【作者】 陈平；

【导师】 胡红钢；

【作者基本信息】 中国科学技术大学 ， 网络空间安全， 2020， 硕士

【摘要】 近年来,随着智能化信息技术的普及,人们的衣食住行都离不开各种智能设备和嵌入式设备。这些设备都实现了安全模块,而安全模块的核心就是密码技术。侧信道攻击作为一种面向具体密码技术实现的密码分析方法,与传统的数学分析方法相比,它具有更强的实践性。更重要的是,侧信道攻击破解信息安全系统的案例层出不穷,受到了越来越多密码分析研究人员的关注。侧信道攻击的过程可以简单概括为:攻击者使用示波器采集密码算法在目标设备上运行时的计时、功耗、电磁辐射、声音、热量、射频、故障输出等旁路泄露信息,接着分析这些信息和密码设备执行过程中的中间运算、中间状态的关系(这些中间运算、中间状态依赖于密码算法的密钥),进而根据分析结果恢复出密钥。攻击者采集的旁路泄露信息又被称作能量迹,在分析能量迹和中间运算、中间状态的关系之前,需要对能量迹进行预处理。能量迹预处理是分析和猜测密钥的基础,因为密钥分析依赖于能量迹信号,能量迹信号的质量会很大程度上影响猜测密钥的准确度,而影响能量迹质量的一个因素就是和密钥无关的噪声。因此,为了减少侧信道攻击的复杂性,需要进一步探索有效的方法对能量迹进行降噪,提高和密钥的相关性。本文作者在进行相关研究工作时发现,卷积神经网络处理端到端的侧信道攻击时表现良好,但卷积神经网络在能量迹预处理中的应用还有很大的缺口。于是,本文探索使用卷积神经网络对能量迹进行降噪预处理,为此提出了两种基于卷积神经网络的预处理方案。1、提出了基于SincNet卷积神经网络的能量迹预处理方案。本文实现了基于sinc带通滤波器的Sinc卷积层SincConv1D,以此构建SincNet网络进行侧信道攻击。Sinc卷积层的每个sinc滤波器均能够自适应地学习能量迹的高和低截止频率,对能量迹进行频域预处理,滤除噪声频带,为后续的网络层提供和密钥相关性更高的特征,最终达到以更少的能量迹条数成功实施侧信道攻击的目的。2、提出了基于Sinc卷积降噪自编码器的能量迹预处理方案。本文将Sinc卷积层和传统的卷积层结合起来构造卷积降噪自编码器,并结合能量迹估计算法,训练从含噪能量迹到干净能量迹的能量迹预处理模型。该方案能够有效地降低能量迹中噪声的影响,减少侧信道攻击的复杂性。最后,对于上述两种方案本文均在公开的侧信道数据集上展开了实验,实验结果表明了上述两种方案的有效性。更多还原

【Abstract】 Recently,with the rapid development of intelligence information technology,peo-ple are further inter connected with various intelligent devices and embedded devices.These devices are required to comprise security models,the fundamental of which is cryptography.Side-channel attack is a practical cryptoanalysis comparing to classical algebraic analysis,and faces the security of the implementation of cryptographic tech-nology.This kind of attack brings great threats to secure systems and thus becomes an attractive issue of more and more cryptoanalysis researchers.In a common side-channel attack,attackers collect the timing behavior,power con-sumption,electromagnetic emanation,acoustic emissions or thermal effects of the tar-get device running a cryptographic algorithm with an employed acquisition equipment.Then,they analyze the relationship between the collected data and intermediate values during the device’s execution.Since the intermediate values usually depend on the key of the cryptographic algorithm,attackers could recover the key based on the analysis results.The collected data are usually named as power traces.As the quality of power traces greatly affects the success rate,pre-processing is required before the analysis performing.A major factor that affects the quality of power traces is key-independent noise.Hence,we need to study effective methods to filter the noise of power traces and improve the relevance between power traces and the key to reduce the complexity of side-channel attacks.Previous work shows that the convolutional neural networks have good performance in handling end-to-end side-channel attacks while it lacks applica-tions in pre-processing power traces.Thus,the thesis researches on such applications and proposes two schemes that pre-process power traces and reduce the noise based on convolutional neural networks.One is a power traces pre-processing scheme based on SincNet convolutional neu-ral network.We implement the Sinc convolution layer called SincConv1D based on the sinc band-pass filter to construct a SincNet network for side channel attacks.Each sinc filter of the Sinc convolutional layer can adaptively learn the high and low cut-off frequencies of power traces,and filter out the noisy bands to provide higher key related features for subsequent network layers.We ultimately achieve the goal of performing the side-channel attack successfully with fewer power traces.The other is a power traces pre-processing scheme based on Sinc convolutional denoising auto-encoder.In the thesis,the Sinc layer and traditional convolutional layers are combined to construct a convolutional denoising auto-encoder.It aims to train the power traces pre-processing model from noisy power traces to clean power traces.In addition,the scheme can effectively lower the influence of noise in power traces and reduce the complexity of side-channel attacks.Finally,experiments are carried out on the public side-channel dataset for the above two schemes.The experimental results show the effectiveness of the proposed schemes.更多还原