A Link-level Intrusion Detection Approach
Hongjie Sun, Binxing Fang, Hongli Zhang · 2005
Abstract:- The more and more complicated and advanced network attacks greatly thread the security of network. In order to detect and locate the source of anomaly and attack in the beginning of their spreading, we abstract the required link-level properties of network performance using end-to-end measurements and propose a new approach using maximum likelihood estimation and neural network for anomaly link detection and location. Maximum likelihood estimation is used to estimate the distribution of link character, a mixing and optimizing neural network solution combining the BackPropagation (BP) Algorithm with the Simulated Annealing (SA) Algorithm is used for link activity profile learning and anomaly link detection. Comparing with single BP algorithm, the value calculation result shows that BP-SA mixing and optimizing solution has a higher speed and higher accuracy. Experiment results indicate the new approach is effective and of a definite practicability. It is a bran-new idea and has a further develop potential for large scale network anomaly detection. Key-Words:- Intrusion detection, network security, backpropagation algorithm, simulated annealing algorithm,