Security module
Security Module and Hardware Key Generation
The pervasiveness and vast number of deployed nodes monitoring the environment makes security a fundamental challenge, especially in IoT applications. Security issues are expected to arise in terms of data authenticity, integrity, and confidentiality. Data as well as the sender of this data need to be verified and security must be assured down to the hardware level (i.e., each node needs to be confirmed to be). Security can be inserted in several levels. Chip authentication is necessary when nodes are added onto the network. Data sent between the nodes can also be secured via lightweight encryption. Finally commands for actuation need to be authenticated and verified. All these can be enhanced through machine learning.
Year 1 of this project focuses on the use of physically unclonable functions (PUFs) as unconditionally secure hardware key. This requires two things: (1) characterization of the technology and (2) a survey of existing topologies. As such, these two prerequisites are the target activities for the first quarter. The rest of the activities for the year are shown in the figure below.
Physically Unclonable Functions
In the recent past, Physically Unclonable Functions (PUFs) have emerged as potentially highly secure and lightweight solution to ensure data and hardware security, assuring trustworthiness down to the chip level[1]. A PUF is a function that maps an input (digital) challenge to an output (digital) response in a repeatable but unpredictable manner, leveraging on chip-specific random process variations. Machine learning was also proposed to improve authentication with PUF[2]. PUFs have 3 main properties: randomness, uniqueness and reliability[3]. Randomness refers to the bias of the generated key, or the ratio between logic 1 and logic 0 bits in the key (ideally, the bias would be 50%). Uniqueness is measured through the inter-PUF hamming distance fraction, or the percentage of PUF bits that are different between keys. Ideally, we would want a hamming distance fraction of 50%.
Curriculum and Manpower Development
As part of the objective of the CIDR program to continuously improve the curriculum and manpower, the topic on physically unclonable functions was incorporated in the EE 226 (Digital Integrated Circuits) as a possible area of research. The introduction lecture can now be found in youtube[4].
References
- ↑ R. Maes, V. Rozic, I. Verbauwhede, P. Koeberl, E. van der Sluis, V. can der Leest, "Experimental Evaluation of Physically Unclonable Functions in 65 nm CMOS", in European Solid State Circuit Conference (ESSCIRC), 2012, pp. 486489.
- ↑ B. Chatterjee, D. Das, S. Sen, "RF-PUF: IoT Security Enhancement through Authentication of Wireless Nodes using In-situ Machine Learning", IEEE International Symposium on Hardware Oriented Security and Trust (HOST), 2018, pp. 205-208.
- ↑ [1] A. Alvarez and M. Alioto, “Security down to the Hardware Level,” in Enabling the Internet of Things - from Circuits to Networks, Springer, 2017.
- ↑ https://youtu.be/JfNnSYuIOkc