid exploit detection network (AHEDNet).

This paper introduces a new probabilistic composite model for the detection of zero-day exploits targeting the capabilities of existing anomaly detection systems in terms of accuracy, computational time, and adaptability. To address the issues mentioned above, the proposed framework consisted of three novel elements. The first key innovations are the introduction of "Adaptive WavePCA-Autoencoder (AWPA)" for pre-processing stage which address the denoising and dimensionality reduction, and contributes to the general dependability and accuracy of zero-day exploit detection. Additionally, a novel "Meta-Attention Transformer Autoencoder (MATA)" for enhancing feature extraction which address the subtlety issue, and improves the model's ability and flexibility to detect new security threats, and a novel "Genetic Mongoose-Chameleon Optimization (GMCO)" was introduced for effective feature selection in the case of addressing the efficiency challenges. Furthermore, a novel "Adaptive Hybrid Exploit Detection Network (AHEDNet)" was introduced which address the dynamic ensemble adaptation issue where the accuracy of anomaly detection is very high with low false positives. The experimental results show the proposed model outperforms the other models of dataset 1 in accuracy of 0.988086 and 0.990469, precision of 0.987976 and 0.990628, recall of 0.988298 and 0.990435, with the lowest Hamming Loss of 0.011914 and 0.009531, also, the proposed model outperforms the other models of dataset 2 in accuracy of 0.9819 and 0.9919, precision of 0.9868 and 0.9968, recall of 0.9813 and 0.9923, with the lowest Hamming Loss of 0.0209 and 0.0109, thus the proposed model outperformed the other models in detecting zero-day exploits.