Intrusion detection in big data environment using hybrid deep learning algorithm (VAE-CNN)

In the internet era, billions of devices are connected to the network generates large volume of data and the generation rate increases exponentially every day. As the data increases, the chances for cyber attackers to exploit the data increases which results into numerous security threats to organizations and network. Fast and accurate detection of attacks in big data environment is difficult due to its volume and variety and velocity. Over a decade, numerous attack detection systems are developed using machine learning. However, most of the traditional detection systems cannot recognize the attack types specifically which reduces the detection performances and network performances. Thus, the intrusion detection model presented in this research which incorporates deep variational auto-encoder and convolutional neural network to detect intrusions. Experimentations using benchmark dataset validated the proposed model better performances over existing machine learning techniques like logistic regression, random forest, extreme gradient boosting, k-nearest neighbor, and selfscalable heuristic artificial neural network algorithms using accuracy, recall, precision, and F1-score. The proposed model outperforms with a maximum precision of 97.48%, Recall of 99.52%, F1-score of 98.49% and accuracy of 98.65% over conventional intrusion detection algorithms.