Research and Implementation on Power Analysis Attacks for Unbalanced Data

Xiaoyi Duan,Xiaohong Fan,You Li,Dong Chen,Xiuying Li,Ding Ding

doi:10.1155/2020/5695943

Abstract

In the power analysis attack, when the Hamming weight model is used to describe the power consumption of the chip operation data, the result of the random forest (RF) algorithm is not ideal, so a random forest classification method based on synthetic minority oversampling technique (SMOTE) is proposed. It compensates for the problem that the random forest algorithm is affected by the data imbalance and the classification accuracy of the minority classification is low, which improves the overall classification accuracy rate. The experimental results show that when the training set data is 800, the random forest algorithm predicts the correct rate of 84%, but the classification accuracy of the minority data is 0%, and the SMOTE-based random forest algorithm improves the prediction accuracy of the same set of test data by 91%. The classification accuracy rate of a few categories has increased from 0% to 100%.

Highlights

In the process of machine learning, the relevant learning algorithm is generated from the accumulated data and the data obtained from experience is provided to the learning algorithm to generate the corresponding model after processing
In COSADE 2012, Heuser et al based on the hamming weight model, used the multiclassification support vector machine (SVM) with probability to divide the keys into nine categories [3]. eir experiments show that in order to achieve the same goal under strong noise, SVM attacks require less training power curves than template attacks, so SVM attacks are more general
Random Forest Algorithm. e random forest algorithm is a common and effective supervised machine learning algorithm which is proposed by Leo Breiman and Adele Cutler [10]. is algorithm is an integrated algorithm based on decision trees, and it improves the disadvantages of decision trees and makes classification results better

Summary

Introduction

In the process of machine learning, the relevant learning algorithm is generated from the accumulated data and the data obtained from experience is provided to the learning algorithm to generate the corresponding model after processing. Machine learning is a method of “learning” from data and being able to analyze unknown data It conforms to the needs of side channel attack to crack unknown encryption information. The experiment shows that the parameter setting of support vector machine (SVM) has a great impact on the classification performance, but the number of power curves and sampling points has a small impact on the results. E power analysis attack based on machine learning is a classification problem and it is similar to the traditional template attack. The SMOTE technique is introduced in the attack of power consumption, and the authors suggest a method of random forest classification based on SMOTE, using the SMOTE algorithm to synthesize the minority samples to achieve the balance and improve the accuracy of the minority and the whole.

The Influence of Unbalanced Data Distribution on Random Forest Algorithm

Introduction to Algorithms

Random Forest Algorithm Based on SMOTE

Characteristics of Data and Feature Point Selection

Experimental Results and Analysis