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Security Module and Hardware Key Generation | ==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. | 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. | ||
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<ref>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.</ref>. 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<ref>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. </ref>. | 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<ref>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.</ref>. 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<ref>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. </ref>. | ||
==References== | |||
Revision as of 19:10, 14 November 2022
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.
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].
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.