Deep Learning Based APT Attack Recognition

Advanced Persistent Threat (APT) attacks pose a significant challenge to network security due to their covert and persistent nature. This paper proposes a deep learning-based method to identify APT attacks. Our approach analyzes network traffic data and employs the concept of "data layer by layer reduction, information layer by layer confirmation" to scrutinize encrypted traffic, effectively reducing false alarms while accurately identifying threats. By leveraging deep learning technology, we can effectively detect APT attack patterns. Experimental results demonstrate that our method excels in detection accuracy and real-time performance, highlighting its strong practicality and generalizability.