All your contacts are belong to us: automated identity theft attacks on social networks

By Leyla Bilge, Thorsten Strufe, Davide Balzarotti, Engin Kirda. The Web Conference, 2009

651 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/d473c9e8cbec56af335a0b3bcf84c45a87dda5b6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Neil Zhenqiang Gong
2. Binghui Wang
3. Jinyuan Jia
4. Le Zhang
5. Prateek Mittal
6. Giovanni Vigna
7. Ari Juels
8. Christopher Krügel

In Computer Networks and Communications

1. Neil Zhenqiang Gong
2. Binghui Wang
3. Le Zhang
4. Jinyuan Jia
5. Prateek Mittal
6. Peng Gao
7. Giovanni Vigna
8. Christopher Krügel

In Information Systems

1. Neil Zhenqiang Gong
2. Binghui Wang
3. Le Zhang
4. Jinyuan Jia
5. Prateek Mittal
6. Ari Juels
7. Giovanni Vigna
8. Peng Gao

Citations in the corpus

• Exploiting Temporal Dynamics in Sybil Defenses (2015, background): “Studies of Sybil attacks in online social networks have shown that although many users do accept friendship requests from strangers, a significant fraction of users do not [6].”
• Connecting users across social media sites: a behavioral-modeling approach (2013, background): “Deanonymization of social networks is tightly coupled with the research in privacy preserving data mining or Identity Theft attacks [3].”
• The socialbot network: when bots socialize for fame and money (2011, methodology): “[6] show that most users in OSNs are not cautious when accepting connection requests that are sent to them.”
• EVILCOHORT: Detecting Communities of Malicious Accounts on Online Services (2015, background): “A second advantage of using online services to spread malicious content is that while users have become aware of the threats associated with email, they are not as familiar with scams and spam that spreads through other communication channels (such as social networks) [5, 18, 27].”
• COMPA: Detecting Compromised Accounts on Social Networks (2013, background): “COMPA was able to identify compromised accounts on both social networks with high precision.”
• Sybil Attacks and Their Defenses in the Internet of Things (2014, background): “In addition, due to the observation that a portion of OSN relationships among users are untrusted [55], SybilDefender also includes a mechanism to limit the number of attack edges.”
• SoK: The Evolution of Sybil Defense via Social Networks (2013, methodology): “Social graphs do not seem to be fast mixing after all [17], [23], and the probability with which fake identities are accepted as friends is much higher than anticipated [6], [43], implying that the set of attack edges is not as sparse as assumed.”
• Honeywords: making password-cracking detectable (2013, background): “Given a user’s identity, there are then many ways to find demographic or biographical data about her online—by exploiting information published on social networks, for example [5].”
• Advanced social engineering attacks (2015, background): “The attackers first compromised the development site ”iPhoneDevSDK”, which is a popular forum for iOS application developers.”
• Safebook: A privacy-preserving online social network leveraging on real-life trust (2009, background): “has been shown that this combination may lead to a new kind of vulnerabilities5,6,[3].”
• An analysis of social network-based Sybil defenses (2010, background): “However, it remains an open question whether this is true in any online social network of today; it is clear that, at least in some social networks, the assumption does not hold [5].”
• Aiding the Detection of Fake Accounts in Large Scale Social Online Services (2012, background): “Although previous studies [17, 19] suggest that fake identities can befriend others, a recent study shows that most of their connections are established with other fakes [43].”
• Privacy wizards for social networking sites (2010, background): “Social networking sites may also enable new forms of classical attacks, including phishing [9] and spam [10].”
• SybilBelief: A Semi-Supervised Learning Approach for Structure-Based Sybil Detection (2013, background): “activities such as spreading spam or malware [3], stealing other users’ private information [4], [5], and manipulating web search results via “+1” or “like” clicks [6].”
• SybilFuse: Combining Local Attributes with Global Structure to Perform Robust Sybil Detection (2018, background): “Previous work [8], [21] also showed that Sybil nodes could befriend benign users on Facebook at a large scale.”