Extremely boosted neural network for more accurate multi-stage Cyber attack prediction in cloud computing environment

Abstract

Abstract There is an increase in cyberattacks directed at the network behind firewalls. An all-inclusive approach is proposed in this assessment to deal with the problem of identifying new, complicated threats and the appropriate countermeasures. In particular, zero-day attacks and multi-step assaults, which are made up of a number of different phases, some malicious and others benign, illustrate this problem well. In this paper, we propose a highly Boosted Neural Network to detect the multi-stageattack scenario. This paper demonstrated the results of executing various machine learning algorithms and proposed an enormously boosted neural network. The accuracy level achieved in the prediction of multi-stage cyber attacks is 94.09% (Quest Model), 97.29% (Bayesian Network), and 99.09% (Neural Network). The evaluation results of the Multi-Step Cyber-Attack Dataset (MSCAD) show that the proposed Extremely Boosted Neural Network can predict the multi-stage cyber attack with 99.72% accuracy. Such accurate prediction plays a vital role in managing cyber attacks in real-time communication.

Keywords

• Zero-day attack
• Multi-stage cyber attack
• Neural network
• Quest
• Bayesian network
• Intrusion detection