Big data analytics have gained tremendous successes in mining valuable information in various fields. However, its potential to solve complex problems in hardware security has not been adequately tapped. This paper presents a non-invasive approach to identify the state registers of a finite state machine (FSM) in an integrated chip. The state registers of the FSM are mined from the scan-dump data by exploiting the strongly connected property and chronologically correlated state codes of the FSM. The sequence of data scanned out of each scan register is partitioned into non-overlapping strings of high weighted frequencies by a string-matching algorithm. A coherency between a pair of registers is defined and computed based on the partitioned strings. The dimension of the coherency matrix is first reduced by pruning some registers of low influence by a regression analysis. The registers are then clustered to minimize the within-cluster variances based on their coherency values. The proposed scheme is applied to some IP cores from OpenCores. The experimental results show that our scheme can correctly identify the FSM state registers in most designs with high hit rate.

中文翻译：

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通过扫描转储数据分析识别 FSM 状态寄存器

大数据分析在挖掘各个领域的有价值信息方面取得了巨大成功。然而，它解决硬件安全中复杂问题的潜力尚未得到充分挖掘。本文提出了一种非侵入式方法来识别集成芯片中有限状态机 (FSM) 的状态寄存器。FSM 的状态寄存器是通过利用 FSM 的强连接属性和按时间顺序相关的状态代码从扫描转储数据中挖掘出来的。从每个扫描寄存器扫描出的数据序列通过串匹配算法被划分为高加权频率的非重叠串。一对寄存器之间的一致性是根据分区字符串定义和计算的。首先通过回归分析修剪一些影响较小的寄存器来降低一致性矩阵的维度。然后将寄存器聚类以根据它们的一致性值最小化簇内差异。所提出的方案适用于 OpenCores 的一些 IP 核。实验结果表明，我们的方案可以正确识别大多数设计中的 FSM 状态寄存器，并且具有较高的命中率。