Malicious Node Identification in Coded Distributed Storage Systems under Pollution Attacks

In coding-based distributed storage systems (DSSs), a set of nodes (SNs) hold coded fragments data unit that collectively allow one to recover the original information. It is well known modification (a.k.a. pollution attack) Achilles’ heel such coding systems; indeed, intentional single fragment has potential prevent reconstruction information because error propagation induced by decoding algorithm. The challenge we take in this work devise an algorithm identify polluted within encoding and characterize its performance. To end, provide following contributions: (i) We MIND (Malicious node IdeNtification DSS), general with respect mechanism chosen for DSS, it able cope heterogeneous allocation SNs, effective successfully identifying low-redundancy scenario; (ii) formally prove both termination correctness; (iii) derive accurate analytical characterization performance (hit probability complexity); (iv) develop C++ prototype implements validate predictions model. Finally, show applicability our work, define robustness metrics SNs apply results select allocations yielding collusion as highest actual attackers.