Research on the Method of Side Channel Attack Based on RF Algorithm

doi:10.3969/j.issn.1671-1122.2022.01.003

Abstract

Abstract:

At present, the full potential of random forest(RF) algorithm in the field of side channel analysis has not been fully utilized. This paper proposed a side channel attack based on RF algorithm, which optimized the model from input data processing and parameter control, and improved it from feature point selection and RF algorithm parameter optimization. For high-dimensional data, the SOST correlation coefficient method was used to select 100 feature points, and then optimize the parameters of RF algorithm. The results show that compared with the RF algorithm directly based on the default parameter value, the attack success rate of this method is dramatically, and the generalization ability of the model is also improved by a certain extent.

Key words: machine learning, side channel attack, RF algorithm, model selection

CLC Number:

TP309

DUAN Xiaoyi, LI You, LINGHU Yunxing, HU Ronglei. Research on the Method of Side Channel Attack Based on RF Algorithm[J]. Netinfo Security, 2022, 22(1): 19-26.

Figures/Tables 24

References 13

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参数	含义	默认值
n_estimators	随机森林中树的棵数	10
max_features	判断条件可选的特征数量	None
max_depth	树的最大深度	None
min_sample_split	最小划分样本数	2
max_leaf_nodes	最大叶子节点数	None
min_impurity_split	节点的最小不纯度	0
random_state	随机状态	None

参数	参数对模型评估性能的影响	影响程度
n_estimators	n_estimators增加不影响单个模型的复杂度	非常大
max_depth	max_depth增加模型更简单,且向图像左边移动	大
min_samples_leaf	min_samples_leaf增加模型更简单,且向图像左边移动	中等
min_samples_split	min_samples_leaf增加模型更简单,且向图像左边移动	中等
max_features	既能使模型更简单,也能使模型更复杂	弱

n_estimators	模型准确率
100	0.938444
200	0.941667
300	0.943778
400	0.944333
500	0.944444
600	0.945222
700	0.945778
800	0.945111
900	0.944778
1000	0.944444

n_estimators	模型准确率
600	0.945222
650	0.944778
700	0.945778
750	0.945000
800	0.945111

n_estimators	模型准确率
690	0.945333
700	0.945778
710	0.945778
720	0.945556