Conclusion
In this paper, a new method identifying online port scan attacks in IP traffic is proposed. First, some relevant statistics are extracted from the data stream using the sliding HyperLogLog algorithm. A good choice of the parameters of the this algorithm has to be done in order to ensure an acceptable accuracy of the statistics. Second, a change point detection method based on the EWMA algorithm is used to identify suspicious traffic. It is mainly an adaption of the EWMA to the data stream context. For this purpose, a learning phase is added at the beginning of the algorithm in order to initialize its parameters. Then, a new constraint is added to the moving average EWMA(t) update to overcome the false-positive problem when this latter exceeds the UCL detection threshold. Finally, we run experiments on a real traffic trace captured in the IP backbone network of Orange Labs in December 2007 in the context of the ANR-RNRT OSCAR project. The obtained results confirm the efficiency of the adapted combination of the HyperLogLog and EWMA algorithms in terms of accuracy, memory usage, and time response.