Detecting DDoS attacks at the source using multiscaling analysis

The proliferation of Distributed Denial of Service (DDoS) attacks is a constant threat to business and individuals. Existing systems proved to be inefficient when deploying counter-measures at the target of the attacks. In fact, efficient counteractions should be applied at the networks that contain the sources of the attack. However, the detection of such type of attacks at the source is extremely difficult. In this work, we propose a novel and more efficient methodology to detect DDoS attacks at the source that relies on the inherent periodicity of the traffic generated by DDoS attack sources. Detecting and quantifying the traffic periodic components using multiscaling traffic analysis based on wavelet scalograms allows an efficient detection of DDoS attacks at the source, even when the attacks are performed using encrypted channels or are embedded within licit traffic.