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Attacks and Security Mechanisms
Published in Yan Zhang, Jun Zheng, Honglin Hu, Security in Wireless Mesh Networks, 2008
Anjum Naveed, Salil S. Kanhere, Sanjay K. Jha
A Sybil attack is the form of attack where a malicious node creates multiple identities in the network, each appearing as a legitimate node [20]. A Sybil attack was first exposed in distributed computing applications where the redundancy in the system was exploited by creating multiple identities and controlling the considerable system resources. In the networking scenario, a number of services like packet forwarding, routing, and collaborative security mechanisms can be disrupted by the adversary using a Sybil attack. Following form of the attack affects the network layer of WMNs, which are supposed to take advantage of the path diversity in the network to increase the available bandwidth and reliability. If the malicious node creates multiple identities in the network, the legitimate nodes, assuming these identities to be distinct network nodes, will add these identities in the list of distinct paths available to a particular destination. When the packets are forwarded to these fake nodes, the malicious node that created the identities processes these packets. Consequently, all the distinct routing paths will pass through the malicious node. The malicious node may then launch any of the above-mentioned attacks. Even if no other attack is launched, the advantage of path diversity is diminished, resulting in degraded performance.
Survey of Sybil Attacks in Networks
Published in Mohammad Ilyas, Sami S. Alwakeel, Mohammed M. Alwakeel, el-Hadi M. Aggoune, Sensor Networks for Sustainable Development, 2017
Peer-to-peer systems play an ever-increasingly important part of our daily lives. However, most of the peer-to-peer systems are vulnerable to Sybil attacks. In order to design more efficient and practical Sybil defenses, we write this survey. This article is the first survey focusing on the developments of Sybil defenses. We first give the definition of Sybil attacks and provide the classification of Sybil attacks. Then, we give several realistic systems that are vulnerable to Sybil attacks. After that, defense mechanisms and their corresponding strengths and weaknesses were discussed. Unlike other surveys, we describe these mechanisms according to anti-Sybil approaches’ developing stages. By the end of this survey, we provide some directions for future research.
Security, Privacy, and Trust for User-Generated Content
Published in Kuan-Ching Li, Hai Jiang, Albert Y. Zomaya, Big Data Management and Processing, 2017
Extensive studies have been conducted to detect and prevent Sybil attacks. For example, in [44], users and their trust relationships are modeled as a social graph, where vertexes represent users and links represent user relationship. The defense scheme, SybilGuard, is proposed to differentiate Sybil accounts from normal ones by identifying the disproportionately small “cut” in the social graph based on the assumption that malicious users can create many identities but few trust relationships. In their later study in [45], an improved protocol named SybilLimit has been proposed to optimize the approach by further minimizing the number of Sybil account in million-node synthetic social network. A Bayesian-based inference approach, SybilInfer, has been proposed in [46] that consists of a probabilistic model of honest social networks as well as an inference engine. Buchegger and Boudec [47] have proposed a solution to detect and eliminate Sybil accounts by periodically re-evaluating the behavior of accounts and discounting historical ratings. In [48], the authors have examined the graph of distributed hash tables (DHTs) (a graph showing a node is introduced by which node) to identify Sybil accounts, based on the assumption that a large number of malicious accounts are introduced by a small number of Sybil entities. In addition, Sybil attacks can also be prevented by utilizing a centralized authority, which issues trusted certifications to ensure that only one identity would be assigned to a given entity [49]. Other solutions may include checking resource owned by identities, increasing cost to create Sybil identities, using trusted devices, etc. [49].
Towards a Conceptual Typology of Darknet Risks
Published in Journal of Computer Information Systems, 2023
Obi Ogbanufe, Jordan Wolfe, Fallon Baucum
Sybil attacks are when a person creates several fictional accounts and generates a positive reputation by performing transactions with those accounts.62 However, when dealing with real accounts, they receive their money but default on their obligation to send the goods or render service. The fake account earns a bad reputation, creating distrust in the marketplace. Slander attacks provide negative feedback to sellers even when they have not been in any transaction with the account giving the feedback. Another means is by calling attention to the presence of unsafe, low-quality drugs or unprotected hacking tools (e.g., with backdoors) in the marketplace, even when there is no evidence of those. These tactics create distrust and increase perceptions of risk for buyers and vendors, which may reduce their benefits. These findings demonstrate that gossip and slander are successful in disrupting darknet sites.
Distributed Ledger and Decentralised Technology Adoption for Smart Digital Transition in Collaborative Enterprise
Published in Enterprise Information Systems, 2023
A Sybil attack is a type of attack in which a malicious user or attacker disguises malfeasance or gains access by generating numerous false identities to increase control over the DLT network. This can be an intentional or wilful action with the intent to harm the DLT infrastructure (Rauchs et al. 2018). Hence, since identity is an exogenous property, the DLT platform by itself cannot avert such malicious attacks. It will depend on the involvement of an outside agent such as a Sybil-resistance mechanism or a credentialing authority or such as POW or PoS to mitigate these attacks. Another notable technique for preventing Sybil attacks is to incorporate the consensus algorithm for public or permissionless DLT platforms (Ølnes, Ubacht, and Janssen 2017). PoW and PoS makeit computationally challenging resulting to time-consuming and costly to generate new records (Rauchs et al. 2018). Although permissioned or public DLT platforms do not require this prevention as these systems carefully vet all entities before granting them permission to join the node network and generate records.