CyberSecurity Attack Prediction: A Deep Learning Approach
Ouissem Ben Fredj, Alaeddine Mihoub, Moez Krichen, Omar Cheikhrouhou, Abdelouahid Derhab
Abstract
Cybersecurity attacks are exponentially increasing, making existing detection mechanisms insufficient and enhancing the necessity to design more relevant prediction models and approaches. This issue is still an open research problem since existing attack prediction models are failing to follow the huge amount of attacks and their variety. Recently, machine learning approaches and especially deep learning techniques have received much attention from researchers since their unparalleled high performance in several prediction-based fields. In this context, this paper explores the application of deep learning techniques for predicting cybersecurity attacks. Particularly, it proposes a new LSTM (Long Short-Term Memory), RNN (Recurrent Neural Network), and MLP (Multilayer Perceptron) based models carefully designed to predict the type of attack potentially to hap-pen. The proposed models were validated using a recently available dataset called CTF showing encouraging results especially for the LSTM model with an f-measure greater than 93%.