The Sybil Attack

By John R. Douceur. 2002

5,583 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Privacy & Security, Signal Processing

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Citations in the corpus

You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion (2020, background): “Any GitHub user can star any repo, making stars vulnerable to Sybil attacks [19] that use multiple “sock-puppet” accounts to manipulate ratings.”
Advances and Open Problems in Federated Learning (2019, background): “An actively malicious adversary controlling the server could simulate a large number of fake client devices (a “Sybil attack” [140]) or could preferentially select previously compromised devices from the pool of available devices.”
Local Model Poisoning Attacks to Byzantine-Robust Federated Learning (2019, background): “Specifically, like Sybil attacks [17] to distributed systems, the attacker could inject c fake worker devices into the federated learning system or compromise c benign worker devices.”
Mitigating Sybils in Federated Learning Poisoning (2018, background): “Sybils [14] are a common strategy for adversaries to increase the effectiveness of attacks on crowd-sourced systems.”
Prochlo: Strong Privacy for Analytics in the Crowd (2017, background): “, where users’ data is combined into a “crowd” comprising only Sybil data [24].”
Prio: Private, Robust, and Scalable Computation of Aggregate Statistics (2017, background): “Standard defenses [3, 114, 124, 125] against Sybil attacks [52] would apply here.”
Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning (2018, methodology): “oisoning attack on the shared model or an information leakage attack against a target peer victim. In doing so, we assume that the adversary may control multiple peers in the system in a sybil attack [18]. We do not restrict adversaries at the API level: they can provide arbitrary values when comput”
Proactively Accountable Anonymous Messaging in Verdict (2012, background): “Sybil Attacks If it is too easy to join a group, dishonest participants might flood the group with Sybil identities [19], giving an anonymous slot owner the impression that she has more anonymity than she actually does.”
Wireless Sensor Network Security: A Survey (2006, background): “described as an attack able to defeat the redundancy mechanisms of distributed data storage systems in peerto-peer networks [15].”
V2X Access Technologies: Regulation, Research, and Remaining Challenges (2018, background): “Attacks on availability, like the jamming, black hole (see Figure 5, and Sybil [117] attacks, involve interfering with the transmission and routing of packets, such that the VANET cannot function within operational constraints, degrading service and endangering lives.”
Security of the Internet of Things: Vulnerabilities, Attacks, and Countermeasures (2019, background): “Apart from these services it may also affect the performance of other schemes such as misbehavior detection, voting-based algorithms, data aggregation, fusion and distributed storage [82].”
Distributed detection of node replication attacks in sensor networks (2005, background): “Even more insidiously, node replicas placed at judiciously chosen locations can revoke legitimate nodes and disconnect the network by triggering correct execution of node-revocation protocols that rely on threshold voting schemes [6, 10, 13, 27].”
Adversarial Web Search (2011, background): “In a Sibyl4 attack, there is a single attacker (an individual or a company) that creates multiple identities, in this case multiple Web pages or sites, not easily identifiable as belonging to the same individual [64].”
Spotting fake reviewer groups in consumer reviews (2012, background): “Sybil Attacks [7] in security create pseudo identities to subvert a reputation system.”
Designing secure sensor networks (2004, background): “The Sybil attack is where a malicious node illegitimately claims multiple identities [10, 11].”
Secure Remote Execution of Sequential Computations (2009, background): “However, participants might be able to operate under several identities (Sybil attack [21]) or to re-enter the system with a new identity (i.”
Biscotti: A Blockchain System for Private and Secure Federated Learning (2021, background): “In doing so, we assume that the adversary may control multiple peers in a sybil attack [45] but does not control more than 30 percent of the total stake.”
Decentralized Applications: The Blockchain-Empowered Software System (2018, background): “Nevertheless, intruders may perform Sybil attack [8] to control a substantial fraction of the public P2P system by representing multiple identities, which may lead to a critical ‘‘Double Spending’’ issue in the blockchain-empowered decentralized ledger.”
Enhancing Bitcoin Security and Performance with Strong Consistency via Collective Signing (2016, background): “ByzCoin addresses this conﬂict by forming consensus groups dynamically from windows of recently mined blocks, giving recent miners shares or voting power proportional to their recent commitment of hash power.”