A Look at the Dark Side of Hardware Reverse Engineering -- A Case Study
October 01, 2019 Β· Declared Dead Β· π International Verification and Security Workshop
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Sebastian Wallat, Marc Fyrbiak, Moritz SchlΓΆgel, Christof Paar
arXiv ID
1910.01519
Category
cs.CR: Cryptography & Security
Citations
33
Venue
International Verification and Security Workshop
Last Checked
6 months ago
Abstract
A massive threat to the modern and complex IC production chain is the use of untrusted off-shore foundries which are able to infringe valuable hardware design IP or to inject hardware Trojans causing severe loss of safety and security. Similarly, market dominating SRAM-based FPGAs are vulnerable to both attacks since the crucial gate-level netlist can be retrieved even in field for the majority of deployed device series. In order to perform IP infringement or Trojan injection, reverse engineering (parts of) the hardware design is necessary to understand its internal workings. Even though IP protection and obfuscation techniques exist to hinder both attacks, the security of most techniques is doubtful since realistic capabilities of reverse engineering are often neglected. The contribution of our work is twofold: first, we carefully review an IP watermarking scheme tailored to FPGAs and improve its security by using opaque predicates. In addition, we show novel reverse engineering strategies on proposed opaque predicate implementations that again enables to automatically detect and alter watermarks. Second, we demonstrate automatic injection of hardware Trojans specifically tailored for third-party cryptographic IP gate-level netlists. More precisely, we extend our understanding of adversary's capabilities by presenting how block and stream cipher implementations can be surreptitiously weakened.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Cryptography & Security
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
π»
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
π»
Ghosted
Spectre Attacks: Exploiting Speculative Execution
R.I.P.
π»
Ghosted
How To Backdoor Federated Learning
R.I.P.
π»
Ghosted
Evasion Attacks against Machine Learning at Test Time
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted