01536nas a2200229   4500000000100000000000100001008004100002260001200043653003900055653000900094653002600103100001200129700001400141700001300155700001600168245005600184856008100240300001200321490000600333520095300339022001401292       2020                            d  c12/202010aConvolutional Neural Network (CNN)10aHMAC10aSide Channel Analysis1 aXin Jin1 aYong Xiao1 aShiqi Li1 aSuying Wang00aDeep Learning-based Side Channel Attack on HMAC SM3  uhttps://www.ijimai.org/journal/sites/default/files/2020-11/ijimai_6_4_12.pdf  a113-1200 v63 aSM3 is a Chinese hash standard. HMAC SM3 uses a secret key to encrypt the input text and gives an output as the HMAC of the input text. If the key is recovered, adversaries can easily forge a valid HMAC. We can choose different methods, such as traditional side channel analysis, template attack-based side channel analysis to recover the secret key. Deep Learning has recently been introduced as a new alternative to perform Side-Channel analysis. In this paper, we try to recover the secret key with deep learning-based side channel analysis. We should train the network recursively for different parameters by using the same dataset and attack the target dataset with the trained network to recover different parameters. The experiment results show that the secret key can be recovered with deep learning-based side channel analysis. This work demonstrates the interests of this new method and show that this attack can be performed in practice.  a1989-1660