Android Open Source

  Java NPRNG Example Neural Pseudo-Random Number Generator (Android Java) App.java https://drive.google.com/file/d/1tCeqStW7ypx5B0wesBYwaVn2y1IQ07NK/view?usp=sharing MainActivity.java https://drive.google.com/file/d/1WDq33mgUecTM5V0GXcO5NSFoQqtXTuXo/view?usp=sharing The results of the work are output to Log - a pseudo-random 280-bit number (280x8 bit) The initial parameters (master key) are set in MainActivity.java App.java - computing class This is a simple working example. Generates a pseudo-random number of large dimension (any dimension). This class can be used to build huge NPRNG generators. Java NPRNG Example

Neural Network in PRNG The greatest share of PRNG vulnerability is created by constant parameters: constant (even if considered crypto-resistant) generation algorithm, constant length of generated numbers, constant (on some interval of generated numbers) master key. Using neural networks with feedback in PRNG leads to inflation of crypto-resistance. With the help of neural networks it is easy to make ALL generator parameters variable. At every step. The complication of cryptanalysis is especially effective with preliminary distortion of encrypted data. For example, when using several layers of encryption. Of course, we are not talking about stream encryption. Let's write an example of a neural network in Java from one neuron, emulating a network of 6 neurons. Then we'll teach it to rebuild the structure and change parameters. But for now we won't study learning. Android JAVA

  Full Source Project The JAVA project Photo Web Cameras with FTP client has been modernized. The project is being prepared for publication in full, ready for compilation and installation. Android Java Project

  Bloom-Blum-Shub The Bloom-Blum-Shub algorithm is very interesting and, if certain conditions are met, allows you to generate crypto-resistant pseudo-random numbers of large size. But if you generate numbers of 1024 bits or 2048 bits, then all the prime numbers that you can use in this algorithm have long been known. The required pairs of prime numbers for the algorithm are even smaller. And there is a risk of successful cryptanalysis of your protected data. It makes sense to use multiple layers of data encryption so that decoded at one layer does not give a meaningful view of the data. And at each level, use algorithms to improve data quality. This is of little use for stream encryption. But for storing personal data, master keys, passwords, PIN codes, tokens it can be very, very successful. And at the same time synchronicity is maintained. Those. multiple generators will create the same keys (numbers) on any number of generator instances. The downside is that generator instances nee

  Scope of planning When you query a search engine for pictures on a specific topic (for example, an airplane with stirrup engines) and select one of the ones found, you are training a Neural Network. Millions of users, requesting a search engine and making a choice, train it even more extensively. But your queries and choices can be recorded and used to train future versions of Neural Networks (and do it faster). Therefore, the emergence of Large Distributed Fake Systems is inevitable, which will introduce incorrect training data into Neural Networks (or Artificial Intelligence). And the scale of systems generating queries and making choices must be enormous to have an advantage or maintain a leading position in the development of Neural Networks. New reality

  Opinion Naval unmanned attack boats have demonstrated their effectiveness. The issue of protecting ships from them is very important. This is probably important for existing ships. One option is narrow pontoons along each side. With a streamlined shape, they will slow down the ship less, but will significantly reduce roll and prevent an unmanned boat from hitting the side of the ship. This is some kind of trimaran. Moreover, it is possible to provide for the release of pontoons to achieve maximum speed. The bow and stern of the ship will remain uncovered. But these are narrow sectors that can be protected with rapid-fire systems. Ships systems