  The globalization of the Integrated Circuit (IC) supply chain, driven by
time-to-market and cost considerations, has made ICs vulnerable to hardware
Trojans (HTs). Against this threat, a promising approach is to use Machine
Learning (ML)-based side-channel analysis, which has the advantage of being a
non-intrusive method, along with efficiently detecting HTs under golden
chip-free settings. In this paper, we question the trustworthiness of ML-based
HT detection via side-channel analysis. We introduce a HT obfuscation (HTO)
approach to allow HTs to bypass this detection method. Rather than
theoretically misleading the model by simulated adversarial traces, a key
aspect of our approach is the design and implementation of adversarial noise as
part of the circuitry, alongside the HT. We detail HTO methodologies for ASICs
and FPGAs, and evaluate our approach using TrustHub benchmark. Interestingly,
we found that HTO can be implemented with only a single transistor for ASIC
designs to generate adversarial power traces that can fool the defense with
100% efficiency. We also efficiently implemented our approach on a Spartan 6
Xilinx FPGA using 2 different variants: (i) DSP slices-based, and (ii)
ring-oscillator-based design. Additionally, we assess the efficiency of
countermeasures like spectral domain analysis, and we show that an adaptive
attacker can still design evasive HTOs by constraining the design with a
spectral noise budget. In addition, while adversarial training (AT) offers
higher protection against evasive HTs, AT models suffer from a considerable
utility loss, potentially rendering them unsuitable for such security
application. We believe this research represents a significant step in
understanding and exploiting ML vulnerabilities in a hardware security context,
and we make all resources and designs openly available online:
https://dev.d18uu4lqwhbmka.amplifyapp.com
