Extremely boosted neural network for more accurate multi-stage Cyber attack prediction in cloud computing environment - Surjeet Dalal (4906894) (2023)
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. Correction: Extremely boosted neural network for more accurate multi-stage Cyber attack prediction in cloud computing environment: https://dx.doi.org/10.1186/s13677-023-00551-2, published online 28 November 2023. Other Information Published in: Journal of Cloud Computing License: https://creativecommons.org/licenses/by/4.0 See article on publisher's website: https://dx.doi.org/10.1186/s13677-022-00356-9