  In the age of the Internet, people's lives are increasingly dependent on
today's network technology. Maintaining network integrity and protecting the
legitimate interests of users is at the heart of network construction. Threat
detection is an important part of a complete and effective defense system. How
to effectively detect unknown threats is one of the concerns of network
protection. Currently, network threat detection is usually based on rules and
traditional machine learning methods, which create artificial rules or extract
common spatiotemporal features, which cannot be applied to large-scale data
applications, and the emergence of unknown risks causes the detection accuracy
of the original model to decline. With this in mind, this paper uses deep
learning for advanced threat detection to improve protective measures in the
financial industry. Many network researchers have shifted their focus to
exception-based intrusion detection techniques. The detection technology mainly
uses statistical machine learning methods - collecting normal program and
network behavior data, extracting multidimensional features, and training
decision machine learning models on this basis (commonly used include naive
Bayes, decision trees, support vector machines, random forests, etc.).
