随着信息技术的飞速发展,网络攻击事件频发,造成了日益严重的经济损失或社会影响.为了减少损失或预防未来潜在的攻击,需要对网络攻击事件进行溯源以实现对攻击者的挖掘追责.当前的溯源过程主要依赖于人工完成,效率低下.面对日益增加的海量溯源数据和日趋全面的溯源建模分析维度,亟需半自动化或自动化的网络攻击者挖掘方法.提出一种基于图模型的网络攻击溯源方法,建立网络攻击事件溯源本体模型,融合网络攻击事件中提取的线索数据和威胁情报数据,形成网络攻击事件溯源关系图;引入图嵌入算法自动学习嵌有关联线索特征的网络攻击事件特征向量,进而利用历史网络攻击事件特征向量训练SVM(support vector machine)分类器,并基于SVM分类器完成网络攻击者的挖掘溯源;最后,通过实验验证了该方法的可行性和有效性.

Since differential fault analysis(DFA)was first implemented on data encryption standard(DES),many scholars have improved this attack and ex-tended the limit of the original last two rounds to the earlier rounds.However,the performance of the novel at-tacks which target middle rounds is not effective,i.e.the number of correct/incorrect ciphertexts required is very large and the recovered result maybe not correct.We ad-dress this problem by presenting new DFA methods that can break 3DES when injecting faults at round 12 or 13.By simulating the process of single-bit error propagation,we have built two kinds of error propagation models as well as an intermediate error propagation state table.Then we simplify the intermediate states into state tem-plates that will be further used to locate the injected fault position,which is the main difficulty of implementing fault injection in the middle rounds.Finally,in terms of the idea of error propagation and probability theory,we can recover the last round key only using 2 sets of cor-rect/incorrect ciphertexts when inducting fault in the 13th round and 4 sets of correct/incorrect ciphertexts when inducting fault in the 12th round.

Power analysis methods are commonly used for evaluating the security of cryptographic devices.They are characteristically low-cost and display a high success rate and the ability to obtain important device information, e.g., keys. Given the current wide application of deep-learning technology, there is a growing tendency to incorporate power-analysis technology in development.This study investigates non-profiled deep-learning-based power analysis. The labels used in this attack are uncertain,and the attack conditions required are greatly reduced. We choose the Recurrent neural network（RNN）, multilayer perceptron, and convolutional neural network algorithms,which use the same network structure, to recover the keys for the SM4 software and DES hardware implementations.We propose combining the RNN algorithm with power analysis, and validate the benefits experimentally. The experimental results show that they all successfully recover the correct key for the SM4 software implementation,although the RNN algorithm by itself achieves a better effect. This conclusion also applies to attacks on the DES hardware implementation but is limited to labels based on the bit model.

With the development of science and technology, grid technology has played an increasingly important role in people’s lives. Grid technologies prospects has also been given great expectations. However, due to the platform of grid feature relies the Internet and its characteristics in use will encounter a range of security issues, and as an emerging technology, it also will face a variety of challenges, so the research of the problems of grid security communication becomes practical significance. In this article, it will be focused on security communication of grid services and grid access control, and put forward the own points idea.

在不断加剧的网络安全攻防对抗过程中,攻防双方存在着天然的不对称性,网络安全威胁情报共享利用是一种有效提高防护方响应能力和效果的手段.然而威胁情报共享利用中的隐私保护需求与构建完整攻击链的需求之间存在矛盾.针对上述矛盾点,提出一种基于区块链的网络安全威胁情报共享模型,利用了区块链技术的账户匿名性和不可篡改性,使用单向加密函数保护情报中的隐私信息,基于加密后的情报构建完整攻击链,借助区块链的回溯能力完成攻击链中攻击源的解密.最后,通过实验验证了该模型的可行性和有效性.

在建模类攻击场景下,基于多元高斯分布的模板攻击是常用的侧信道逆向分析方法.在同样的场景下,分析了深度学习方法在逆向分析领域的应用,提出了基于深度学习的S盒逆向分析算法.通过选取适用于侧信道逆向分析的深度学习算法、损失函数和标签设计,对类SM4算法进行了S盒逆向恢复实验.实验结果表明,使用深度学习进行S盒逆向分析是可行的,且在一定的条件下优于模板攻击;另外,多层感知机算法预测的结果要优于卷积神经网络算法预测的结果.

芯片后门指令是激活硬件木马的典型方式之一,其安全风险高,影响范围广,且难于检测.本文提出了一种基于能量分析的后门指令检测方法,通过对指令分段穷举、并分别采集其能量信息,可有效区分常规指令和后门指令.实验表明,通过简单能量分析即可从能量迹中直接判定出后门指令.进一步,本文提出了一种自动化识别后门指令的相关能量分析方法,通过判断其相关系数与系数均值之间的关系,可高效、自动地完成后门指令分析.

First, by analyzing the previous fault analysis on RSA using square and multiply algorithm because these attacks require quite precise fault injections like a bit flip and the fault injection can’t be used effectively, this paper builds a new fault analysis model on RSA using square and multiply algorithm based on non-invasive fault, including the square operation fault and the multiply operation fault; Next, an improved fault analysis algorithm based on recovering key segment and a random fault analysis algorithm on RSA using SPA-FA resistant are proposed separately, these proposed fault analysis algorithms are proved in theory and the method of controlling the opportunity in fault injection is suggested; Finally, a fault attack is implemented on RSA in a Protues simulation environment, using the computer program simulation to inject the fault. Experimental results show that the proposed fault analysis algorithm based on recovering key segment can reduce the accuracy requirements of fault injection, and number of fault injection is reduced by about 80%; and the random fault analysis algorithm based on the multiply operation fault can cancel the dependence between the each key bit recover, further improve the feasibility of fault analysis.

在实际密码系统或模块的安全性评估中,对未知密码算法的逆向分析是一项重要的评估内容.目前关于密码算法的逆向分析方式主要分为数学分析和物理旁路分析2种,后者因其代价低、通用性高等优点更为流行.基于独立分量技术的侧信道分析技术绕过传统侧信道分析中的"先猜测后确定"的攻击思路限制,直接恢复中间状态值.研究了类GIFT算法在逆向分析下的安全性,利用GIFT算法结构的特点,将P置换输入作为独立分量攻击观测条件,利用独立分量技术成功恢复出了S盒内容.该结果是最早关于类GIFT算法的逆向分析结果之一,其方法对于其他未知算法的逆向分析也具有参考意义.

In side channel attack (SCA) studies, it is widely believed that unprotected implementations leak information about the intermediate states of the internal cryptographic process. However, directly recovering the intermediate states is not common practice in today's SCA study. Instead, most SCAs exploit the leakages in a "guess- and-determine" way, where they take a partial key guess, compute the corresponding intermediate states, then try to identify which one fits the observed leakages better. In this paper, we ask whether it is possible to take the other way around-directly learning the intermediate states from the side channel leakages. Under certain circumstances, we find that the intermediate states can be efficiently recovered with the well-studied Independent Component Analysis (ICA). Specifically, we propose several methods to convert the side channel leakages into effective ICA observations. For more robust recovery, we also present a specialized ICA algorithm which exploits the specific features of circuit signals. Experiments confirm the validity of our analysis in various circumstances, where most intermediate states can be correctly recovered with only a few hundred traces. Our approach brings new possibilities to the current SCA study, including building an alternative SCA distinguisher, directly attacking the middle encryption rounds and reverse engineering with fewer restrictions. Considering its potential in more advanced applications, we believe our ICA-based SCA deserves more research attention in the future study.

The past ten years have seen tremendous progress in the uptake of side channel analysis in various applications. Among them, Side Channel Analysis for Reverse Engineering (SCARE) is an especially fruitful area. Taking the side channel leakage into account, SCARE efficiently recovers secret ciphers in a non-destructive and non-intrusive manner. Unfortunately, most previous works focus on customizing SCARE for a certain type of ciphers or implementations. In this paper, we ask whether the attacker can loosen these restrictions and reverse secret block ciphers in a more general manner. To this end, we propose a SCARE based on Linear Regression Attack (LRA), which simultaneously detects and analyzes the power leakages of the secret encryption process. Compared with the previous SCAREs, our approach uses less a priori knowledge, covers more block cipher instances in a completely non-profiled manner. Moreover, we further present a complete SCARE flow with realistic power measurements of an unprotected software implementation. From traces that can barely recognize the encryption rounds, our experiments demonstrate how the underlying cipher can be recovered step-by-step. Although our approach still has some limitations, we believe it can serve as an alternative tool for reverse engineering in the future.