Visible to the public Analysis of Side-Channel Attack AES Hardware Trojan Benchmarks against Countermeasures

TitleAnalysis of Side-Channel Attack AES Hardware Trojan Benchmarks against Countermeasures
Publication TypeConference Paper
Year of Publication2017
AuthorsK, S. K., Sahoo, S., Mahapatra, A., Swain, A. K., Mahapatra, K. K.
Conference Name2017 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)
ISBN Number978-1-5090-6762-6
KeywordsAES benchmark, Benchmark testing, composability, cryptography, cyber physical systems, Encryption, Hardware, hardware security, hardware security issue, hardware trojan, HT benchmark, HT defense, HT design, HT detection, pubcrawl, resilience, Resiliency, Side-channel attack, side-channel attack AES hardware Trojan benchmarks, side-channel attacks, trojan horse detection, Trojan horses

Hardware Trojan (HT) is one of the well known hardware security issue in research community in last one decade. HT research is mainly focused on HT detection, HT defense and designing novel HT's. HT's are inserted by an adversary for leaking secret data, denial of service attacks etc. Trojan benchmark circuits for processors, cryptography and communication protocols from Trust-hub are widely used in HT research. And power analysis based side channel attacks and designing countermeasures against side channel attacks is a well established research area. Trust-Hub provides a power based side-channel attack promoting Advanced Encryption Standard (AES) HT benchmarks for research. In this work, we analyze the strength of AES HT benchmarks in the presence well known side-channel attack countermeasures. Masking, Random delay insertion and tweaking the operating frequency of clock used in sensitive operations are applied on AES benchmarks. Simulation and power profiling studies confirm that side-channel promoting HT benchmarks are resilient against these selected countermeasures and even in the presence of these countermeasures; an adversary can get the sensitive data by triggering the HT.

Citation Keyk_analysis_2017