Abstract: Intrusion detection is an important task in network security,which aims to detect anomalous behaviors and potential attacks.In recent years,deep learning methods have made great breakthroughs in intrusion detection tasks.However,with the rapid development of the Internet industry in recent years,new types of attacks are increasing,and deep learning methods tend to give a prediction result in a known category with high confidence when faced with a new type of category in testing,resulting in the inability to recognize unknown attacks.Based on this,this paper proposes an open set identification method based on uncertainty modeling,i.e.,MC-Dropout is applied to deep learning classifiers to capture uncertainty and thus obtain high-quality prediction probabilities.This open set identification method is not only able to classify known categories,but also able to discriminate unknown categories.The proposed method is validated on the CICIDS2017 dataset,and is able to achieve the detection of unknown categories,and has a certain degree of sophistication compared with other existing methods,and achieves the best performance in all the metrics compared with the benchmark model,which can be effectively applied to the real-world network environment.

Key words: Intrusion detection, Open set identification, Deep learning, MC-Dropout