Fetching the paper…
Reading the bibliography…
Current Hardware Trojan (HT) detection techniques are mostly developed based on a limited set of HT benchmarks.
L. H. Goldstein and E. L. Thigpen, “Scoap: Sandia controllability/observability analysis program,” in Proceedings of the 17th Design Automation Conference , 1980, pp. 190–196
1980
Earlier work this paper cites.
D. Bryan, “The ISCAS’85 benchmark circuits and netlist format,” p. 39, 1985
1985
Earlier work this paper cites.
M. L. Bushnell, “Essentials of electronic testing for digital,” Memory & Mixed-Signal VLSI Circuits , 2000
2000
Earlier work this paper cites.
A. A. Hagberg, D. A. Schult, and P. J. Swart, “Exploring network structure, dynamics, and function using networkx,” in Proceedings of the 7th Python in Science Conference , G. Varoquaux, T. Vaught, and J. Millman, Eds., Pasadena, CA USA, 2008, pp. 11 – 15
2008
Earlier work this paper cites.
R. S. Chakraborty, F. Wolff, S. Paul, C. Papachristou, and S. Bhunia, “Mero: A statistical approach for hardware trojan detection,” in International Workshop on Cryptographic Hardware and Embedded Systems . Springer, 2009, pp. 396–410
2009
Earlier work this paper cites.
H. Salmani, M. Tehranipoor, and R. Karri, “On design vulnerability analysis and trust benchmarks development,” in 2013 IEEE 31st international conference on computer design (ICCD) . IEEE, 2013, pp. 471–474
2013
Earlier work this paper cites.
C. Wolf, J. Glaser, and J. Kepler, “Yosys-a free verilog synthesis suite,” in Proceedings of the 21st Austrian Workshop on Microelectronics (Austrochip) , 2013
2013
Earlier work this paper cites.
L. Bassett, Introduction to JavaScript Object Notation: A To-the-Point Guide to JSON . Sebastopol: O’Reilly Media, 2015. [Online]. Available: https://books.google.com/books?id=Qv9PCgAAQBAJ
2015
Earlier work this paper cites.
L. Amarú, P.-E. Gaillardon, and G. De Micheli, “The epfl combinational benchmark suite,” in Proceedings of the 24th International Workshop on Logic & Synthesis (IWLS) , no. CONF, 2015
2015
Earlier work this paper cites.
H. Salmani, “Cotd: Reference-free hardware trojan detection and recovery based on controllability and observability in gate-level netlist,” IEEE Transactions on Information Forensics and Security , vol. 12, no. 2, pp. 338–350, 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot et al. , “Mastering the game of go with deep neural networks and tree search,” nature , vol. 529, no. 7587, pp. 484–489, 2016
2016
Earlier work this paper cites.
B. Shakya, T. He, H. Salmani, D. Forte, S. Bhunia, and M. Tehranipoor, “Benchmarking of hardware trojans and maliciously affected circuits,” Journal of Hardware and Systems Security , vol. 1, no. 1, pp. 85–102, 2017
2017
Earlier work this paper cites.
K. Hasegawa, M. Yanagisawa, and N. Togawa, “Trojan-feature extraction at gate-level netlists and its application to hardware-trojan detection using random forest classifier,” in 2017 IEEE International Symposium on Circuits and Systems (ISCAS) . IEEE, 2017, pp. 1–4
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
V. Jyothi, P. Krishnamurthy, F. Khorrami, and R. Karri, “Taint: Tool for automated insertion of trojans,” in 2017 IEEE International Conference on Computer Design (ICCD) . IEEE, 2017, pp. 545–548
2017
Earlier work this paper cites.
S. Wallat, M. Fyrbiak, M. Schlögel, and C. Paar, “A look at the dark side of hardware reverse engineering-a case study,” in 2017 IEEE 2nd International Verification and Security Workshop (IVSW) . IEEE, 2017, pp. 95–100
2017
Earlier work this paper cites.
L. Tai, G. Paolo, and M. Liu, “Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2017, pp. 31–36
2017
Earlier work this paper cites.
S. M. Sebt, A. Patooghy, H. Beitollahi, and M. Kinsy, “Circuit enclaves susceptible to hardware trojans insertion at gate-level designs,” IET Computers & Digital Techniques , vol. 12, no. 6, pp. 251–257, 2018
2018
Cited alongside, same era.
J. Cruz, Y. Huang, P. Mishra, and S. Bhunia, “An automated configurable trojan insertion framework for dynamic trust benchmarks,” in 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE) . IEEE, 2018, pp. 1598–1603
2018
Cited alongside, same era.
M. Fyrbiak, S. Wallat, P. Swierczynski, M. Hoffmann, S. Hoppach, M. Wilhelm, T. Weidlich, R. Tessier, and C. Paar, “HAL- the missing piece of the puzzle for hardware reverse engineering, trojan detection and insertion,” IEEE Transactions on Dependable and Secure Computing , 2018
2018
Cited alongside, same era.
D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel et al. , “A general reinforcement learning algorithm that masters chess, shogi, and go through self-play,” Science , vol. 362, no. 6419, pp. 1140–1144, 2018
S.-Y. Yu, R. Yasaei, Q. Zhou, T. Nguyen, and M. A. Al Faruque, “Hw2vec: A graph learning tool for automating hardware security,” in 2021 IEEE International Symposium on Hardware Oriented Security and Trust (HOST) . IEEE, 2021, pp. 13–23
2021
Later among the works it cites.
A. Raffin, A. Hill, A. Gleave, A. Kanervisto, M. Ernestus, and N. Dormann, “Stable-baselines3: Reliable reinforcement learning implementations,” Journal of Machine Learning Research , vol. 22, no. 268, pp. 1–8, 2021. [Online]. Available: http://jmlr.org/papers/v22/20-1364.html
2021
Later among the works it cites.
V. Gohil, S. Patnaik, H. Guo, D. Kalathil, and J. Rajendran, “Deterrent: detecting trojans using reinforcement learning,” in Proceedings of the 59th ACM/IEEE Design Automation Conference , 2022, pp. 697–702
2022
Later among the works it cites.
V. Gohil, H. Guo, S. Patnaik, and J. Rajendran, “Attrition: Attacking static hardware trojan detection techniques using reinforcement learning,” in Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security , 2022, pp. 1275–1289
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
J. Yin, Y. Eckert, S. Che, M. Oskin, and G. H. Loh, “Toward more efficient noc arbitration: A deep reinforcement learning approach,” in Proc. IEEE 1st Int. Workshop AI-assisted Des. Architecture , vol. 128, 2018
2018
Cited alongside, same era.
S. Yu, W. Liu, and M. O’Neill, “An improved automatic hardware trojan generation platform,” in 2019 IEEE Computer Society Annual Symposium on VLSI (ISVLSI) . IEEE, 2019, pp. 302–307
2019
Cited alongside, same era.
T. T. Nguyen and V. J. Reddi, “Deep reinforcement learning for cyber security,” IEEE Transactions on Neural Networks and Learning Systems , 2019
2019
Cited alongside, same era.
J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter, “Learning agile and dynamic motor skills for legged robots,” Science Robotics , vol. 4, no. 26, p. eaau5872, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
S. Narayanan, R. Gupta, and M. A. Breuer, “Optimal configuring of multiple scan chains,” IEEE transactions on computers , vol. 42, no. 9, pp. 1121–1131, 1993
2019
Cited alongside, same era.
2022
Later among the works it cites.
A. Sarihi, A. Patooghy, P. Jamieson, and A.-H. A. Badawy, “Hardware trojan insertion using reinforcement learning,” in Proceedings of the Great Lakes Symposium on VLSI 2022 , 2022, pp. 139–142
2022
Later among the works it cites.
2022
Later among the works it cites.
T. Perez and S. Pagliarini, “Hardware trojan insertion in finalized layouts: From methodology to a silicon demonstration,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems , 2022
2022
Later among the works it cites.
A. Hepp, T. Perez, S. Pagliarini, and G. Sigl, “A pragmatic methodology for blind hardware trojan insertion in finalized layouts,” in Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design , 2022, pp. 1–9
2022
Later among the works it cites.
S. Patnaik, V. Gohil, H. Guo, and J. J. Rajendran, “Reinforcement learning for hardware security: Opportunities, developments, and challenges,” in 2022 19th International SoC Design Conference (ISOCC) , 2022, pp. 217–218
2022
Later among the works it cites.
“Trust-Hub,” https://trust-hub.org/ , accessed: 2023-11-08
2023
Closest in time.
2023
Closest in time.
C. Krieg, “Reflections on trusting trusthub,” in 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD) . IEEE, 2023, pp. 1–9
2023
Closest in time.
E. Puschner, T. Moos, S. Becker, C. Kison, A. Moradi, and C. Paar, “Red team vs. blue team: A real-world hardware trojan detection case study across four modern cmos technology generations,” in 2023 IEEE Symposium on Security and Privacy (SP) . IEEE, 2023, pp. 56–74
2023
Closest in time.
“GitHub - NMSU-PEARL/Hardware-Trojan-Insertion-and-Detection-with-Reinforcement-Learning: Reinforcement Learning-based Hardware Trojan Detector — github.com,” https://github.com/NMSU-PEARL/Hardware-Trojan-Insertion-and-Detection-with-Reinforcement-Learning , [Accessed 27-12-2023]
2023
Closest in time.
“ISCAS High-Level Models,” https://web.eecs.umich.edu/~jhayes/iscas.restore/benchmark.html , accessed: 2023-11-07
2023
Closest in time.
“WWW: ISCAS89 Sequential Benchmark Circuits — filebox.ece.vt.edu,” https://filebox.ece.vt.edu/~mhsiao/iscas89.html , [Accessed 22-01-2024]
2024
Closest in time.
“ITC’99 Benchmark Homepage — cerc.utexas.edu,” https://www.cerc.utexas.edu/itc99-benchmarks/bench.html , [Accessed 22-01-2024]
2024
Closest in time.