Algorithms (X, sigma, eta): Quasi-random Mutations for Evolution Strategies

Physical Unclonable Functions (PUFs) are known for their unclonability and light-weight design. However, several known issues with state-of-the-art PUF designs exist including vulnerability against machine learning attacks, low output randomness, and low reliability. To address these problems, we present a reconfigurable interconnected PUF network (IPN) design that significantly strengthens the security and unclonability of strong PUFs. While the IPN structure itself significantly increases the system complexity and nonlinearity, the reconfiguration mechanism remaps the input–output mapping before an attacker could collect sufficient challenge-response pairs (CRPs). We also propose using an evolution strategies (ES) algorithm to efficiently search for a network configuration that is capable of producing random and stable responses. The experimental results show that applying state-of-the-art machine learning attacks result in less than 53.19% accuracy for single-bit output prediction on a reconfigurable IPN with random configurations. We also show that, when applying configurations explored by our proposed ES method instead of random configurations, the output randomness is significantly improved by 220.8% and output stability by at least 22.62% in different variations of IPN.

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Network evolution strategies: Network and equipment provider's perspectives

IEEE Communications Magazine ◽

10.1109/mcom.1987.1093549 ◽

1987 ◽

Vol 25 (3) ◽

pp. 14-18 ◽

Cited By ~ 1

Author(s):

I. Ebert

Keyword(s):

Network Evolution ◽

Evolution Strategies

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Evolution Strategies with Adaptively Rescaled Mutation Vectors

2005 IEEE Congress on Evolutionary Computation ◽

10.1109/cec.2005.1555019 ◽

2005 ◽

Cited By ~ 3

Author(s):

D.V. Arnold

Keyword(s):

Evolution Strategies

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Economic Load Dispatch Using Bacterial Foraging Optimization Algorithm Based on Evolution Strategies

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.860-863.2040 ◽

2013 ◽

Vol 860-863 ◽

pp. 2040-2045 ◽

Cited By ~ 1

Author(s):

Xiao Hua Feng ◽

Yu Yao He ◽

Juan Yu

Keyword(s):

Search Space ◽

Complex Problem ◽

Evolution Strategies ◽

Power Losses ◽

Bacterial Foraging Optimization ◽

Thermal Generation ◽

Bacterial Foraging ◽

Global And Local ◽

Generation Test ◽

Bacterial Foraging Optimization Algorithm

This paper presents a novel modiﬁed bacterial foraging optimization(BFO) to solve economic loaddispatch (ELD) problems. BFO isalready successfully employed to solve variousoptimization problems. However original BFOfor small problems with moderate dimensionand searching space is satisfactory. As searchspace and complexity growexponentially in scalable ELD problems, it shows poorconvergence properties. To tackle this complex problem considering itshigh-dimensioned search space, the Evolution Strategies is introduced to thebasic BFO. The chemotactic step is adjusted to have a dynamic non-linearbehavior in order to improve balancing the global and local search. Theproposed algorithm is validated using several thermal generation test systems.The results are compared with those obtained by other algorithms previouslyapplied to solve the problem considering valve-point effects and power losses.

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