Abstract-Resiliency and security in control systems such as SCADA and Nuclear plant's in today's world of hackers and malware are a relevant concern. Com puter systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM -Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a com bination of two neural network learning algorithms -the Error-Back Propagation and LevenbergMarquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.
Authors of REF presented an IDS using Neural Network based Modelling (IDS-NNM) algorithm following the supervised learning approach.
Neural Network based Intrusion Detection System for critical infrastructures
2009 International Joint Conference on Neural Networks
2009 International Joint Conference on Neural Networks
Computer Science