NEW STRATEGIES FOR IMPROVING NETWORK SECURITY AGAINST CYBER ATTACK BASED ON INTELLIGENT ALGORITHMS

Abstract

Gradually, since the number of linked computer systems that use networks linked to the Internet is raised the information that is delivered through those systems becomes more vulnerable to cyber threats. This article presents proposed algorithms based on Machine Learning (ML) that ensure early detection of cyber threats that cause network breaking through the use of the Correlation Ranking Filter feature selection method. These proposed algorithms were applied to the Multi-Step Cyber-Attack Dataset (MSCAD) which consists of 66 features. The proposed strategy will apply machine learning algorithms like Adaptive Boosting-Deep Learning (AdaBoost-Deep Learning) or (ABDL), Multi-Layer Perceptron (MLP), Bayesian Networks Model (BNM), and Random Forest (RF), the feature would be decreased to high valuable of 46 features were included with a threshold of 0.1 or higher. The accuracy would be increased when the no. of features decreased to 46 with a threshold of ≥ 0.1 with the ABDL algorithm producing an accuracy of 99.7076%. The obtained results showed that the proposed algorithm delivered a suitable accuracy of 99.6791% with the ABDL algorithm even with a higher number of features.