  We present an algorithm for the identification of transient noise artifacts
(glitches) in cross-correlation searches for long O(10s) gravitational-wave
transients. The algorithm utilizes the auto-power in each detector as a
discriminator between well-behaved Gaussian noise (possibly including a
gravitational-wave signal) and glitches. We test the algorithm with both Monte
Carlo noise and time-shifted data from the LIGO S5 science run and find that it
is effective at removing a significant fraction of glitches while keeping the
vast majority (99.6%) of the data. Using an accretion disk instability signal
model, we estimate that the algorithm is accidentally triggered at a rate of
less than 10^-5% by realistic signals, and less than 3% even for exceptionally
loud signals. We conclude that the algorithm is a safe and effective method for
cleaning the cross-correlation data used in searches for long
gravitational-wave transients.
