  Online social networks (OSNs) are increasingly threatened by social bots
which are software-controlled OSN accounts that mimic human users with
malicious intentions. A social botnet refers to a group of social bots under
the control of a single botmaster, which collaborate to conduct malicious
behavior while mimicking the interactions among normal OSN users to reduce
their individual risk of being detected. We demonstrate the effectiveness and
advantages of exploiting a social botnet for spam distribution and
digital-influence manipulation through real experiments on Twitter and also
trace-driven simulations. We also propose the corresponding countermeasures and
evaluate their effectiveness. Our results can help understand the potentially
detrimental effects of social botnets and help OSNs improve their bot(net)
detection systems.
