  Payload attribution systems (PAS) are one of the most important tools of
network forensics for detecting an offender after the occurrence of a
cybercrime. A PAS stores the network traffic history in order to detect the
source and destination pair of a certain data stream in case a malicious
activity occurs on the network. The huge volume of information that is daily
transferred in the network means that the data stored by a PAS must be as
compact and concise as possible. Moreover, the investigation of this large
volume of data for a malicious data stream must be handled within a reasonable
time. For this purpose, several techniques based on storing a digest of traffic
using Bloom filters have been proposed in the literature. The false positive
rate of existing techniques for detecting cybercriminals is unacceptably high,
i.e., many source and destination pairs are falsely determined as malicious,
making it difficult to detect the true criminal. In order to ameliorate this
problem, we have proposed a solution based on compressed bitmap index tables
and traffic downsampling. Our analytical evaluation and experimental results
show that the proposed method significantly reduces the false positive rate.
