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DoS attack detection using a two dimensional wavelet transform

The analysis of network traffic is a key area for the management of fault-tolerant systems, since anomalies in network traffic can affect the availability and quality of service (QoS). This work proposes an intrusion detection tool based on the two-dimensional wavelet transform to quickly and effectively detect anomalies in computer networks generated by denial of service (DoS). Experiments were performed using two databases: a synthetic (DARPA) and another one from data collected at the Federal University of Santa Maria (UFSM), allowing analysis of the intrusion detection tool under different scenarios. The wavelets considered for the tests were all from the orthonormal family of Daubechies: Haar (Db1), Db2, Db4 and Db8 (with 1, 2, 4 and 8 null vanishing moments respectively). For the DARPA database we obtained a detection rate up to 100% and 95% for the UFSM database.