The expansion of the internet has made everyone’s personal and professional lives
more transparent. There are network security issues because people like sharing
resources under the right conditions. Academics have demonstrated significant
interest in situation awareness, which includes situation prediction, situation
appraisal, and event detection, rather than focusing on the security of a single
device in the network. Multi-stage attack forecasting and security situation
awareness are two significant issues for network supervisors because the future
usually is unknown. Hence, this study suggests combined intuitionistic fuzzy
sets and deep neural network (CIFS-DNN) for network security situation
prediction. The goal is to provide network administrators with a resource they
can use as a point of reference while they formulate and carry out preventive
actions in the event of a network assault. The job requires differentiating
between the event of an assault and a typical instance, as well as
differentiating between the various sorts of attacks and a typical case. In this
article, we present a model that can more accurately and effectively forecast
network security scenarios, and our experiments bear this out. The results show
that the proposed technique is successful and exact in predicting network
security issues. The suggested CIFS-DNN approach has a low delay rate of 10%, a
low latency rate of 20%, a low error rate of 25%, a high prediction ratio of
98.6%, a high security rate of 98.3%, a high accuracy ratio of 99.6%, and a high
efficiency ratio of 93.9%
