Hardware Spiking Neural Network based Sbox AES

Hardware Spiking Neural Network based Sbox AES

Ma, Hanyu (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Circuits and Systems)

van Leuken, T.G.R.M. (mentor)
Aledo Ortega, D. (graduation committee)

Delft University of Technology

Electrical Engineering | Circuits and Systems

2021-11-29

Hardware cryptographic algorithm implementation is easy to attack by side-channel attacks. The power-based side-channel attacks are powerful among several side-channel attacks. This attack methods use the relationship between the leakage model and power traces to reveal the secret key. Some existing countermeasures like mask and hide can protect the algorithms from attacking. However, they can not break the relationship between power traces and the leakage model. Based on the property of the neural network, the linear relationship can be easily broken. Furthermore, the spiking neural network is more hardware-friendly than a conventional neural network. The design replaces the sbox in AES with a pipeline spiking neural network-based sbox and implements it in hardware. The help of the FPGA attack platform demonstrates that the proposed design can resist DPA, CPA, Template Attacks, and Deep Learning-based attacks.The developed model is focused on the design phase of the New Sluices and it showed the impact of scope change on the project progress. The simulations resulted in 19 months of delay compared to the initial duration of the project. Moreover, it was shown that the impact of the second order effect contributed within 10 months of that delay. Finally, two further investigations were conducted regarding staff morale and optimism bias. The results showed that uncertainties and poor risk management can impact the staff morale through rework, lack of transparency, and bad leadership. To overcome the loss of morale the presence of a skilled manager and implementing an effective risk management system, where all the different types of project risks are incorporated is essential. Regarding the optimism bias research, it was shown that optimism bias influenced the decision-making process in the project. It was hypothesized that it resulted from the ‘Accumulation of Planning Fallacy’ that was happening during the iterations of the planning process. The proposed solution for overcoming optimism bias was the usage of the ‘outside view’, when forecasting the costs, durations, and benefits of the projects. ‘Outside view’ would bypass optimism bias and produce more accurate predictions.

spiking neural networks
Hardware Security
AES
Artifical neural networks

http://resolver.tudelft.nl/uuid:f7518f7e-028c-4e65-b4a1-bdf824385b06

Student theses

master thesis

© 2021 Hanyu Ma