iPersea : The Improved Persea with Sybil Detection Mechanism

TL;DR

This paper enhances the Persea P2P system by introducing a Sybil detection mechanism that effectively identifies malicious nodes, ensuring reliable lookups even under high attack conditions in social network-based DHTs.

Contribution

It proposes a novel Sybil detection scheme built on Persea that filters malicious nodes, preventing incorrect responses and improving system robustness against Sybil attacks.

Findings

Achieves 100% lookup success rate in large social network simulations.

Effectively detects and filters Sybil nodes during lookups.

Maintains reliable system performance even with high attack edge ratios.

Abstract

P2P systems are highly susceptible to Sybil attacks, in which an attacker creates a large number of identities and uses them to control a substantial fraction of the system. Persea is the most recent approach towards designing a social network based Sybil-resistant DHT. Unlike prior Sybil-resistant P2P systems based on social networks, Persea does not rely on two key assumptions: (i) that the social network is fast mixing, and (ii) that there is a small ratio of attack edges to honest peers. Both assumptions have been shown to be unreliable in real social networks. The hierarchical distribution of node IDs in Persea confines a large attacker botnet to a considerably smaller region of the ID space than in a normal P2P system and its replication mechanism lets a peer to retrieve the desired results even if a given region is occupied by attackers. However, Persea system suffers from certain limitations, since it cannot handle the scenario, where the malicious target returns an incorrect result instead of just ignoring the lookup request. In this paper, we address this major limitation of Persea through a Sybil detection mechanism built on top of Persea system, which accommodates inspection lookup, a specially designed lookup scheme to detect the Sybil nodes based on their responses to the lookup query. We design a scheme to filter those detected Sybils to ensure the participation of honest nodes on the lookup path during regular DHT lookup. Since the malicious nodes are opt-out from the lookup path in our system, they cannot return any incorrect result during regular lookup. We evaluate our system in simulations with social network datasets and the results show that catster, the largest network in our simulation with 149700 nodes and 5449275 edges, gains 100% lookup success rate, even when the number of attack edges is equal to the number of benign peers in the network.