An improved RBF neural network method for information security evaluation

Abstract

Itis well-known that information security means the protection of information,and ensuring the availability, confidentiality and integrity of information. The purposeof this paper is to present an improved RBF neural network method forinformation evaluation. Ant colony optimization is a multi-agent approach fordifficult combinatorial optimization problems, which has been applied tovarious NP hard problems. Here, ant colony optimization algorithm is applied tooptimize the parameters of RBF neural network. In this paper, we employ  “unauthorized access”, “unauthorized accessto a system resource”, “data leakage”, “denial of service”, “unauthorizedmodification data and software”, “system crash” as the features of informationsecurity evaluation. It is indicated that the information security evaluation error of the improvedRBF neural network is smaller than that of the RBF neural network. Thus, theimproved RBF neural network is very suitable for information security evaluation.

DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4806