Aplicacion de las Redes Neuronales al Reconocimiento de Sistemas Operativos
This work addresses a domain-specific classification problem in cybersecurity, but it is incremental as it applies established AI methods to a new application area.
The paper tackled the problem of remote operating system identification for information security by applying multi-layer perceptron neural networks, achieving better classification results than existing classic techniques.
In this work we present a family of neural networks, the multi-layer perceptron networks, and some of the algorithms used to train those networks (we hope that with enough details and precision as to satisfy a mathematical public). Then we study how to use those networks to solve a problem that arises from the field of information security: the remote identification of Operating Systems (part of the information gathering steps of the penetration testing methodology). This is the contribution of this work: it is an application of classic Artificial Intelligence techniques to a classification problem that gave better results than the classic techniques used to solve it.