Hiding in Temporal Networks
- Computer ScienceIEEE Transactions on Network Science and Engineering
It is found that it is usually computationally infeasible to find the optimal way of hiding in temporal networks of edges changing in time, but by manipulating one's contacts, one could add a surprising amount of privacy.
How Members of Covert Networks Conceal the Identities of Their Leaders
- Computer ScienceACM Trans. Intell. Syst. Technol.
This work analyzes the problem of choosing a set of edges to be added to a network to decrease the leaders’ ranking according to three fundamental centrality measures, namely, degree, closeness, and betweenness and proves that this problem is NP-complete for each measure.
SHOWING 1-10 OF 89 REFERENCES
Source detection of rumor in social network - A review
- Computer ScienceOnline Soc. Networks Media
An analysis of social network-based Sybil defenses
- Computer ScienceSIGCOMM '10
It is demonstrated that networks with well-defined community structure are inherently more vulnerable to Sybil attacks, and that, in such networks, Sybils can carefully target their links in order to make their attacks more effective.
How to Hide One’s Relationships from Link Prediction Algorithms
- Computer ScienceScientific Reports
It is proved that the optimization problem faced by such an individual is NP-complete, meaning that any attempt to identify an optimal way to hide one’s relationships is futile, and effective heuristics that are readily-applicable by users of existing social media platforms to conceal any connections they deem sensitive are developed.
Fast rumor source identification via random walks
- Computer ScienceSocial Network Analysis and Mining
This work proposes a heuristic based on the hitting time statistics of a surrogate random walk process that can be used to approximate the maximum likelihood estimator of the rumor source.
Aiding the Detection of Fake Accounts in Large Scale Social Online Services
- Computer ScienceNSDI
A new tool in the hands of OSN operators, which relies on social graph properties to rank users according to their perceived likelihood of being fake (SybilRank), which is computationally efficient and can scale to graphs with hundreds of millions of nodes, as demonstrated by the Hadoop prototype.
Rumors in a Network: Who's the Culprit?
- Computer ScienceIEEE Transactions on Information Theory
Simulations show that rumor centrality outperforms distance centrality in finding rumor sources in networks which are not tree-like, and it is proved that on trees, the rumor center and distance center are equivalent, but on general networks, they may differ.
Uncovering Large Groups of Active Malicious Accounts in Online Social Networks
- Computer ScienceCCS
This work designs and implements a malicious account detection system called SynchroTrap that clusters user accounts according to the similarity of their actions and uncovers large groups of malicious accounts that act similarly at around the same time for a sustained period of time.
SybilGuard: Defending Against Sybil Attacks via Social Networks
- Computer ScienceIEEE/ACM Transactions on Networking
This paper presents SybilGuard, a novel protocol for limiting the corruptive influences of sybil attacks, based on the ldquosocial networkrdquo among user identities, where an edge between two identities indicates a human-established trust relationship.
The spread of low-credibility content by social bots
- Computer ScienceNature Communications
It is found that bots play a major role in the spread of low-credibility content on Twitter, and control measures for limiting thespread of misinformation are suggested.
You Are How You Click: Clickstream Analysis for Sybil Detection
- Computer ScienceUSENIX Security Symposium
A detection approach that groups "similar" user clickstreams into behavioral clusters, by partitioning a similarity graph that captures distances between clickstream sequences, and shows that it provides very high detection accuracy on clickstream traces.