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Adaptive Reconnaissance Attacks with Near-Optimal Parallel Batching
Xiang Li, Johnathan Smith and My Thai
University of Florida, University of Florida, University of Florida

In assessing privacy on online social networks, it is important to investigate their vulnerability to reconnaissance strategies, in which attackers lure targets into being their friends by exploiting the social graph in order to extract victims sensitive information. As the network topology is only partially revealed after each successful friend request, attackers need to employ an adaptive strategy. Existing work only considered a simple strategy in which attackers sequentially acquire one friend at a time, which causes tremendous delay in waiting for responses before sending the next request, and which lack the ability to retry failed requests after the network has changed. In contrast, we investigate an adaptive and parallel strategy, of which attackers can simultaneously send multiple friend requests in batch and recover from failed requests by retrying after topology changes, thereby significantly reducing the time of reaching the targets and improving robustness. We cast this approach as an optimization problem, Max-Crawling, and show it inapproximable within (1 _ 1/e + ). We first design our core algorithm PM-AReST which has an approximation ratio of (1 _ e (1/e_1)) using adaptive monotonic submodular properties. We next provide a near-optimal solution ((1 _ 1/e)) via a twostage stochastic programming approach. We further establish the gap bound of (1 _ e _(1_1/e) 2 ) between batch strategies versus the optimal sequential one. We experimentally validate our theoretical results, finding that our algorithm performs nearoptimally in practice and that this is robust under a variety of problem settings.