Differential fault location identification by machine learning

作     者：Anubhab Baksi Santanu Sarkar Akhilesh Siddhanti Ravi Anand Anupam Chattopadhyay 

作者机构：Nanyang Technological UniversitySingapore Indian Institute of TechnologyChennaiIndia Georgia Institute of TechnologyAtlantaUSA Indian Institute of TechnologyKharagpurIndia 

出 版 物：《CAAI Transactions on Intelligence Technology》 (智能技术学报（英文）)

年 卷 期：2021年第6卷第1期

页      面：17-24页

核心收录：

学科分类：0810[工学-信息与通信工程] 1205[管理学-图书情报与档案管理] 070801[理学-固体地球物理学] 07[理学] 0839[工学-网络空间安全] 0708[理学-地球物理学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：fault typically gauge 

摘      要：As the fault‐based attacks are becoming a more pertinent threat in today s era of edge computing/internet‐of‐things,there is a need to streamline the existing tools for better accuracy and ease of use,so that we can gauge the attacker s power and a proper countermeasure can be devised in the long *** this regard,we propose a machine learning(ML)assisted tool that can be used in the context of a differential fault *** particular,finding the exact fault location by analysing the output difference(typically the XOR of the nonfaulty and the faulty ciphertexts)is somewhat *** the literature survey,we notice that the Pearson s correlation coefficient dominantly is used for this purpose,and has almost become the defacto *** this method can yield good accuracy for certain cases,we argue that an MLbased method is more powerful in all the situations we experiment *** sub-stantiate our claim by showing the relative performances(we choose the commonly used multilayer perceptron as our ML tool)with two variants of Grain‐128a(a stream cipher,and a stream cipher with authentication),the lightweight stream cipher LIZARD and the lightweight block cipher SIMON‐32(where the faults are injected at the fifth last rounds).Our results demonstrate that a common ML tool can outperform the correlation with the same training/testing *** believe that our work extends the state‐of‐the‐art by showing how traditional cryptographic methods can be replaced by a more powerful ML tool.