Improving Performance and Convergence Rates in Multi-Layer Feed Forward Neural Network Intrusion Detection Systems

Abstract

Today's anomaly-based network intrusion detection systems (IDSs) are plagued with detecting new and unknown attacks. The review of the literature builds ideas for researching the problem of detecting these attacks using multi-layered feed forward neural network (MLFFNN) IDSs. The scope of the paper focused on a review of the literature from primarily 2008 to the present found in peer-review and scholarly journals. A key word search was used to compare and contrast the literature to find strengths, weaknesses and gaps. The significance of the research found that further work is needed to improve the performance and convergence rates of MLFFNN IDSs. This literature review contributes to the area of intrusion detection by looking at the effects of architecture, algorithms, and input data on the performance and convergence rates of MLFFNN IDSs.

Incorporating a Honeyfarm With MLFFNN Ids for Improving Intrusion Detection

“…There is no standard method for collecting real traffic for training and testing a MLFFNN IDS [7]. Reference [8] found that a dataset composed of simulated and real traffic is needed. A solution is to use honeypots to collect real traffic.…”