Abstract: Cache side-channel attacks pose a serious threat to the security of various systems, and detecting the attacks can effectively block the attacks. Therefore, an AD detection model based on decision tree and AdaBoost is proposed to quickly and effectively identify cache side-channel attacks by matching system hardware event information features. Firstly, the characteristics of cache side-channel attacks are analyzed, and attack hardware event feature patterns are extracted. Secondly, the decision tree's rapid response capability is utilized, combined with AdaBoost's weighted iterative learning of data samples, to train the model on different load conditions. The model is optimized to improve the overall detection accuracy under different loads. Experimental results show that the detection accuracy of this model under different system load conditions is not less than 98.8%, and it can quickly and accurately detect cache side-channel attacks.

Key words: system security, cache side-channel attack, machine learning, detection method