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APT traffic detection is an important task in network security domain, whichis of great significance in the field of enterprise security. Most APT trafficuses encrypted communication protocol as data transmission medium, whichgreatly increases the difficulty of detection. This paper analyzes the existingproblems of current APT encrypted traffic detection methods based on machinelearning, and proposes an APT encrypted traffic detection method based on twoparties and multi-session. This method only needs to extract a small amount offeatures, such as session sequence, session time interval, upstream anddownstream data size, and convert them into images. Then convolutional neuralnetwork method can be used to realize image recognition. Thus, network trafficidentification can be realized too. In the preliminary test of fiveexperiments, this method achieves good experimental results, which verifies theeffectiveness of the method.

APT Encrypted Traffic Detection Method based on Two-Parties and Multi-Session for IoT