2results about How to "Increase time complexity" patented technology

The application discloses a modulus decomposition method of an RSA variant cryptosystem, and classifies and discusses a public-private key pair of the RSA variant cryptosystem. 2 <ed<N 3 When the public-private key pair satisfies N 3 <ed<N 4 , the modulus N of the RSA is decomposed by using an enumeration method to continuously test a candidate value, finding a suitable k value to calculate a key equation, and by using a lattice theory analysis skill, an LLL algorithm to find a short vector in a lattice, a variant of a Coppersmith method and a direct application of an approximate integer common factor of a Howgrave-Graham algorithm to transform a modulus equation solving problem into a short vector solving problem in a lattice, and the modulus N of the RSA is decomposed. 12 The time complexity of the method for decomposing a polynomial is O(2log N). The application enriches the application of lattice cryptography on the RSA algorithm and improves the security of the variant RSA algorithm.

The invention belongs to the technical field of black-box confrontation attacks, discloses a black-box confrontation attack method based on proxy function optimization and feature probability diffusion, and aims to solve the problems that traditional evading attacks cannot disturb a network intrusion detection system based on machine learning and the number of black-box confrontation sample attack query times is large. In order to solve the problems of large calculation overhead, universality of adversarial samples and the like, a method for generating the adversarial samples by combining the adversarial samples, reinforcement learning and a diffusion model is designed, an indirect optimization framework is realized, and a trainable neural network is introduced for generating adversarial disturbance. Through back propagation of the proxy loss function J, parameters of the neural network can be indirectly updated. According to the mechanism, an unguidable black box optimization problem is ingeniously converted into a trainable and end-to-end neural network optimization problem, so that indirect optimization against disturbance is realized.