  One of the most important practices of cybercrime investigations is to search
a network traffic history for an excerpt of traffic, such as the disclosed
information of an organization or a worm signature. In post-mortem
investigations, criminals and targets are detected by attributing the excerpt
to payloads of traffic flows. Since it is impossible to store the high volume
of data related to long-term traffic history, payload attribution systems (PAS)
based on storing a compact digest of traffic using Bloom filters have been
presented in the literature. In these systems, querying the stored digest for
an excerpt results in a low number of suspects instead of certain criminals. In
this paper, we present a different PAS which is based on simple and widespread
digital signal processing techniques. Our traffic digesting scheme has been
inspired by DSP-based compression algorithms. The proposed payload attribution
system, named DSPAS, not only results in a low false positive rate but also
outperforms previous schemes in response to wildcard queries which are
essentially applicable for complex excerpts such as the signature of
polymorphic worms. Our theoretical analysis and practical evaluations show that
DSPAS results in a significantly lower false positive rate and also a lower
processing time for wildcard queries in comparison to previous works. Moreover,
our PAS can prevent a malicious insider from evading the PAS by its ability to
find strings similar to a queried excerpt.
