Novel adaptive cyberattack prediction model using an enhanced genetic algorithm and deep learning (AdacDeep)

ABSTRACT

Some of the problems of extant cyberattack prediction approaches are low prediction accuracy, high false positive rate, very long training time, and the choice of hyperparameters to overcome overfitting or under fitting the model on the training data. These problems have culminated in the escalation of cyberattacks in recent times and as such significant improvement to the performance of extant models is crucial. Some deep learning architectures such as Recurrent Neural Networks (RNN) have been applied to cyberattack prediction. However, Recurrent Neural Networks (RNN) suffer from the vanishing and exploding gradient problem, and are difficult to train. Also, determining the different states and hyperparameters of the network for optimal prediction performance is difficult. Therefore, this paper proposes a novel approach called AdacDeep that uses an Enhanced Genetic Algorithm (EGA), Deep Autoencoder and a Deep Feedforward Neural Network (DFFNN) with backpropagation learning to accurately predict different attack types. The performance of AdacDeep is evaluated using two well-known datasets, namely, the CICIDS2017 and UNSW_NB15 datasets as the benchmark. The experimental results show that AdacDeep outperforms other state-of-the-art comparative models in terms of prediction accuracy with 0.22–35% improvement, F-Score with 0.1–34.7% improvement and very low false positive rate.

KEYWORDS:

• Deep learning
• multi-objective genetic algorithm
• cyberattack prediction
• information security
• network traffic