| LDR | | 00000nmm u2200205 4500 |
| 001 | | 000000329864 |
| 005 | | 20241016155712 |
| 008 | | 181129s2018 ||| | | | eng d |
| 020 | |
▼a 9780438050280 |
| 035 | |
▼a (MiAaPQ)AAI10824059 |
| 035 | |
▼a (MiAaPQ)princeton:12613 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 049 | 1 |
▼f DP |
| 082 | 0 |
▼a 621.3 |
| 100 | 1 |
▼a Cakir, Burcin. |
| 245 | 10 |
▼a Addressing Integrated Circuit Integrity Using Statistical Analysis and Machine Learning Techniques. |
| 260 | |
▼a [S.l.] :
▼b Princeton University.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 114 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B. |
| 500 | |
▼a Adviser: Sharad Malik. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Princeton University, 2018. |
| 520 | |
▼a Outsourcing of design and manufacturing processes makes integrated circuits (ICs) vulnerable to adversarial changes and raises concerns about their security and integrity. The difference in the levels of abstraction between the initial specifica |
| 520 | |
▼a In this thesis, we present a novel approach for the analysis of circuits using graph algorithms and different concepts from linear algebra, signal processing and machine learning techniques to detect malicious insertions and reverse engineer a g |
| 520 | |
▼a All algorithms have been implemented and demonstrated to be scalable to significant sized ICs. They present valuable insights for reverse engineering digital ICs as well as for Trojan detection. |
| 590 | |
▼a School code: 0181. |
| 650 | 4 |
▼a Electrical engineering. |
| 690 | |
▼a 0544 |
| 710 | 20 |
▼a Princeton University.
▼b Electrical Engineering. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-10B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0181 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998623
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자
▼b 관리자 |