SIMCom: Statistical sniffing of inter-module communications for runtime hardware trojan detection

Faiq Khalid, Syed Rafay Hasan, Osman Hasan, Muhammad Shafique

Research output: Contribution to journalArticlepeer-review


Timely detection of Hardware Trojans (HTs) has become a major challenge for secure integrated circuits. We present a run-time methodology for HT detection that employs a multi-parameter statistical traffic modeling of the communication channel in a given System-on-Chip (SoC), named as SIMCom. The main idea is to model the communication using multiple side-channel information like the Hurst exponent, the standard deviation of the injection distribution, and the hop distribution jointly to accurately identify HT-based online anomalies (that affects the communication without affecting the protocols or control signals). At design time, our methodology employs a “property specification language” to define and embed assertions in the RTL, specifying the correct communication behavior of a given SoC. At run-time, it monitors the anomalies in the communication behavior by checking the execution patterns against these assertions. For illustration, we evaluate SIMCom for three SoCs, i.e., SoC1 (four single-core MC8051 and UART modules), SoC2 (four single-core MC8051, AES, ethernet, memctrl, BasicRSA, RS232 modules), and SoC3 (four single-core LEON3 connected with each other and AES, ethernet, memctrl, BasicRSA, RS23s modules microcontrollers). The experimental results show that with the combined analysis of multiple statistical parameters, SIMCom is able to detect all the benchmark Trojans (available on trust-hub) with less than 1% area and power overhead.

Original languageEnglish (US)
Article number103122
JournalMicroprocessors and Microsystems
StatePublished - Sep 2020


  • Communication
  • Hardware trojans
  • Hurst exponent
  • Internet-of-thing
  • IoT
  • Microcontrollers
  • Statistical modeling

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence


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