The article is devoted to the problem of data protection from interception as a result of the "man in the middle" attacks. The proposed technique for detecting these attacks is based on the analysis of the headers of transit packets passing through the default gateway. Based on the data obtained, a table of correspondence between IP and MAC addresses is constructed, for which software provides up-to-date and reliable information. The addresses of packets passing through the gateway are compared with the records in this table and, in case of a mismatch and impossibility of confirming the correctness of addresses in the headers of the channel and network layers, it is concluded that there is an additional intermediate node in the network that appeared as a result of the default gateway substitution. The article presents approaches to software implementation of this technique, describes the packet analysis algorithm.

Keywords: local area network, man-in-the-middle, DHCP-spoofing, ARP-poisoning, traffic analysis, gateway, network address, packet, ARP-table

It describes a method of train set forming for using artificial neural networks to search inauthentic rows  in databases tables. An existing methods of the reliability ensure involve the use of integrity constraints and provides a truthfulness, but there is still a possibility of entering of inauthentic data, appropriate to all constraints. A more accurate assessment of reliability is possible with the use of artificial neural networks that require a training set. The main requirement for the training set - representation includes sufficiency, diversity and evenness. The approaches to each of these requirements are describes. Also calculations a sufficient number of rows for training neural networks of various types is given, as well as the results of experiments that confirm correctness of the theoretical calculations.

Keywords: database, authenticity, artificial neural networks, training set, representation