Features Selection Using a Metaheuristic Approach For Anomaly Detection in Computer Networks.

• Grant number: 16/03088-4
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: July 01, 2016
• End date: December 31, 2016
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Kelton Augusto Pontara da Costa
• Grantee: Bruna de Camargo Rubio
• Host Institution: Faculdade de Ciências (FC). Universidade Estadual Paulista (UNESP). Campus de Bauru. Bauru , SP, Brazil

Abstract

Currently a number of people interested in obtaining unauthorized access to information contained in computer networks is growing exponentially along with all kinds of intrusions, attacks and malicious code. One of the areas that has been most studied in the context of anomaly detection is the invasion of computer networks, mainly due to the variety of attacks, as well as yours complexities. As a result, companies have increased their investment in research for the development of more effective intrusion detection systems, using such artificial intelligence techniques. The proposed objective scientific research to create a robust database of anomalies in computer networks and the application of a technique for selection of meta-heuristic-based features in order to maximize the hit rate of the classifiers to be applied for this database. The new database is intended to serve as an aid to researchers at security area in computer networks that require large databases to improve the results of their experiments.