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Probing attacks against integrated circuits (IC) have become a serious concern, especially for security-critical applications. With the help of modern circuit editing tools, an attacker could remove layers of materials and expose wires carrying sensitive on-chip assets, such as cryptographic keys and proprietary firmware for probing. Most existing protection methods use active shield which provides tamper-evident covers at the top-most metal layers to the circuity below. However, they lack formal proofs of their effectiveness as some active shields have already been circumvented by hackers. In this paper, we investigate the problem of protection against front-side probing attacks and present a framework to assess a design’s vulnerabilities against probing attacks. Metrics are developed to evaluate the resilience of designs to bypass attack and reroute attack which are two common techniques used to compromise an anti-probing mechanism. Exemplary assets from an SoC layout are used to evaluate the proposed flow. Results show that long net and high layer wires are vulnerable to probing attack equipped with high aspect ratio FIB. Meanwhile, nets that occupy small area on the chip are probably compromised through rerouting shield wires. On the other hand, multi-layer internal orthogonal shield performs the best among common shield structures.
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Smart Inverter Grid Probing for Learning Loads: Part II - Probing Injection Design
- Award ID(s):
- 1751085
- PAR ID:
- 10090067
- Date Published:
- Journal Name:
- IEEE Transactions on Power Systems
- ISSN:
- 0885-8950
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Braverman, Mark (Ed.)We develop approximation algorithms for set-selection problems with deterministic constraints, but random objective values, i.e., stochastic probing problems. When the goal is to maximize the objective, approximation algorithms for probing problems are well-studied. On the other hand, few techniques are known for minimizing the objective, especially in the adaptive setting, where information about the random objective is revealed during the set-selection process and allowed to influence it. For minimization problems in particular, incorporating adaptivity can have a considerable effect on performance. In this work, we seek approximation algorithms that compare well to the optimal adaptive policy. We develop new techniques for adaptive minimization, applying them to a few problems of interest. The core technique we develop here is an approximate reduction from an adaptive expectation minimization problem to a set of adaptive probability minimization problems which we call threshold problems. By providing near-optimal solutions to these threshold problems, we obtain bicriteria adaptive policies. We apply this method to obtain an adaptive approximation algorithm for the Min-Element problem, where the goal is to adaptively pick random variables to minimize the expected minimum value seen among them, subject to a knapsack constraint. This partially resolves an open problem raised in [Goel et al., 2010]. We further consider three extensions on the Min-Element problem, where our objective is the sum of the smallest k element-weights, or the weight of the min-weight basis of a given matroid, or where the constraint is not given by a knapsack but by a matroid constraint. For all three of the variations we explore, we develop adaptive approximation algorithms for their corresponding threshold problems, and prove their near-optimality via coupling arguments.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TPWRS.2019.2906306
