Identification of FSM State Registers by Analytics of Scan-Dump Data

Abstract

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.

Research on the Implementation Method of Situational Awareness Strategy by Fusing Computational Migration Models

“…However, with the continuous change of the monitoring environment and the increase of time-series data, the traditional situational awareness strategies can no longer meet the increasingly complex monitoring environment, which makes the processing efficiency and accuracy reduced. Existing situational awareness strategies mostly use the feature matrix situational prediction method, which maps the collected time-series data to the corresponding time-series matrix [9] and extracts the features of the matrix to realize the relevant situational prediction. The above methods can effectively solve the processing of long time-series data [10][11][12] , but they do not take into account the data processing load, and the overall processing speed is slow, resulting in weak real-time results.…”