The security of the proposed hash function improved after KSCNN. It proposed a new structure named Keyed Sponge Chaotic Neural Network (KSCNN hash function) based on chaotic maps, NN and sponge construction. Similar uses of chaotic maps were apparent in. This paper used several testing sets to show the validity of the presented proposal. ANN could be used to generate a one way hash function. A similar idea was presented in, which focused on a theoretical analysis of the possibility of using ANNs and chaotic maps for hashing. Effective hash functions are the cornerstone of security in today’s communication networks. ![]() Furthermore, this proposed algorithm can be used in cryptography, which includes the following two major operations: generation of NN parameters using fast and efficient Chaotic Generator and the iteration of the message through the Chaotic NN. Furthermore, the implemented Hash function demonstrated good statistical properties, strong Collision Resistance and High Message Sensitivity. The proposed function encodes the plaintext of arbitrary length into the fixed length, improving security against statistical attacks, birthday attacks, and meet-in-the-middle attacks. The chaotic function and NNs were used in data encryption due to their cipher-suitable properties, and a hash function based on them was constructed which made use of their advantageous properties. Neural Networks can also be combined with the chaotic function to produce secure hashes. Hamming distance was used again for randomness measurements. The output response bit of PicoPUFs would be used as a challenging bit for APUF to increase security. It used a 1-bits MPUF design that contained multiple PicoPUFs with one APUF. The goal for the PUF is to have better resistance against ML attacks. Furthermore, the authors of proposed a new design for PUF that improved the existing weak PUF to a strong PUF. The result revealed its higher prediction error when using modern ML algorithms as opposed to regular PUF. Ring oscillators would be compared through frequencies to form final response bits. Responses generated from the APUF would select ring oscillators. An APUF combined with Ring Oscillators can provide randomness to prevent ML attacks. ![]() ![]() This provides more randomness for the PUF circuit. The result from this PUF would have Configurable Tristate PUF (CTPUF) ( 1616 ) 16 possible outcomes, which depend on delays and voltage. The output of the current mirror PUF would be placed into the APUF as an input. A current mirror PUF and an APUF were combined to form a PUF that was unpredictable for ML algorithms. In, multiple PUFs were used to form a more complex PUF with more robustness against ML attacks.
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